Machine learning of plan robustness knowledge about
微生物功能差异 统计学方法
微生物功能差异统计学方法Microbial Functional Differences and Statistical MethodsMicroorganisms play a crucial role in various ecosystems, including the human microbiome, soil, and marine environments. These microscopic organisms exhibitremarkable functional diversity, which contributes significantly to the overall functioning of these ecosystems. Understanding the differences in microbial functions and employing appropriate statistical methods to analyze them is essential for advancing our knowledge inthis field.微生物功能差异和统计学方法微生物在各种生态系统中起着至关重要的作用，包括人体微生物组、土壤和海洋环境等。
这些微观生物表现出显著的功能多样性，对于整个生态系统的运行起到重要作用。
了解微生物功能之间的差异，并采用适当的统计学方法进行分析，对于推动该领域的知识进展非常重要。
To study the functional differences among microorganisms, researchers utilize various approaches such as metagenomics, transcriptomics, proteomics, and metabolomics. Metagenomics involves sequencing the DNA or RNA from samples containing mixed microbial communities to identify and characterize their genetic potential. Transcriptomics focuses onstudying gene expression patterns using techniques like RNA sequencing (RNA-seq). Proteomics examines the composition and abundance of proteins present in microbial cells. Metabolomics analyzes the small molecule metabolites produced by microorganisms.为了研究微生物之间的功能差异，研究人员采用了多种方法，如宏基因组学、转录组学、蛋白质组学和代谢组学。
关于人工学习的挑战英语作文
关于人工学习的挑战英语作文Artificial learning is a rapidly evolving field that has captured the attention of researchers, technologists, and the general public alike. As the capabilities of machine learning algorithms and artificial intelligence (AI) continue to advance, the potential applications of these technologies have become increasingly diverse and far-reaching. From automated decision-making systems to natural language processing and computer vision, the impact of artificial learning is being felt across a wide range of industries and domains.However, the development of effective and reliable artificial learning systems is not without its challenges. In this essay, we will explore some of the key challenges and considerations that must be addressed in order to realize the full potential of artificial learning.One of the primary challenges in artificial learning is the issue of data quality and availability. Effective machine learning models require large, high-quality datasets to train on, and the acquisition and curation of such data can be a significant obstacle. In many cases, the necessary data may not exist or may be difficult to obtain due toprivacy concerns, technical limitations, or other factors. Additionally, the data that is available may be biased, incomplete, or of poor quality, which can lead to suboptimal model performance and unreliable results.Another challenge in artificial learning is the issue of interpretability and transparency. Many modern machine learning algorithms, particularly those based on deep neural networks, are often described as "black boxes" – their inner workings and decision-making processes are complex and difficult to understand, even for the researchers and engineers who develop them. This lack of interpretability can be problematic in applications where transparency and accountability are essential, such as in healthcare, finance, or legal decision-making.Furthermore, the development of artificial learning systems often requires significant computational resources and specialized expertise, which can present barriers to entry for smaller organizations or individuals. The high costs associated with hardware, software, and skilled personnel can make it challenging for some entities to invest in and deploy these technologies effectively.Another key challenge in artificial learning is the issue of safety and robustness. As these systems become more sophisticated and are deployed in real-world applications, it is crucial that they are able tooperate reliably and safely, even in the face of unexpected or adversarial inputs. Ensuring the security and resilience of artificial learning systems is an ongoing area of research and development, as researchers work to address issues such as adversarial attacks, model drift, and unexpected edge cases.Additionally, the ethical implications of artificial learning must be carefully considered. As these systems become more powerful and influential, there are concerns about the potential for bias, discrimination, and unintended consequences. Questions around the fairness, accountability, and transparency of artificial learning systems must be addressed to ensure that they are developed and deployed in a responsible and ethical manner.Finally, the integration of artificial learning into existing systems and workflows can also present significant challenges. Effectively incorporating these technologies into complex, real-world environments often requires careful planning, coordination, and change management, as organizations must adapt their processes, infrastructure, and human resources to accommodate the new capabilities and requirements of artificial learning.In conclusion, the challenges facing the development and deployment of effective and reliable artificial learning systems are multifaceted and complex. From data quality and availability tointerpretability, computational resources, safety, ethics, and integration, there are numerous hurdles that must be overcome in order to realize the full potential of these technologies.However, despite these challenges, the field of artificial learning continues to evolve and progress, with researchers, technologists, and policymakers working to address these issues and drive the development of more advanced and capable systems. As these efforts continue, it is likely that we will see increasingly sophisticated and impactful applications of artificial learning in a wide range of domains, transforming the way we work, live, and interact with the world around us.。
深度学习精简版
如果信息是保持不变，这意味着输入I经过每一层Si都没有任何的信息损失，即 在任何一层Si，它都是原有信息（即输入I）的另外一种表示。现在回到我们的 主题Deep Learning，我们需要自动地学习特征，假设我们有一堆输入I（如一堆 图像或者文本），假设我们设计了一个系统S（有n层），我们通过调整系统中 参数，使得它的输出仍然是输入I，那么我们就可以自动地获取得到输入I的一系 列层次特征，即S1，…, Sn。
Deep learning
2012, Google Brain, 2012, 微究所”（IDL，Institue of Deep Learning）
为什么拥有大数据的互联网公司争相投入大量资源研发深度学习技术。听起来感 觉deeplearning很牛那样。 那什么是deep learning？ 为什么有deep learning？ 它是怎么来的？ 又能干什么呢？
Deep learning
例如图像识别、语音识别、自然语言理解、天气预测、基因表达、内容推荐等等。
Deep learning
Deep learning
1981 年的诺贝尔医学奖，颁发给了 David Hubel（出生于加拿大的美国神经生物学 家） 和TorstenWiesel，以及 Roger Sperry。前两位的主要贡献，是“发现了视觉系 统的信息处理”：可视皮层是分级的.
人工智能化词汇12.17
常用英语词汇-andrew Ng课程intensity 强度Regression 回归Loss function 损失函数non-convex 非凸函数neural network 神经网络supervised learning 监督学习regression problem 回归问题处理的是连续的问题classification problem 分类问题discreet value 离散值support vector machines 支持向量机learning theory 学习理论learning algorithms 学习算法unsupervised learning 无监督学习gradient descent 梯度下降linear regression 线性回归Neural Network 神经网络gradient descent 梯度下降normal equationslinear algebra 线性代数superscript上标exponentiation 指数training set 训练集合training example 训练样本hypothesis 假设，用来表示学习算法的输出LMS algorithm “least mean squares 最小二乘法算法batch gradient descent 批量梯度下降constantly gradient descent 随机梯度下降iterative algorithm 迭代算法partial derivative 偏导数contour 等高线quadratic function 二元函数locally weighted regression局部加权回归underfitting欠拟合overfitting 过拟合non-parametric learning algorithms 无参数学习算法parametric learning algorithm 参数学习算法activation 激活值activation function 激活函数additive noise 加性噪声autoencoder 自编码器Autoencoders 自编码算法average firing rate 平均激活率average sum-of-squares error 均方差backpropagation 后向传播basis 基basis feature vectors 特征基向量batch gradient ascent 批量梯度上升法Bayesian regularization method 贝叶斯规则化方法Bernoulli random variable 伯努利随机变量bias term 偏置项binary classfication 二元分类class labels 类型标记concatenation 级联conjugate gradient 共轭梯度contiguous groups 联通区域convex optimization software 凸优化软件convolution 卷积cost function 代价函数covariance matrix 协方差矩阵DC component 直流分量decorrelation 去相关degeneracy 退化demensionality reduction 降维derivative 导函数diagonal 对角线diffusion of gradients 梯度的弥散eigenvalue 特征值eigenvector 特征向量error term 残差feature matrix 特征矩阵feature standardization 特征标准化feedforward architectures 前馈结构算法feedforward neural network 前馈神经网络feedforward pass 前馈传导fine-tuned 微调first-order feature 一阶特征forward pass 前向传导forward propagation 前向传播Gaussian prior 高斯先验概率generative model 生成模型gradient descent 梯度下降Greedy layer-wise training 逐层贪婪训练方法grouping matrix 分组矩阵Hadamard product 阿达马乘积Hessian matrix Hessian 矩阵hidden layer 隐含层hidden units 隐藏神经元Hierarchical grouping 层次型分组higher-order features 更高阶特征highly non-convex optimization problem高度非凸的优化问题histogram 直方图hyperbolic tangent 双曲正切函数hypothesis 估值，假设identity activation function 恒等激励函数IID 独立同分布illumination 照明inactive 抑制independent component analysis 独立成份分析input domains 输入域input layer 输入层intensity 亮度/灰度intercept term 截距KL divergence 相对熵KL divergence KL分散度k-Means K-均值learning rate 学习速率least squares 最小二乘法linear correspondence 线性响应linear superposition 线性叠加line-search algorithm 线搜索算法local mean subtraction 局部均值消减local optima 局部最优解logistic regression 逻辑回归loss function 损失函数low-pass filtering 低通滤波magnitude 幅值MAP 极大后验估计maximum likelihood estimation 极大似然估计mean 平均值MFCC Mel 倒频系数multi-class classification 多元分类neural networks 神经网络neuron 神经元Newton’s method 牛顿法non-convex function 非凸函数non-linear feature 非线性特征norm 范式norm bounded 有界范数norm constrained 范数约束normalization 归一化numerical roundoff errors 数值舍入误差numerically checking 数值检验numerically reliable 数值计算上稳定object detection 物体检测objective function 目标函数off-by-one error 缺位错误orthogonalization 正交化output layer 输出层overall cost function 总体代价函数over-complete basis 超完备基over-fitting 过拟合parts of objects 目标的部件part-whole decompostion 部分-整体分解PCA 主元分析penalty term 惩罚因子per-example mean subtraction 逐样本均值消减pooling 池化pretrain 预训练principal components analysis 主成份分析quadratic constraints 二次约束RBMs 受限Boltzman机reconstruction based models 基于重构的模型reconstruction cost 重建代价reconstruction term 重构项redundant 冗余reflection matrix 反射矩阵regularization 正则化regularization term 正则化项rescaling 缩放robust 鲁棒性run 行程second-order feature 二阶特征sigmoid activation function S型激励函数significant digits 有效数字singular value 奇异值singular vector 奇异向量smoothed L1 penalty 平滑的L1范数惩罚Smoothed topographic L1 sparsity penalty 平滑地形L1稀疏惩罚函数smoothing 平滑Softmax Regresson Softmax回归sorted in decreasing order 降序排列source features 源特征sparse autoencoder 消减归一化Sparsity 稀疏性sparsity parameter 稀疏性参数sparsity penalty 稀疏惩罚square function 平方函数squared-error 方差stationary 平稳性（不变性）stationary stochastic process 平稳随机过程step-size 步长值supervised learning 监督学习symmetric positive semi-definite matrix对称半正定矩阵symmetry breaking 对称失效tanh function 双曲正切函数the average activation 平均活跃度the derivative checking method 梯度验证方法the empirical distribution 经验分布函数the energy function 能量函数the Lagrange dual 拉格朗日对偶函数the log likelihood 对数似然函数the pixel intensity value 像素灰度值the rate of convergence 收敛速度topographic cost term 拓扑代价项topographic ordered 拓扑秩序transformation 变换translation invariant 平移不变性trivial answer 平凡解under-complete basis 不完备基unrolling 组合扩展unsupervised learning 无监督学习variance 方差vecotrized implementation 向量化实现vectorization 矢量化visual cortex 视觉皮层weight decay 权重衰减weighted average 加权平均值whitening 白化zero-mean 均值为零Accumulated error backpropagation 累积误差逆传播Activation Function 激活函数Adaptive Resonance Theory/ART 自适应谐振理论Addictive model 加性学习Adversarial Networks 对抗网络Affine Layer 仿射层Affinity matrix 亲和矩阵Agent 代理/ 智能体Algorithm 算法Alpha-beta pruning α-β剪枝Anomaly detection 异常检测Approximation 近似Area Under ROC Curve／AUC Roc 曲线下面积Artificial General Intelligence/AGI 通用人工智能Artificial Intelligence/AI 人工智能Association analysis 关联分析Attention mechanism 注意力机制Attribute conditional independence assumption属性条件独立性假设Attribute space 属性空间Attribute value 属性值Autoencoder 自编码器Automatic speech recognition 自动语音识别Automatic summarization 自动摘要Average gradient 平均梯度Average-Pooling 平均池化Backpropagation Through Time 通过时间的反向传播Backpropagation/BP 反向传播Base learner 基学习器Base learning algorithm 基学习算法Batch Normalization/BN 批量归一化Bayes decision rule 贝叶斯判定准则Bayes Model Averaging／BMA 贝叶斯模型平均Bayes optimal classifier 贝叶斯最优分类器Bayesian decision theory 贝叶斯决策论Bayesian network 贝叶斯网络Between-class scatter matrix 类间散度矩阵Bias 偏置/ 偏差Bias-variance decomposition 偏差-方差分解Bias-Variance Dilemma 偏差–方差困境Bi-directional Long-Short Term Memory/Bi-LSTM 双向长短期记忆Binary classification 二分类Binomial test 二项检验Bi-partition 二分法Boltzmann machine 玻尔兹曼机Bootstrap sampling 自助采样法／可重复采样Bootstrapping 自助法Break-Event Point／BEP 平衡点Calibration 校准Cascade-Correlation 级联相关Categorical attribute 离散属性Class-conditional probability 类条件概率Classification and regression tree/CART 分类与回归树Classifier 分类器Class-imbalance 类别不平衡Closed -form 闭式Cluster 簇/类/集群Cluster analysis 聚类分析Clustering 聚类Clustering ensemble 聚类集成Co-adapting 共适应Coding matrix 编码矩阵COLT 国际学习理论会议Committee-based learning 基于委员会的学习Competitive learning 竞争型学习Component learner 组件学习器Comprehensibility 可解释性Computation Cost 计算成本Computational Linguistics 计算语言学Computer vision 计算机视觉Concept drift 概念漂移Concept Learning System /CLS 概念学习系统Conditional entropy 条件熵Conditional mutual information 条件互信息Conditional Probability Table／CPT 条件概率表Conditional random field/CRF 条件随机场Conditional risk 条件风险Confidence 置信度Confusion matrix 混淆矩阵Connection weight 连接权Connectionism 连结主义Consistency 一致性／相合性Contingency table 列联表Continuous attribute 连续属性Convergence 收敛Conversational agent 会话智能体Convex quadratic programming 凸二次规划Convexity 凸性Convolutional neural network/CNN 卷积神经网络Co-occurrence 同现Correlation coefficient 相关系数Cosine similarity 余弦相似度Cost curve 成本曲线Cost Function 成本函数Cost matrix 成本矩阵Cost-sensitive 成本敏感Cross entropy 交叉熵Cross validation 交叉验证Crowdsourcing 众包Curse of dimensionality 维数灾难Cut point 截断点Cutting plane algorithm 割平面法Data mining 数据挖掘Data set 数据集Decision Boundary 决策边界Decision stump 决策树桩Decision tree 决策树／判定树Deduction 演绎Deep Belief Network 深度信念网络DeepConvolutional Generative Adversarial Network DCGAN 深度卷积生成对抗网络Deep learning 深度学习Deep neural network/DNN 深度神经网络Deep Q-Learning 深度Q 学习Deep Q-Network 深度Q 网络Density estimation 密度估计Density-based clustering 密度聚类Differentiable neural computer 可微分神经计算机Dimensionality reduction algorithm 降维算法Directed edge 有向边Disagreement measure 不合度量Discriminative model 判别模型Discriminator 判别器Distance measure 距离度量Distance metric learning 距离度量学习Distribution 分布Divergence 散度Diversity measure 多样性度量／差异性度量Domain adaption 领域自适应Downsampling 下采样D-separation （Directed separation）有向分离Dual problem 对偶问题Dummy node 哑结点Dynamic Fusion 动态融合Dynamic programming 动态规划Eigenvalue decomposition 特征值分解Embedding 嵌入Emotional analysis 情绪分析Empirical conditional entropy 经验条件熵Empirical entropy 经验熵Empirical error 经验误差Empirical risk 经验风险End-to-End 端到端Energy-based model 基于能量的模型Ensemble learning 集成学习Ensemble pruning 集成修剪Error Correcting Output Codes／ECOC 纠错输出码Error rate 错误率Error-ambiguity decomposition 误差-分歧分解Euclidean distance 欧氏距离Evolutionary computation 演化计算Expectation-Maximization 期望最大化Expected loss 期望损失Exploding Gradient Problem 梯度爆炸问题Exponential loss function 指数损失函数Extreme Learning Machine/ELM 超限学习机Factorization 因子分解False negative 假负类False positive 假正类False Positive Rate/FPR 假正例率Feature engineering 特征工程Feature selection 特征选择Feature vector 特征向量Featured Learning 特征学习Feedforward Neural Networks/FNN 前馈神经网络Fine-tuning 微调Flipping output 翻转法Fluctuation 震荡Forward stagewise algorithm 前向分步算法Frequentist 频率主义学派Full-rank matrix 满秩矩阵Functional neuron 功能神经元Gain ratio 增益率Game theory 博弈论Gaussian kernel function 高斯核函数Gaussian Mixture Model 高斯混合模型General Problem Solving 通用问题求解Generalization 泛化Generalization error 泛化误差Generalization error bound 泛化误差上界Generalized Lagrange function 广义拉格朗日函数Generalized linear model 广义线性模型Generalized Rayleigh quotient 广义瑞利商Generative Adversarial Networks/GAN 生成对抗网络Generative Model 生成模型Generator 生成器Genetic Algorithm/GA 遗传算法Gibbs sampling 吉布斯采样Gini index 基尼指数Global minimum 全局最小Global Optimization 全局优化Gradient boosting 梯度提升Gradient Descent 梯度下降Graph theory 图论Ground-truth 真相／真实Hard margin 硬间隔Hard voting 硬投票Harmonic mean 调和平均Hesse matrix 海塞矩阵Hidden dynamic model 隐动态模型Hidden layer 隐藏层Hidden Markov Model/HMM 隐马尔可夫模型Hierarchical clustering 层次聚类Hilbert space 希尔伯特空间Hinge loss function 合页损失函数Hold-out 留出法Homogeneous 同质Hybrid computing 混合计算Hyperparameter 超参数Hypothesis 假设Hypothesis test 假设验证ICML 国际机器学习会议Improved iterative scaling/IIS 改进的迭代尺度法Incremental learning 增量学习Independent and identically distributed/i.i.d.独立同分布Independent Component Analysis/ICA 独立成分分析Indicator function 指示函数Individual learner 个体学习器Induction 归纳Inductive bias 归纳偏好Inductive learning 归纳学习Inductive Logic Programming／ILP 归纳逻辑程序设计Information entropy 信息熵Information gain 信息增益Input layer 输入层Insensitive loss 不敏感损失Inter-cluster similarity 簇间相似度International Conference for Machine Learning/ICML 国际机器学习大会Intra-cluster similarity 簇内相似度Intrinsic value 固有值Isometric Mapping/Isomap 等度量映射Isotonic regression 等分回归Iterative Dichotomiser 迭代二分器Kernel method 核方法Kernel trick 核技巧Kernelized Linear Discriminant Analysis／KLDA核线性判别分析K-fold cross validation k 折交叉验证／k 倍交叉验证K-Means Clustering K –均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base 知识库Knowledge Representation 知识表征Label space 标记空间Lagrange duality 拉格朗日对偶性Lagrange multiplier 拉格朗日乘子Laplace smoothing 拉普拉斯平滑Laplacian correction 拉普拉斯修正Latent Dirichlet Allocation 隐狄利克雷分布Latent semantic analysis 潜在语义分析Latent variable 隐变量Lazy learning 懒惰学习Learner 学习器Learning by analogy 类比学习Learning rate 学习率Learning Vector Quantization/LVQ 学习向量量化Least squares regression tree 最小二乘回归树Leave-One-Out/LOO 留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis／LDA 线性判别分析Linear model 线性模型Linear Regression 线性回归Link function 联系函数Local Markov property 局部马尔可夫性Local minimum 局部最小Log likelihood 对数似然Log odds／logit 对数几率Logistic Regression Logistic 回归Log-likelihood 对数似然Log-linear regression 对数线性回归Long-Short Term Memory/LSTM 长短期记忆Loss function 损失函数Machine translation/MT 机器翻译Macron-P 宏查准率Macron-R 宏查全率Majority voting 绝对多数投票法Manifold assumption 流形假设Manifold learning 流形学习Margin theory 间隔理论Marginal distribution 边际分布Marginal independence 边际独立性Marginalization 边际化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field 马尔可夫随机场Maximal clique 最大团Maximum Likelihood Estimation/MLE极大似然估计／极大似然法Maximum margin 最大间隔Maximum weighted spanning tree 最大带权生成树Max-Pooling 最大池化Mean squared error 均方误差Meta-learner 元学习器Metric learning 度量学习Micro-P 微查准率Micro-R 微查全率Minimal Description Length/MDL 最小描述长度Minimax game 极小极大博弈Misclassification cost 误分类成本Mixture of experts 混合专家Momentum 动量Moral graph 道德图／端正图Multi-class classification 多分类Multi-document summarization 多文档摘要Multi-layer feedforward neural networks多层前馈神经网络Multilayer Perceptron/MLP 多层感知器Multimodal learning 多模态学习Multiple Dimensional Scaling 多维缩放Multiple linear regression 多元线性回归Multi-response Linear Regression ／MLR多响应线性回归Mutual information 互信息Naive bayes 朴素贝叶斯Naive Bayes Classifier 朴素贝叶斯分类器Named entity recognition 命名实体识别Nash equilibrium 纳什均衡Natural language generation/NLG 自然语言生成Natural language processing 自然语言处理Negative class 负类Negative correlation 负相关法Negative Log Likelihood 负对数似然Neighbourhood Component Analysis/NCA近邻成分分析Neural Machine Translation 神经机器翻译Neural Turing Machine 神经图灵机Newton method 牛顿法NIPS 国际神经信息处理系统会议No Free Lunch Theorem／NFL 没有免费的午餐定理Noise-contrastive estimation 噪音对比估计Nominal attribute 列名属性Non-convex optimization 非凸优化Nonlinear model 非线性模型Non-metric distance 非度量距离Non-negative matrix factorization 非负矩阵分解Non-ordinal attribute 无序属性Non-Saturating Game 非饱和博弈Norm 范数Normalization 归一化Nuclear norm 核范数Numerical attribute 数值属性Letter OObjective function 目标函数Oblique decision tree 斜决策树Occam’s razor 奥卡姆剃刀Odds 几率Off-Policy 离策略One shot learning 一次性学习One-Dependent Estimator／ODE 独依赖估计On-Policy 在策略Ordinal attribute 有序属性Out-of-bag estimate 包外估计Output layer 输出层Output smearing 输出调制法Overfitting 过拟合／过配Oversampling 过采样Paired t-test 成对t 检验Pairwise 成对型Pairwise Markov property 成对马尔可夫性Parameter 参数Parameter estimation 参数估计Parameter tuning 调参Parse tree 解析树Particle Swarm Optimization/PSO 粒子群优化算法Part-of-speech tagging 词性标注Perceptron 感知机Performance measure 性能度量Plug and Play Generative Network 即插即用生成网络Plurality voting 相对多数投票法Polarity detection 极性检测Polynomial kernel function 多项式核函数Pooling 池化Positive class 正类Positive definite matrix 正定矩阵Post-hoc test 后续检验Post-pruning 后剪枝potential function 势函数Precision 查准率／准确率Prepruning 预剪枝Principal component analysis/PCA 主成分分析Principle of multiple explanations 多释原则Prior 先验Probability Graphical Model 概率图模型Proximal Gradient Descent/PGD 近端梯度下降Pruning 剪枝Pseudo-label 伪标记Quantized Neural Network 量子化神经网络Quantum computer 量子计算机Quantum Computing 量子计算Quasi Newton method 拟牛顿法Radial Basis Function／RBF 径向基函数Random Forest Algorithm 随机森林算法Random walk 随机漫步Recall 查全率／召回率Receiver Operating Characteristic/ROC受试者工作特征Rectified Linear Unit/ReLU 线性修正单元Recurrent Neural Network 循环神经网络Recursive neural network 递归神经网络Reference model 参考模型Regression 回归Regularization 正则化Reinforcement learning/RL 强化学习Representation learning 表征学习Representer theorem 表示定理reproducing kernel Hilbert space/RKHS再生核希尔伯特空间Re-sampling 重采样法Rescaling 再缩放Residual Mapping 残差映射Residual Network 残差网络Restricted Boltzmann Machine/RBM 受限玻尔兹曼机Restricted Isometry Property/RIP 限定等距性Re-weighting 重赋权法Robustness 稳健性/鲁棒性Root node 根结点Rule Engine 规则引擎Rule learning 规则学习Saddle point 鞍点Sample space 样本空间Sampling 采样Score function 评分函数Self-Driving 自动驾驶Self-Organizing Map／SOM 自组织映射Semi-naive Bayes classifiers 半朴素贝叶斯分类器Semi-Supervised Learning 半监督学习semi-Supervised Support Vector Machine半监督支持向量机Sentiment analysis 情感分析Separating hyperplane 分离超平面Sigmoid function Sigmoid 函数Similarity measure 相似度度量Simulated annealing 模拟退火Simultaneous localization and mapping同步定位与地图构建Singular Value Decomposition 奇异值分解Slack variables 松弛变量Smoothing 平滑Soft margin 软间隔Soft margin maximization 软间隔最大化Soft voting 软投票Sparse representation 稀疏表征Sparsity 稀疏性Specialization 特化Spectral Clustering 谱聚类Speech Recognition 语音识别Splitting variable 切分变量Squashing function 挤压函数Stability-plasticity dilemma 可塑性-稳定性困境Statistical learning 统计学习Status feature function 状态特征函Stochastic gradient descent 随机梯度下降Stratified sampling 分层采样Structural risk 结构风险Structural risk minimization/SRM 结构风险最小化Subspace 子空间Supervised learning 监督学习／有导师学习support vector expansion 支持向量展式Support Vector Machine/SVM 支持向量机Surrogat loss 替代损失Surrogate function 替代函数Symbolic learning 符号学习Symbolism 符号主义Synset 同义词集T-Distribution Stochastic Neighbour Embedding t-SNE T –分布随机近邻嵌入Tensor 张量Tensor Processing Units/TPU 张量处理单元The least square method 最小二乘法Threshold 阈值Threshold logic unit 阈值逻辑单元Threshold-moving 阈值移动Time Step 时间步骤Tokenization 标记化Training error 训练误差Training instance 训练示例／训练例Transductive learning 直推学习Transfer learning 迁移学习Treebank 树库Tria-by-error 试错法True negative 真负类True positive 真正类True Positive Rate/TPR 真正例率Turing Machine 图灵机Twice-learning 二次学习Underfitting 欠拟合／欠配Undersampling 欠采样Understandability 可理解性Unequal cost 非均等代价Unit-step function 单位阶跃函数Univariate decision tree 单变量决策树Unsupervised learning 无监督学习／无导师学习Unsupervised layer-wise training 无监督逐层训练Upsampling 上采样Vanishing Gradient Problem 梯度消失问题Variational inference 变分推断VC Theory VC维理论Version space 版本空间Viterbi algorithm 维特比算法Von Neumann architecture 冯·诺伊曼架构Wasserstein GAN/WGAN Wasserstein生成对抗网络Weak learner 弱学习器Weight 权重Weight sharing 权共享Weighted voting 加权投票法Within-class scatter matrix 类内散度矩阵Word embedding 词嵌入Word sense disambiguation 词义消歧Zero-data learning 零数据学习Zero-shot learning 零次学习approximations近似值arbitrary随意的affine仿射的arbitrary任意的amino acid氨基酸amenable经得起检验的axiom公理，原则abstract提取architecture架构，体系结构；建造业absolute绝对的arsenal军火库assignment分配algebra线性代数asymptotically无症状的appropriate恰当的bias偏差brevity简短，简洁；短暂[800 ] broader广泛briefly简短的batch批量convergence 收敛，集中到一点convex凸的contours轮廓constraint约束constant常理commercial商务的complementarity补充coordinate ascent同等级上升clipping剪下物；剪报；修剪component分量；部件continuous连续的covariance协方差canonical正规的，正则的concave非凸的corresponds相符合；相当；通信corollary推论concrete具体的事物，实在的东西cross validation交叉验证correlation相互关系convention约定cluster一簇centroids 质心，形心converge收敛computationally计算(机)的calculus计算derive获得，取得dual二元的duality二元性；二象性；对偶性derivation求导；得到；起源denote预示，表示，是…的标志；意味着，[逻]指称divergence 散度；发散性dimension尺度，规格；维数dot小圆点distortion变形density概率密度函数discrete离散的discriminative有识别能力的diagonal对角dispersion分散，散开determinant决定因素disjoint不相交的encounter遇到ellipses椭圆equality等式extra额外的empirical经验；观察ennmerate例举，计数exceed超过，越出expectation期望efficient生效的endow赋予explicitly清楚的exponential family指数家族equivalently等价的feasible可行的forary初次尝试finite有限的，限定的forgo摒弃，放弃fliter过滤frequentist最常发生的forward search前向式搜索formalize使定形generalized归纳的generalization概括，归纳；普遍化；判断（根据不足）guarantee保证；抵押品generate形成，产生geometric margins几何边界gap裂口generative生产的；有生产力的heuristic启发式的；启发法；启发程序hone怀恋；磨hyperplane超平面initial最初的implement执行intuitive凭直觉获知的incremental增加的intercept截距intuitious直觉instantiation例子indicator指示物，指示器interative重复的，迭代的integral积分identical相等的；完全相同的indicate表示，指出invariance不变性，恒定性impose把…强加于intermediate中间的interpretation解释，翻译joint distribution联合概率lieu替代logarithmic对数的，用对数表示的latent潜在的Leave-one-out cross validation留一法交叉验证magnitude巨大mapping绘图，制图；映射matrix矩阵mutual相互的，共同的monotonically单调的minor较小的，次要的multinomial多项的multi-class classification二分类问题nasty讨厌的notation标志，注释naïve朴素的obtain得到oscillate摆动optimization problem最优化问题objective function目标函数optimal最理想的orthogonal(矢量，矩阵等)正交的orientation方向ordinary普通的occasionally偶然的partial derivative偏导数property性质proportional成比例的primal原始的，最初的permit允许pseudocode伪代码permissible可允许的polynomial多项式preliminary预备precision精度perturbation 不安，扰乱poist假定，设想positive semi-definite半正定的parentheses圆括号posterior probability后验概率plementarity补充pictorially图像的parameterize确定…的参数poisson distribution柏松分布pertinent相关的quadratic二次的quantity量，数量；分量query疑问的regularization使系统化；调整reoptimize重新优化restrict限制；限定；约束reminiscent回忆往事的；提醒的；使人联想…的（of）remark注意random variable随机变量respect考虑respectively各自的；分别的redundant过多的；冗余的susceptible敏感的stochastic可能的；随机的symmetric对称的sophisticated复杂的spurious假的；伪造的subtract减去；减法器simultaneously同时发生地；同步地suffice满足scarce稀有的，难得的split分解，分离subset子集statistic统计量successive iteratious连续的迭代scale标度sort of有几分的squares平方trajectory轨迹temporarily暂时的terminology专用名词tolerance容忍；公差thumb翻阅threshold阈，临界theorem定理tangent正弦unit-length vector单位向量valid有效的，正确的variance方差variable变量；变元vocabulary词汇valued经估价的；宝贵的wrapper包装总计1038词汇。
robustness
robustnessRobustnessIntroduction:Robustness is a fundamental concept in various fields, including engineering, computer science, and biology. It refers to the ability of a system or organism to withstand and adapt to changing conditions, disturbances, or uncertainties. In this document, we will explore the concept of robustness, its significance in different domains, and strategies to enhance it.1. Importance of Robustness:Robustness plays a crucial role in ensuring the stability, reliability, and efficiency of systems. In engineering, it is essential to design robust structures and machines that can endure extreme conditions without significant damage. For example, in civil engineering, structures like buildings and bridges must be robust enough to withstand earthquakes, heavy winds, and other external forces.Similarly, in computer science, robustness is critical for ensuring the resilience and availability of software systems.Robust software is capable of handling unexpected user inputs, recovering from errors, and preventing crashes or system failures. This is especially important in mission-critical systems such as aviation, healthcare, and financial sectors.2. Factors Affecting Robustness:Various factors contribute to the robustness or vulnerability of a system. One crucial factor is redundancy, which involves duplicating critical components or functionalities. Redundancy can provide backup mechanisms that help the system to continue functioning even if some parts fail. For example, in electric power distribution systems, redundancy is employed to minimize the impact of a single point of failure.Another factor is adaptability, which refers to the system's ability to adjust its behavior or configuration in response to changing conditions. Adaptive systems can detect deviations, learn from them, and modify their strategies accordingly. This adaptability is particularly important in dynamic environments, such as autonomous vehicles navigating through traffic or robotics systems faced with unpredictable scenarios.Furthermore, fault tolerance is a measure of a system's ability to continue operating even when certain components orprocesses fail. Fault-tolerant systems utilize mechanisms such as error detection, error recovery, and error correction to prevent complete system failures. These techniques are commonly employed in communication networks, where the failure of individual nodes should not disrupt the entire network.3. Enhancing Robustness:There are several strategies that can be employed to enhance the robustness of a system or organism:a. Redundancy: Introducing redundancies in critical components or processes can provide backup mechanisms to ensure continuous operation. Redundancy can be achieved through component duplication, introducing backup systems, or implementing failover mechanisms.b. Testing and Validation: Thorough testing and validation processes can help identify vulnerabilities and weaknesses in a system. By subjecting the system to various testing scenarios and analyzing its responses, developers can strengthen the system's resilience and prepare it for unexpected challenges.c. Error Handling and Recovery: Implementing robust error handling and recovery mechanisms is crucial for preventing system failures. Techniques such as exception handling, error logging, and graceful degradation can help the system recover from errors in a controlled manner while minimizing the impact on its overall operation.d. Adaptive Control: Adaptive control strategies enable the system to continuously monitor its performance and make real-time adjustments to optimize its behavior. This can involve algorithms that learn from past experiences, machine learning techniques, or feedback control systems that adjust parameters based on environmental changes.e. Security Measures: Enhancing the security of a system can significantly contribute to its robustness. Implementing secure authentication protocols, encryption algorithms, and access control mechanisms can safeguard the system against various threats and minimize vulnerabilities.Conclusion:Robustness is a vital characteristic that ensures the stability, reliability, and adaptability of systems in various domains. By incorporating redundancy, adaptability, fault tolerance, testing/validation, error handling, and security measures,system developers can enhance robustness and ensure optimal performance even under challenging conditions. As technology continues to advance, robustness will remain a critical consideration in designing and managing complex systems.。
支持向量机回归模型英文专业名词
支持向量机回归模型英文专业名词Support Vector Regression (SVR) is a powerful machine learning technique that extends the principles of Support Vector Machines (SVM) to tackle regression problems. Unlike SVMs which are primarily used for classification, SVR models are adept at predicting continuous values.SVR operates by finding the optimal hyperplane that best fits the data within a margin of error, known as the epsilon-tube. This tube encapsulates the data points, allowing for some degree of error, which is crucial for handling real-world data that may contain noise.One of the key features of SVR is its ability to handle non-linear relationships through the use of kernel functions. These functions transform the input data into a higher-dimensional space where a linear regression can be applied, thus making SVR versatile for complex datasets.Regularization is another important aspect of SVR, which helps in preventing overfitting by controlling the model's complexity. The regularization parameter, often denoted as C, plays a pivotal role in balancing the trade-off between achieving a low error and maintaining model simplicity.In practice, SVR models require careful tuning of parameters such as C, the kernel type, and kernel parameters to achieve optimal performance. Cross-validation techniquesare commonly used to find the best combination of these parameters for a given dataset.SVR has been successfully applied in various fields, including finance for predicting stock prices, in medicine for forecasting patient outcomes, and in engineering for modeling complex systems. Its robustness and adaptability make it a valuable tool in the machine learning toolkit.Despite its advantages, SVR can be computationally intensive, especially with large datasets, due to the quadratic programming problem it needs to solve. However, with the advancement of computational resources and optimization algorithms, SVR remains a viable option for regression tasks.。
Bipedal Walking on Rough Terrain Using Manifold Control
Bipedal Walking on Rough Terrain Using Manifold ControlTom Erez and William D.SmartMedia and Machines Lab,Department of Computer Science and EngineeringWashington University in St.Louis,MOetom,wds@Abstract—This paper presents an algorithm for adapting periodic behavior to gradual shifts in task parameters.Since learning optimal control in high dimensional domains is subject to the’curse of dimensionality’,we parametrize the policy only along the limit cycle traversed by the gait,and thus focus the computational effort on a closed one-dimensional manifold,embedded in the high-dimensional state space.We take an initial gait as a departure point,and iterate between modifying the task slightly,and adapting the gait to this modification.This creates a sequence of gaits,each optimized for a different variant of the task.Since every two gaits in this sequence are very similar,the whole sequence spans a two-dimensional manifold,and combining all policies in this 2-manifold provides additional robustness to the system.We demonstrate our approach on two simulations of bipedal robots —the compass gait walker,which is a four-dimensional system, and RABBIT,which is ten-dimensional.The walkers’gaits are adapted to a sequence of changes in the ground slope,and when all policies in the sequence are combined,the walkers can safely traverse a rough terrain,where the incline changes at every step.I.INTRODUCTIONThis paper deals with the general task of augmenting the capacities of legged robots by using reinforcement learn-ing1.The standard paradigm in Control Theory,whereby an optimized reference trajectory is foundfirst,and then a stabilizing controller is designed,can become laborious when a whole range of task variants are considered.Standard algo-rithms of reinforcement learning cannot yet offer compelling alternatives to the control theory paradigm,mostly because of the prohibitive effect of the curse of dimensionality. Legged robots often constitute a high-dimensional system, and standard reinforcement learning methods,with their focus on Markov Decision Processes models,usually cannot overcome the exponential growth in state space volume. Most previous work in machine learning for gait domains required either an exhaustive study of the state space[1], or the use of non-specific optimization techniques,such as genetic algorithms[2].In this paper,we wish to take a first step towards efficient reinforcement learning in high-dimensional domains by focusing on periodic tasks.We make the observation that while legged robots have a high-dimensional state space,not every point in the state space represents a viable pose.By definition,a proper gait would always converge to a stable limit cycle,which traces a closed one-dimensional manifold embedded in the 1The interested reader is encouraged to follow the links mentioned in the footnotes to section IV to see movies of our simulations high-dimensional state space.This is true for any system performing a periodic task,regardless of the size of its state space(see also[3],section3.1,and[4],figure19,for a validation of this point in the model discussed below).This observation holds a great promise:a controller that can keep the system close to one particular limit cycle despite minor perturbations(i.e.has a non-trivial basin of attraction)is free to safely ignore the entire volume of the state space. Finding such a stable controller is far from trivial,and amounts to creating a stable gait.However,for our purpose, such a controller can be suboptimal,and may be supplied by a human tele-operating the system,by leveraging on passive dynamic properties of the system(as in section IV-A),or by applying control theory tools(as in section IV-B).In all cases,the one-dimensional manifold traced by the gait of a stable controller can be identified in one cycle of the gait, simply by recording the state of the system at every time step. Furthermore,by querying the controller,we can identify the local policy on and around that manifold.With these two provided,we can create a local representation of the policy which generated the gait by approximating the policy only on and around that manifold,like a ring embedded in state space,and this holds true regardless of the dimensionality of the state space.By representing the original control function in a compact way we may focus our computational effort on the relevant manifold alone,and circumvent the curse of dimensionality as such a parametrization does not scale exponentially with the number of dimensions.This opens a door for an array of reinforcement learning methods(such as policy gradient)which may be used to adapt the initial controller to different conditions,and thus augment the capacities of the system.In this article we report two experiments.Thefirst studies the Compass-Gait walker([9],[10],[11],a system known for its capacity to walk without actuation on a small range of downward slopes.The second experiment uses a simulation of the robot RABBIT[3],a biped robot with knees and a torso,but no feet,which has been studied before by the control theory community[5],[4],[6].Thefirst model has a four-dimensional state space,and the second model has10 state dimensions and4action dimensions.By composing together several controllers,each adapted to a different incline,we are able to create a composite controller that can stably traverse a rough terrain going downhill.The same algorithm was successfully applied to the second system too, although the size of that problem would be prohibitive formost reinforcement learning algorithms.In the following wefirst give a short review of previous work in machine learning,and then explain the technical aspects of constructing a manifold controller,as well as the learning algorithms used.We then demonstrate the effec-tiveness of Manifold Control by showing how it is used to augment the capacities of existing controllers in two different models of bipedal walk.We conclude by discussing the potential of our approach,and offer directions for future work.II.P REVIOUS W ORKThe generalfield of gait design has been at the focus of mechanical engineering for many years,and recent years saw an increase in the contribution from the domain of machine learning.For example,Stilman et al.[7]studied an eight-dimensional system of a biped robot with knees, similar to the one studied below.They showed that in their particular case the dimensionality can be reduced through some specific approximations during different phases.Then, they partitioned the entire volume of the reduced state space into a grid,and performed Q-learning using a simulation model of the system’s dynamics.The result was a robot walker that can handle a range of slopes around the level horizontal plane.In addition,there is a growing interest in recent years in gaits that can effectively take advantage of the passive dynamics(see the review by Collins et al.[8]for a thorough coverage).Tedrake[9]discusses several versions the com-pass gait walker which were built and analyzed.Controllers for the compass gait based on an analytical treatment of the system equations wasfirst suggested by Goswami et al.[10],who used both hip and ankle actuation.Further results were described by Spong and Bhatia[11],where the case of uneven terrain was also discussed.Ramamoorthy and Kuipers[12]suggested hybrid control of walking over irregular terrain by seeking inspiration from human walking.III.M ANIFOLD C ONTROLA.The Basic Control SchemeThe basic idea in manifold control is to focus the com-putational effort on the limit cycle.Therefore,the policy is approximated using locally activated processing elements (PEs),positioned along the manifold spanned by the limit cycle.Each PE defines a local policy,linear in the position of the state relative to that PE.When the policy is queried with a given state x,the local policy of each PE is calculated as:µi(x)=[1(x−c i)T M T]G i(1) where c i is the location of element i,M is a diagonal matrix which determines the scale of each dimension,and G i is an (n+1)-by-m matrix,where m is the action dimension and n is the number of state space dimensions.G i is made of m columns,one for each action dimension,and each column is an(n+1)-sized gain vector.Thefinal policy u(x)is calculated by mixing the local policies of each PEaccordingFig.1.Illustrations of the models used.On the left,the compass-gait walker:the system’s state is defined by the two legs’angles from the vertical direction and the associated angular velocities,for a total of four dimensions. Thisfigure also depicts the incline of the sloped ground.On the right, RABBIT:the system’s state is defined bt the angle of the torso form the vertical direction,the angles between the thighs and the torso,and the knee angles between the shank and the thigh.This model of RABBIT has ten state dimensions,where at every moment one leg isfixed to the ground, and the other leg is free to swing.to a normalized Gaussian activation function,usingσas a scalar bandwidth term:w i=exp(−(x−c i)T M TσM(x−c i)),(2)u(x)= n i=1w iµitraverse a path of higher value(i.e.collect more rewards,or less cost)along its modified limit cycle.1)Defining the Value Function:In the present work we consider a standard nondiscounted reinforcement learning formulation with afinite time horizon and no terminal costs. More accurately,we define the Value Vπ(x0)of a given state x0under afixed policyπ(x)as:Vπ(x0)= T0r(x t,π(x t))dt(4) where r(x,u)is the reward determined by the current state and the selected action,T is the time horizon,and x t is the solution of the time invariant ordinary differential equation ˙x=f(x,π(x))with the initial condition x=x0,so thatx t= t0f(xτ,π(xτ))dτ.(5) 2)Approximating the Policy Gradient:With C being the locations of the processing elements,and G being the set of their local gains,we make use of a method,due to[14],of piecewise estimation of the gradient of the value function at a given initial state x0with respect to the parameter set G. As Munos showed in[14],from(4)we can write∂V∂G,(6) and for the general form r=r(x,u)we can decompose∂r/∂G as∂r∂u ∂u∂x∂xFig.2.The path used to test the compass gait walker,and an overlay of the walker traversing this path.Note how the step length adapts to the changing incline.the forward swing,but will undergo an inelastic collision with thefloor during the backward swing.At this point it will become the stance leg,and the other leg will be set free to swing.The entire system is placed on a plane that is inclined at a variable angleφfrom the horizon.In the interests of brevity,we omit a complete description of the system dynamics here,referring the interested reader to the relevant literature[15],[10],[11].Although previous work considered actuation both at the hip and the ankle,we chose to study the case of actuation at the hip only.The learning phase in this system was done using simple stochastic gradient ascent,rather than the elaborate policy gradient estimation described in section III-B.2.The initial manifold was sampled at an incline ofφ=−0.05(the initial policy is the zero policy,so there were no approximation errors involved).One shaping iteration consisted of the following:first,G was modified to G tent=G+ηδG, withη=0.1andδG drawn at random from a multinormal distribution with unit covariance.The new policy’s perfor-mance was measured as a sum of all the rewards along20 steps.If the value of this new policy was bigger than the present one,it was adopted,otherwise it was rejected.Then, a newδG was drawn,and the process repeated itself.After 3successful adoptions,the shaping iterations step concluded with a resampling of the new controller,and the incline was decreased by0.01.After10shaping iteration steps,we had controllers that could handle inclines up toφ=−0.14.After another10 iteration steps with the incline increasing by0.005,we had controllers that could handle inclines up toφ=0.0025(a slight uphill).This range is approxmately double the limit of the passive walker[15].Finally,we combined the various controllers into one composite controller.This new controller used1500charts to span a two-dimensional manifold embedded in the four-dimensional state space.The performance of the composite controller was tested on an uneven terrain where the incline was gradually changed fromφ=0toφ=0.15radians, made of“tiles”of variable length whose inclines were0.01 radians apart.figure III-B.2shows an overlay of the walker’s downhill path.A movie of this march is available online.2B.The Uphill-Walking RABBIT RobotWe applied manifold control also to simulations of the legged robot RABBIT,using code from Prof.Jessy Grizzle that is freely available online[16].RABBIT is a biped robot with a torso,two knees and no feet(seefigure1b.),and is actuated at four places:both hip joins(where thighs are actuated against the torso),and both knees(where shanks are actuated against the thighs).The simulation assumes a stance leg with no slippage,and a swing leg that is free to move at all directions until it collides inelastically with the floor,and becomes the stance leg,freeing the other leg to swing.This robot too is modeled as a nonlinear system with impulse effects.Again,we are forced to omit a complete reconstruction of the model’s details,and refer the interested reader to[4],equation8.This model was studied extensively by the control theory community.In particular,an optimal desired signal was derived in[6],and a controller that successfully realizes this signal was presented in[4].However,all the efforts were focused on RABBIT walking on even terrain.We sought a way to augment the capacities of the RABBIT model,and allow it to traverse a rough,uneven terrain.We found that the controller suggested by[4]can easily handle negative (downhill)incline of0.2radiand and more,but cannot handle positive(uphill)inclines.3.Learning started by approximating the policy from[4]as a manifold controller,using400processing elements with a mean distance of about0.03state space length units.The performance of the manifold controller was indistinguishable to the naked eye from the original controller,and perfor-mance,as measured by the performance criterion C3in[6] (the same used by[4]),was only1%worse,probably due to minor approximation errors.The policy gradient was estimated using(6),according to a simple reward model:r(x,u)=10v x hip−1Fig.3.The rough terrain traversed by RABBIT.Since this model has knees,it can walk both uphill and downhill.Note how the step length adapts to the changing terrain.The movie of this parade can be seen at tt http:/2b8sdm,which is a shortcut to the YouTube website.where v xhip is the velocity of the hip joint(where the thighand the torso meet)in the positive direction of the X-axis, and u max is a scalar parameter(in our case,chosen to be 120)that tunes the nonlinear action penalty and promotes energetic efficiency.After the initial manifold controller was created,the system followed a fully automated shaping protocol for 20iterations:at every iteration,∂V/∂G was estimated, andηwasfixed to0.1%of|G|.This small learning rate ensured that we don’t modify the policy too much and lose stability.The modified policy,assumed to be slightly better, was then tested on a slightly bigger incline(the veryfirst manifold controller was tried on an incline of0rad.,and in every iteration we increased the incline in0.003rad.).This small modification to the model parameters ensured that the controller can still walk stably on the incline.If stability was not lost(as was the case in all our iterations),we resampled u(·;C,G new)so that C adj overlapped the limit cycle of the modified system(with the new policy and new incline),and the whole process repeated.This procedure allowed a gradual increase in the system’s maximal stable incline.Figure4depicts the evolution of the stability margins of every ring along the shaping iteration:for every iteration we present an upper(and lower)bound on the incline for which the controller can maintain stability.Thiswas tested by setting a test incline,and allowing the system to run for10 seconds.If no collapse happened by this time,the test incline was raised(lowered),until an incline was found for which the system can no longer maintain stability.As this picture shows,our automated shaping protocol does not maintain a tight control on the stability margins-for most iterations,a modest improvement is recorded.The system’s nonlinearity is well illustrated by the curious case of iteration9,where the same magnitude ofδG causes a massive improvement, despite the fact that the control manifold itself didn’t change dramatically(seefigure5).The converse is also true for some iterations(such as17and18)there is a decrease in the stability margins,but this is not harming the overall effectiveness,since these iterations are using training data obtained at an incline that is very far from the stability Fig.4.Thisfigure shows the inclines for which each iteration could maintain a stable gait on the RABBIT model.The diagonal line shows the incline for which each iteration was trained.Iteration0is the original controller.The initial manifold control approximation degrades most of the stability margin of the original control,but this is quickly regained through adaptation.Note that both the learning rate and the incline change rate were held constant through the entire process.The big jump in iteration 9exemplifies the nonlinearity of the system,as small changes may have unpredictable results,in this case,for the best.margin.Finally,three iterations were composed together,and the resulting controller successfully traversed a rough terrain that included inclines from−0.05to0.15radians.Figure3 shows an overlay image of the rough path.V.C ONCLUSION AND F UTURE W ORKIn this paper we present a compact representation of the policy for periodic tasks,and apply a trajectory-based policy gradient algorithm to it.Most importantly,the methods we present do not scale exponentially with the number of dimensions,and hence allow us to circumvent the curse of dimensionality in the particular case of periodic tasks.By following a gradual shaping process,we are able to create robust controllers that augment the capacities of existing systems in a consistent way.33.54−1.5−0.2anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v el.anglea n g . v e l.anglea n g . v e l.Fig.5.A projection of the manifold of several stages of the shaping process for the RABBIT model.The top row shows the angle and angular velocity between the torso and the stance thigh,and the bottom row shows the angle and angular velocity of the knee of the swing leg.Every two consecutive iterations are only slightly different from each other.Throughout the entire shaping process,changes accumulate,and new solutions emerge.Manifold control may also be used when the initial con-troller is profoundly suboptimal 4.It is also important to note that the rough terrain was traversed without informing the walker of the current terrain.We may say that the walkers walked blindly on their rough path.This demonstrates how stable a composite manifold controller can be.However,in some practical applications it could be beneficial to represent this important piece of information explicitly,and select the most appropriate ring at every step.We believe that the combination of local learning and careful shaping holds a great promise to many applications of periodic tasks,and hope to demonstrate it through future work on even higher-dimensional systems.Future research directions could include methods that allow second-order convergence,and learning a model of the plant.R EFERENCES[1]M.Stilman,C.G.Atkeson,J.J.Kuffner,and G.Zeglin,“Dynamicprogramming in reduced dimensional spaces:Dynamic planning for robust biped locomotion,”in Proceedings of the 2005IEEE Interna-tional Conference on Robotics and Automation (ICRA 2005),2005,pp.2399–2404.[2]J.Buchli, F.Iida,and A.Ijspeert,“Finding resonance:Adaptivefrequency oscillators for dynamic legged locomotion,”in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE,2006,pp.3903–3909.[3] C.Chevallereau and P.Sardain,“Design and actuation optimization ofa 4-axes biped robot for walking and running,”in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),2000.[4] F.Plestan,J.W.Grizzle,E.Westervelt,and G.Abba,“Stable walkingof a 7-dof biped robot,”IEEE Trans.Robot.Automat.,vol.19,no.4,pp.653–668,Aug.2003.4theinterested reader is welcome to see other results of manifold learning on a 14-dimensional system at /2h3qny and /2462j7.[5] C.Sabourin,O.Bruneau,and G.Buche,“Experimental validation ofa robust control strategy for the robot rabbit,”in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),2005.[6] C.Chevallereau and Y .Aoustin,“Optimal reference trajectories forwalking and running of a biped robot,”Robotica ,vol.19,no.5,pp.557–569,2001.[7]M.Stilman,C.G.Atkeson,J.J.Kuffner,and G.Zeglin,“Dynamicprogramming in reduced dimensional spaces:Dynamic planning for robust biped locomotion,”in Proceedings of the 2005IEEE Interna-tional Conference on Robotics and Automation (ICRA 2005),2005,pp.2399–2404.[8]S.H.Collins,A.Ruina,R.Tedrake,,and M.Wisse,“Efficient bipedalrobots based on passive-dynamic walkers,”Science ,pp.307:1082–1085,February 2005.[9]R.L.Tedrake,“Applied optimal control for dynamically stable leggedlocomotion,”Ph.D.dissertation,Massachusetts Institute of Technol-ogy,August 2004.[10] A.Goswami, B.Espiau,and A.Keramane,“Limit cycles in apassive compass gait biped and passivity-mimicking control laws,”Autonomous Robots ,vol.4,no.3,pp.273–286,1997.[11]M.W.Spong and G.Bhatia,“Further results on the control of the com-pass gait biped,”in Proceedings of the 2003IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003),vol.2,2003,pp.1933–1938.[12]S.Ramamoorthy and B.Kuipers,“Qualitative hybrid control ofdynamic bipedal walking,”in Robotics :Science and Systems II ,G.S.Sukhatme,S.Schaal,W.Burgard,and D.Fox,Eds.MIT Press,2007.[13]S.Schaal and C.Atkeson,“constructive incremental learning fromonly local information,”neural computation ,no.8,pp.2047–2084,1998.[14]R.Munos,“Policy gradient in continuous time,”Journal of MachineLearning Research ,vol.7,pp.771–791,2006.[15] A.Goswami,B.Thuilot,and B.Espiau,“Compass-like biped robotpart i:Stability and bifurcation of passive gaits,”INRIA,Tech.Rep.2996,October 1996.[16] E.Westervelt, B.Morris,and J.Grizzle.(2003)Five link walker.IEEE-CDC Workshop:Feedback Control of Biped Walking Robots.[Online].Available:/2znlz2。
用alignment造句
用alignment造句英文回答：Alignment is a critical concept in machine learning, computer vision, and natural language processing. It refers to the process of aligning different elements, such as images, text, or data points, to facilitate their comparison, analysis, or integration.In natural language processing, alignment is used to match words, phrases, or sentences in different languages or texts. This is essential for tasks such as machine translation, where a source text in one language needs to be aligned with its translation in another language. Alignment algorithms can be rule-based, statistical, or neural network-based, and they typically involve finding the best possible match between elements, considering factors such as word order, grammar, and semantics.In computer vision, alignment is used to align imagesor objects to facilitate their comparison, recognition, or tracking. This can involve geometric transformations such as translation, rotation, scaling, or warping, and it is often performed using image processing techniques such as feature extraction, keypoint detection, and homography estimation. Alignment is crucial for tasks such as object recognition, image stitching, and video analysis.In machine learning, alignment refers to the process of aligning the predictions of different models or the features of different data points. Model alignment can be used to improve the accuracy and robustness of ensemble models, where multiple models make predictions on the same data. Feature alignment, on the other hand, can be used to facilitate the comparison and integration of data from different sources, which is essential for tasks such as transfer learning and multi-modal learning.Alignment techniques are essential for various applications in natural language processing, computer vision, and machine learning. They enable the comparison, analysis, and integration of different elements, leading toimproved performance in tasks such as machine translation, object recognition, model ensemble, and data integration.中文回答：对齐是机器学习、计算机视觉和自然语言处理中的一个关键概念。
Applied Machine Learning
Applied Machine Learning Machine learning is a rapidly growing field that has the potential to revolutionize various industries and aspects of our daily lives. From healthcare to finance to transportation, the applications of machine learning are vast and diverse. However, with this potential comes a host of challenges and problems that need to be addressed in order for machine learning to reach its full potential. In this response, we will explore some of the key problems facing applied machine learning and discuss potential solutions and avenues for further research. One of the primary challenges in applied machine learning is the issue of bias in data and algorithms. Machine learning models are only as good as the data they are trained on, and if this data is biased or incomplete, the resulting model will also be biased. This can have serious implications in areas such as hiring, lending, and criminal justice, where biased algorithms can perpetuate and even exacerbate existing inequalities. Addressing this problem requires a multi-faceted approach, including careful curation of training data, transparency in algorithmic decision-making, and ongoing monitoring and evaluation of model performance. Another significant problem in applied machine learning is the issue of interpretability. Many machine learning models, particularly deep learning models, are often referred to as "black boxes" due to their complexity and lack of transparency. This can be a major barrier to their adoption in fields where interpretability is crucial, such as healthcare and finance. Researchers and practitioners are actively working on developing methods for making machine learning models more interpretable, such as through the use of attention mechanisms and model distillation techniques. However, this remains an ongoing area of research and development. In addition to bias and interpretability, another challenge in applied machine learning is the issue of scalability. While many machine learning algorithms have been shown to perform well on small-scale problems, scaling these algorithms to handle large and complex datasets remains a significant challenge. This is particularly true in fields such as genomics and climate science, where the volume and complexity of data are immense. Addressing this problem requires advances in both algorithms and computing infrastructure, as well as interdisciplinary collaboration between machine learning experts anddomain-specific researchers. Furthermore, the issue of ethical considerations in applied machine learning is becoming increasingly important. As machine learning technologies become more pervasive, the potential for misuse and unintended consequences also grows. This includes concerns around privacy, security, and the potential for autonomous systems to make decisions with significant societal impact. It is essential for researchers, practitioners, and policymakers to work together to develop ethical guidelines and frameworks for the responsible development and deployment of machine learning technologies. Another challenge in applied machine learning is the issue of data quality and quantity. In many real-world applications, obtaining labeled data for training machine learning models can be expensive and time-consuming. This is particularly true in domains such as healthcare and education, where obtaining ground truth labels may require significant expertise and resources. Advances in semi-supervised and unsupervised learning, as well as techniques for data augmentation and transfer learning, can help mitigate some of these challenges. However, there remains a need for further research into methods for learning from limited and noisy data. Finally, a key challenge in applied machine learning is the issue of reproducibility and robustness. Many machine learning research findings are difficult to reproduce, and models often fail to generalize to new, unseen data. This can be a significant barrier to the adoption of machine learning in critical applications where reliability is paramount. Addressing this problem requires a greater emphasis on rigorous experimental design, open science practices, and the development of benchmark datasets and evaluation metrics. In conclusion, applied machine learning holds tremendous promise for addressing some of the most pressing challenges facing society today. However, in order to realize this potential, it is essential to address a host of problems and challenges, including bias, interpretability, scalability, ethical considerations, data quality and quantity, and reproducibility and robustness. By working together across disciplines and sectors, researchers, practitioners, and policymakers can help ensure that machine learning technologies are developed and deployed in a responsible and impactful manner.。
Robust Control
Robust ControlRobust control is a critical concept in the field of engineering and automation, playing a crucial role in ensuring the stability and performance of complex systems. It encompasses a range of techniques and methodologies aimed at designing control systems that can effectively handle uncertainties and variations in the system and external disturbances. In this discussion, we will explore the significance of robust control, its applications, challenges, and future prospects from various perspectives. From an engineering standpoint, robust control is indispensable in addressing the inherent uncertainties and variations that existin real-world systems. Traditional control techniques often assume precise knowledge of the system dynamics, which may not hold true in practical scenarios. Robust control techniques, such as H-infinity control and mu-synthesis, offer a systematic framework to design controllers that can accommodate uncertainties and variations, thereby enhancing the stability and performance of the controlled system. This is particularly crucial in high-stakes applications such as aerospace, automotive, and process control, where system failures can have severe consequences. Moreover, robust control plays a vital role in addressing the challenges posed by external disturbances and environmental variations. In many practical systems, disturbances such as wind gusts, temperature fluctuations, or sensor noise can significantly impact the system's performance. Robust control techniques provide a means to analyze the impact of such disturbances and design controllers that can effectively attenuate their effects, ensuring the system's stability and performance under varying operating conditions. From a broader perspective, the significance of robust control extends beyond engineering applications. It has implications in fields such as economics, finance, and ecology, where complex, interconnected systems exhibit uncertainties and variations. The principles of robust control can be adapted to develop strategies for managing economic uncertainties, mitigating financial risks, and preserving ecological stability. This interdisciplinary relevance underscores the far-reaching impact of robust control in addressing complex, real-world challenges. However, the application of robust control is not without its challenges. Designing robust controllers often involves trade-offs between performance,robustness, and complexity. The quest for robustness may lead to overly conservative designs that sacrifice optimal performance, or conversely, overly aggressive designs that fail to guarantee stability in the presence of uncertainties. Balancing these trade-offs requires a deep understanding of system dynamics, robust control techniques, and practical constraints, demanding a high level of expertise from control engineers. Furthermore, the implementation of robust control techniques in practical systems poses challenges related to validation, verification, and real-time performance. Validating the robustness ofa control design across the entire range of possible uncertainties anddisturbances is a daunting task, often requiring advanced simulation and testing capabilities. Moreover, deploying robust control algorithms in real-time systems demands efficient computational techniques and hardware, especially inapplications with stringent timing requirements such as autonomous vehicles and robotics. Looking ahead, the future of robust control holds promising prospects driven by advances in data-driven modeling, machine learning, and cyber-physical systems. Integrating data-driven approaches with traditional robust control techniques can potentially enhance the adaptability and performance of control systems in the face of uncertainties. Moreover, the emergence of cyber-physical systems, enabled by the Internet of Things and advanced sensing technologies, opens new frontiers for robust control applications in interconnected, distributed systems. In conclusion, robust control stands as a cornerstone in the realm of engineering and beyond, offering a systematic approach to address uncertainties, variations, and disturbances in complex systems. While posing challenges in design, validation, and implementation, its significance and potential for future advancements are undeniable. As we continue to push the boundaries oftechnological innovation and tackle increasingly complex systems, the role of robust control will remain paramount in ensuring stability, reliability, and performance.。
无限量地生产稳健的量化交易策略
无限量地生产稳健的量化交易策略：Trading System Lab (TSL)于1985 年创立的美国《期货真相》Futures Truth 杂志，旨在服务全球股票及商品期货交易者，而其根本任务是为读者验证系统交易策略的实盘效果。
分布在全球各地的交易系统开发商，将开发出的策略提交至《期货真相》编辑部，以验证实盘交易成绩。
编辑部根据交易成绩进行排名，并定期公布排名榜单。
在过去的多年里，以TSL-[XXX] 命名的交易系统独占鳌头。
更加令人惊讶的是，TSL 系列交易策略是同一家公司应用同一款量化交易策略设计系统软件设计而成。
TSL 的大部分客户，都是通过《期货真相》的排名榜单而认识、了解TSL，继而购置价值不菲的TSL 产品作为交易生涯中不可或缺的寻优稳健交易策略工具！什么是TSL？Trading System Lab (TSL)是一家位于美国矽谷的高新科技公司，同时也是该公司设计和生产的一款与公司同名的金融量化交易策略设计系统软件。
TSL 的与众不同之处在于它被用作优寻的底层算法引擎——AIM-GP(遗传基因程序二元机器代码自动归纳合成算法Automatic Induction of Machine Code with Genetic Programming)。
遗传基因程序(Genetic Programming, GP)是演化算法(Evolution Algorithm)的其中一种。
研究者们，例如瑞典科学家彼特·诺丁(Peter Nordin)博士和美国科学家约翰·R·科赞(John R. Koza)博士等人工智能学者，在90年代初便开始积极地开发这种算法的理论和应用。
在短短几年之间，多达数百篇计算机学术论文应运而生(1992-98年，200多位学者发表超过800篇相关GP的论文)，都是围绕着遗传基因程序这个题目来做的研究，可见科学家们对它寄予厚望。
沃尔夫冈·班茨哈夫(Wolfgang Banzhaf)博士在2013年发表的论文《遗传基因程序与自然发生现象》(Genetic Programming and Emergence)中更指出，天地万物之中的的自然发生现象(Emergence)在遗传基因程序这种算法本质中客观存在。
流形学习（manifoldlearning）综述
流形学习（manifoldlearning）综述假设数据是均匀采样于⼀个⾼维欧⽒空间中的低维流形，流形学习就是从⾼维采样数据中恢复低维流形结构，即找到⾼维空间中的低维流形，并求出相应的嵌⼊映射，以实现维数约简或者数据可视化。
它是从观测到的现象中去寻找事物的本质，找到产⽣数据的内在规律。
流形学习⽅法是模式识别中的基本⽅法，分为线性流形学习算法和⾮线性流形学习算法，线性⽅法就是传统的⽅法如主成分分析（PCA）和线性判别分析（LDA），⾮线⾏流形学习算法包括等距映射（Isomap），拉普拉斯特征映射（LE）等流形学习是个很⼴泛的概念。
这⾥我主要谈的是⾃从2000年以后形成的流形学习概念和其主要代表⽅法。
⾃从2000年以后，流形学习被认为属于⾮线性降维的⼀个分⽀。
众所周知，引导这⼀领域迅速发展的是2000年Science杂志上的两篇⽂章: Isomap and LLE (Locally Linear Embedding)。
1. 流形学习的基本概念那流形学习是什莫呢？为了好懂，我尽可能应⽤少的数学概念来解释这个东西。
所谓流形（manifold）就是⼀般的⼏何对象的总称。
⽐如⼈，有中国⼈、美国⼈等等；流形就包括各种维数的曲线曲⾯等。
和⼀般的降维分析⼀样，流形学习把⼀组在⾼维空间中的数据在低维空间中重新表⽰。
和以往⽅法不同的是，在流形学习中有⼀个假设，就是所处理的数据采样于⼀个潜在的流形上，或是说对于这组数据存在⼀个潜在的流形。
对于不同的⽅法，对于流形性质的要求各不相同，这也就产⽣了在流形假设下的各种不同性质的假设，⽐如在Laplacian Eigenmaps中要假设这个流形是紧致黎曼流形等。
对于描述流形上的点，我们要⽤坐标，⽽流形上本⾝是没有坐标的，所以为了表⽰流形上的点，必须把流形放⼊外围空间（ambient space）中，那末流形上的点就可以⽤外围空间的坐标来表⽰。
⽐如R^3中的球⾯是个2维的曲⾯，因为球⾯上只有两个⾃由度，但是球⾯上的点⼀般是⽤外围R^3空间中的坐标表⽰的，所以我们看到的R^3中球⾯上的点有3个数来表⽰的。
单目 三维重建 综述
单目三维重建综述英文回答：Monocular 3D Reconstruction: A Comprehensive Review.Introduction.Monocular 3D reconstruction is the task of estimating the 3D structure of a scene from a single 2D image. This is a challenging problem, as the lack of multiple viewpoints makes it difficult to disambiguate depth and 3D relationships. However, monocular 3D reconstruction has a wide range of applications, including robotics, autonomous driving, and augmented reality.Methods.There are a variety of methods for monocular 3D reconstruction. These methods can be broadly divided into two categories:Geometric methods: These methods use geometric constraints to infer the 3D structure of a scene. For example, vanishing points can be used to estimate the location of the camera and the orientation of planes in the scene.Learning-based methods: These methods use machine learning techniques to learn the mapping from a single 2D image to a 3D representation. For example, convolutional neural networks (CNNs) can be trained to predict the depth map of a scene from a single image.Evaluation.The performance of monocular 3D reconstruction methods is typically evaluated using a variety of metrics, including:Accuracy: The accuracy of a method is measured by the mean absolute error (MAE) between the predicted 3Dstructure and the ground truth.Completeness: The completeness of a method is measured by the percentage of the ground truth 3D structure that is correctly predicted.Robustness: The robustness of a method is measured by its ability to handle challenging conditions, such as noise, occlusions, and motion blur.Applications.Monocular 3D reconstruction has a wide range of applications, including:Robotics: Monocular 3D reconstruction can be used to create maps of the environment for robots. This information can be used for planning, navigation, and object manipulation.Autonomous driving: Monocular 3D reconstruction can be used to create depth maps of the road ahead. Thisinformation can be used to detect obstacles, plan safepaths, and control the vehicle's speed.Augmented reality: Monocular 3D reconstruction can be used to create virtual objects that can be placed in the real world. This technology can be used for gaming, education, and training.Conclusion.Monocular 3D reconstruction is a challenging but important problem with a wide range of applications. In recent years, there has been significant progress in this area, thanks to the development of new methods and the availability of large datasets. As this field continues to develop, we can expect to see even more accurate, complete, and robust monocular 3D reconstruction methods.中文回答：单目三维重建，综述。
基于机器学习的电力系统负荷预测模型研究
基于机器学习的电力系统负荷预测模型研究摘要本文旨在探讨基于机器学习的电力系统负荷预测模型。
在电力系统中，负荷预测是一项非常重要的任务。
它可以帮助电力公司更好地规划发电计划、调整负荷分配等，从而提高电力系统的效率和稳定性。
本文主要研究基于机器学习的电力系统负荷预测模型，其中包括数据采集、数据预处理、特征提取、模型训练和预测等步骤。
本文采用了多种机器学习算法，包括线性回归、支持向量机、神经网络和决策树等，通过对这些算法的实验比较，选出最适合电力系统负荷预测的算法，并给出了相应的预测结果和误差分析。
实验结果表明，本文所提出的机器学习模型可以有效地预测电力系统负荷，具有较高的准确性和鲁棒性。
关键词：电力系统，负荷预测，机器学习，特征提取，模型训练，预测结果AbstractThis paper aims to explore the machine learning-based load forecasting model for power systems. In the power system, load forecasting is a very important task. It can help power companies better plan their generation plans, adjust load distribution, and improve the efficiency and stability of the power system. This paper mainly studies the machine learning-based load forecasting model for power systems, including data collection, data preprocessing, feature extraction, model training, and prediction. Multiple machine learning algorithms are used in this paper, including linear regression, support vector machine, neural network, and decision tree. By comparing these algorithms through experiments, the algorithm most suitable for load forecasting in power systems is selected, and corresponding prediction results and error analysis are provided. The experimental results show that the machine learning model proposed in this paper can effectively predict the load of the power system with high accuracy and robustness.Keywords: Power system, load forecasting, machine learning, feature extraction, model training, prediction results第一章引言电力系统是现代社会中不可或缺的一部分，它为人们提供了必要的能源，支撑了经济和社会的发展。
智能科学与技术导论课件第5章
由于空间中的每个点对应着一个向量，因此，每一个示例也可称为一个特征向量。
5.1 机器学习概述
5.1.3 机器学习的工作流程
1.基本术语
通常，令D={x1, x2, …, xm}表示包含了m个示例的数据集，每个示例由d个特征描述（例如上例 中西瓜使用了3个特征），则每个示例xi=(xi1, xi2, xi3, …, xid)是d维样本空间中的一个向量， xij是xi在第j个特征上的取值，d称为样本xi的维数（Dimensionality）。
这组记录的集合称为一个数据集（Data set），其中每条记录是关于一个事件或对象（这里是一 个西瓜）的描述，称为一个示例（Instance）或样本（Sample）。
反映事件或对象在某方面的表现或性质的事项，例如“色泽”、“根蒂”，称为“属性” （Attribute）或“特征”（Feature）；特征的取值，例如“青绿”、“乌黑”，称为“特征值” （Feature value）。特征组合所构成的空间称为“特征空间”、“样本空间”或“输入空间”。
这里关于实例结果的信息称为“标记”或“标签”（Label），例如“好瓜”。拥有标签信息的示 例称为“样例”（Example）。因此，用（xi，yi）表示第i个样例，其中yi∈Y是示例xi的标签，Y是 所有标签的集合，亦称为“标签空间”（Label space）或“输出空间”。
若预测结果是离散值，例如“好瓜”、“坏瓜”，此类学习任务称为“分类”；若预测结果是连 续值，例如西瓜的成熟度是0.95、0.36等，此类学习任务称为“回归”。
机器学习算法通过实例进行训练，从过去的经验中学习，并分析历史数据。因此，当一次又一次地 训练实例时，机器学习能够识别模式，以便对未知（新）实例做出预测。
consistency regularization 出处 -回复
consistency regularization 出处-回复Consistency Regularization [出处]Consistency regularization is a technique used in machine learning to improve generalization performance and reduce overfitting. It was first introduced in the paper titled "Consistency Regularization for Unsupervised Domain Adaptation" by Sajjadi et al. in 2016. Since then, consistency regularization has been widely adopted and expanded upon in various domains, including image classification, natural language processing, and reinforcement learning.To understand consistency regularization, let's start by defining the problem it aims to solve. In supervised learning, we train models to predict labels given input data and corresponding ground truth labels. However, this approach assumes that the training data and test data are drawn from the same distribution. In real-world scenarios, this assumption often does not hold true. The distribution of the test data may differ from the distribution of the training data, leading to poor performance on unseen data.Unsupervised domain adaptation addresses this issue by leveraging labeled data from a source domain to improve performance on the target domain, where labeled data is scarce or unavailable. Consistency regularization is a method used inunsupervised domain adaptation to encourage the model to produce consistent predictions across different views of the same input.Consistency regularization works by leveraging the concept of perturbation. Given an input sample, we can create a perturbed version of it by applying a random transformation, such as adding Gaussian noise or flipping the image horizontally. By perturbing the input, we create multiple views of the same sample.With these different views, we can train our model to produce consistent predictions across them. The idea is that if the model is robust and generalizable, small perturbations should not drastically change its predictions. Therefore, by minimizing the discrepancy between the predictions on original and perturbed inputs, we encourage the model to make consistent predictions.To implement consistency regularization, we typically use a deep neural network as our model. We pass both the original and perturbed inputs through the network and calculate the predictions. We then compare the predictions using a loss function, such as mean squared error or cross-entropy loss. The goal is to minimize this loss, effectively encouraging consistency.In addition to the loss function used for consistency regularization, we also need to consider the trade-off betweenconsistency and accuracy. If we emphasize consistency too much, the model may become overly cautious and produce overly confident but predictable predictions. On the other hand, if we focus too much on accuracy, the model may ignore the perturbations and fail to generalize well to unseen data. Striking the right balance is crucial for successful consistency regularization.Consistency regularization has shown promising results in various domains. In image classification tasks, it has been used to reduce overfitting and improve performance on out-of-distribution data. For natural language processing tasks, consistency regularization has been applied to improve language models' robustness and reduce sensitivity to input variations. In reinforcement learning, it has been used to stabilize training and improve exploration-exploitation trade-offs.In conclusion, consistency regularization is a powerful technique in machine learning that helps improve generalization performance and reduce overfitting. It encourages models to produce consistent predictions across different views of the same input, making them more robust and better able to handle slight variations in the data. With its wide applicability and promising results, consistency regularization has become an important tool invarious domains of machine learning and continues to be an active area of research and development.。
机器学习Project-预测NBA球赛
机器学习ProjectPrediction of NBA games based on Machine Learning MethodsDecember, 2013IntroductionNational Basketball Association (NBA ) is the men's professional basketball league in North America. The influence of NBA trespass its borders and have countless fans around all the world. As the league involves a lot of money and fans,not surprisingly, a lot of studies have been developed trying to predict its results, to simulate winning teams, to analyze player's performance and to assist coaches.Through the years a lot of data and statistics have been collected based on NBA and each day the data become more rich and detailed. Although, even with such rich data available, it is still very complex to analyze and try to predict a game. In order to deal with that complexity and to achieve better predictions rate a lot of Machine Learning methods have been implemented over these data. That is exactly the purpose of this project.The main objective is to achieve a good prediction rate using Machine Learning methods. The prediction will only define the winning team, regardless of the score. As a parameter and as a goal, our prediction rate should be higher than the rate of the very naive majority vote classifier that always looked at all previous games (in the season) between two teams and picked the one with the fewest losses as the winner. Moreover, it would be interesting to discover not only the winner, but also what are the most important features to be one. Finally, as a secondary objective, we will try to classify the position of one player based on his features.Background (NBA format)(nfIormation from )The current league organization divides thirty teams into two conferences of three divisions with five teams each.Eastern Conference Western Conference Atlantic Central Southeast Northwest Pacific Southwest5 teams 5 teams 5 teams 5 teams 5 teams 5 teamsDuring the regular season, each team plays 82 games, 41 at home and 41 away. A team faces opponents in its own division four times a year (16 games). Each team plays six of the teams from the other two divisions in its conference four times (24 games), and the remaining four teams three times (12 games). Finally, each team plays all the teams in the other conference twice apiece (30 games).NBA Playoffs begin in late April,with eight teams in each conference going for the Championship. The three division winners, along with the team with the next best record from the conference are given the top four seeds. The next four teams in terms of record are given the lower four seeds.The playoffs follow a tournament format. Each team plays an opponent in a best-of-seven series, with the first team to win four games advancing into the next round, while the other team is eliminated from the playoffs. In the next round, the successful team plays against another advancing team of the same conference. All but one team in each conference are eliminated from the playoffs.The final playoff round, a best-of-seven series between the victors of both conferences, is known as the NBA Finals, and is held annually in June.MethodologyFirstly, the data-set should be studied, selected and organized. It is important to mention that only the Regular Season data will be used, once not all teams plays in playoffs and also the teams that plays it can changes from one year to another.Although there are a lot of rich data available, a lot of time it is not ready to be used. Sometimes it is desirable to select the most important features or also to reduce its number using methods as Principal Components Analysis. After the data is prepared some analysis will be done in order to select the best inputs for the methods.Secondly, the Machine Learning Methods will be implemented. By looking in some articles cited on the references, it seems interesting to start the process with a linear regression, that represents a simple method that has, so far, achieved a good performance. After tried the linear regression and, hopefully, with a good classification rate another method will be explored trying to achieve a better performance. Comparing two methods the results will be more reliable and it will be easier to detect the most important features and to find possible improvements. During the process, probably many modifications will be necessary, so new data analysis may be necessary and also new data preparation.Obs.: This methodology is a guide, but other methods can be tested and implemented if achieves better results.Data PreparationData ExtractionTo decide the data source many websites were visited: Most part of the websites has a wide range of data that goes from the basic box score even to the player's salary. Some points were essential in deciding the data source: first, the easiness of extracting the data from the website; second, the number of seasons provided by the website and finally the basic prerequisites of any data as reliability and utility.The website presented all these features and was selected as major source. The box scores in the website is organized in tables as shown below:To extract the data, the box scores were copied into spreadsheets of the OpenOffice and, using the tool macro, all the team names were replaced by numbers, the unnecessary columns were deleted, and .txt files were generated with the data. The .txt files generated were loaded in MatLab and used to generate the feature vectors.The data extracted was from only regular seasons from the 2006-2007 season to 2012-2013 season. Just for simplicity, now and then the season will only be referred by the year that it started. (so 2012-2013 season will be referred 2012 season). During the data extraction it was observedsome irregularities: the regular season 2012-2013 has only 1229 games and the regular season 2011-2012 has only 990 games, also some teams change its names through the period of 2006-2013.The website, also contains some League Standings as displayed below:Some of the these standings were used during the project, however they were not extracted directly from the website, but, instead, they were derived from the box scores using MatLab scripts. This approach was easier than developing a code as phyton to extract the information from the website. Anyway, the standings of the website were very useful to verify the correctness of the MatLab scripts comparing some standings samples obtained with the website.Feature Vectors Implementation / Data AnaysisIt is really important to spend some time deciding what are going to be the features vectors (input) of the methods, otherwise the results will be frustrating. This fact is well summarized in the phrase "Garbage in, garbage out" , very common in the field of computer scienceInitially, the most intuitive features were implemented as the Win-Loss percentage of both teams and point differential per game of both teams. Posteriorly some other features as: Visitor Team win-Loss percentage as visitor,Home Team win-Loss percentage at home and win-loss percentage in the previous 8 games for both teams. All these features were analyzed through some charts or making some simple predictions.The table below presents the predictions results obtained using:A: Win-Loss Percentage of both teams in all previous games of the season and choosing the one with highest percentage as the winning team.B: point differential per game of both teams in all previous games of the season and choosing the one with highest point differential as the winning team.C: win-loss percentage of both teams in the previous 8 games and choosing the one with highest percentage as the winning team.D: Visitor Team win-Loss percentage as visitor and Home Team win-Loss percentage at home in all previous games and chosing the one with highest percentage as the winning team.E: Prediction based on the results of previous games between those teams. The team with more wins is predicted as the winner.Observations:-Sometimes, the prediction is impossible because there is no information yet about the teams (very common in the first games of the season) or because the percentages of both teams are equal, or the team have not played 8 games yet. When it occurs, the number of unpredictable games is computed and at the end it is considered that half of them was correctly predicted (according to the probability).-In the prediction C, it was used the percentage of both teams in the previous 8 games. The aim of this feature is analyze the performance of the team in a short period of time. The number 8was selected after analyzing the prediction rate for N numbers of previous games for N from 1 to10.When N=8, it achieved the highest prediction rate.-Another analysis done was to verify how the prediction rate increases as more data from the season is collected. It can be observed in the graph below that in the first seven games theprediction increases much faster.-Through the results above it can be observed that all the features are well related with the predction of the game and are good candidates as input for the methods.-All the features has information of the current season only.None of them uses,for example, the percentage of win-loss of the team in the previous season. It means that the data of previous years are used only to train the methods, but not as a data for the current season.MethodsPrimarily, lets remember our goal: "As a parameter and as a goal, our prediction rate should be higher than the rate of the very naive majority vote classifier that always looked at all previous games (in the season) between two teams and picked the one with the fewest losses as the winner”. Therefore we are going to define our minimum prediction rate. Although, instead of using the the number of losses we are going to use the win-loss percentage, as the number of games played by one team, in a specific day of the season, can differ from the number played by the opponent.This parameter has already been calculated during the data analysis and corresponds to thedata analysis A from the table. So our goal is to have predictions rates better than:Linnear RegressionThe first method implemented was the linnear regression. The method consists in multiply each feature by a weight, make a summation of all values obtained and a bias, and use this final value to do the classification.In our case, if Y > 0, the visitor team is considered winner and if Y < 0 the home team is considered winner. Initially all the features obtained were applied as input of the linnear regression:1.Win-Loss Percentage (Visitor Team)2.Win-Loss Percentage (Home Team)3.Point differential per game (Visitor Team)4.Point differential per game (Home Team)5.Win-loss percentage previous 8 games (Visitor Team)6.Win-loss percentage previous 8 games (Home Team)7.Visitor Team win-Loss percentage as visitor8.Home Team win-Loss percentage at homeTo define the weights of the linear regression the least mean square algorithm was used. The algorithm used has the same structure of the one available in the website of the course. In order to achieve the weights, the parameters of the algorithm (step size and number of iterations) were changed many times but no convergence was achieved. The last attempt was a step size = 0.001 and 2x106iterations. It was decided to reduce the number of features, that would increase the probability of a convergence.Before doing the principal component analysis it was expected that the features were highly correlated, but ,in order to avoid losing information removing features, the Principal Component Analysis was implemented over the feature vector. The eigen values obtained were:The first three Eigen V alues were used. The next values to these ones becomes really small. This leads to a reduction in a three dimensional space. The principal component analysis was applied in the data of all seasons.Using the PCA data from 2006-2011 into the LSM algorithm it converged and the following weights for the linear regression were achieved.Prediction Rate of 66.91%, value that is higher than our goal, 63.98%.Another analysis was made using all the seasons as training vectors and each season separately as testing. This analysis is incorrect from a time perspective as the training data includes future games. Although, as the data is large and the algorithm expects that there is a pattern betweenseasons, this analysis can be give an idea of the prediction rate of the Linear Regression. The resultswere:MaximumLikelihood ClassifierThe second method implemented was the maximum likelihood. The code used was the one provide in the website of the course. Initially, all the feature vectors were used as input, but ,as the results were lower than the very naive majority vote, a source code was implemented to select the best input features.Just to select the best input features (and not to really predict results) all the seasons were used as training vector. The code implemented makes an exhaustive search through all possible combinations of the eight features(for any number of features).For each combination the likelihood was trained and if the classification rate was higher than the best combination found before the information of the new best combination was kept.The best combination achieved includes the following feature vectors:2.Win-Loss Percentage (Home Team)4.Point differential per game (Home Team)5.Win-loss percentage previous 8 games (Visitor Team)7.Visitor Team win-Loss percentage as visitor8.Home Team win-Loss percentage at homeAfter selecting these features, the likelihood was applied for each season using as training data the previous seasons. As a consequence the season 2006 was not computed.Ex: 2007 >> Training data 200620011 >> Training data 2006-2010The results obtained were:MultiLayer Perceptron – Back PropagationThe last method implemented was the multilayer perceptron using the back propagation code available in the website of the course. The first approach to the method was using all the features, but the best classification rates achieved for many configurations was around 59%, which is lower than our goal and also lower than the previous methods. Trying the method with the data obtained from the Principle Component Analysis the result has increase considerably,aorund 67/68%. In order to try to find the best structure possible the code was adapted to run the backpropagation algorithm in different configurations. Each configuration were executed three times,below are the mean prediction rate of them. The training data used was the seasons 2006-2011 andthe testing data the season 2012.The weights of the two best Multilayer Perceptron achieved during this procedure werestored. Applying these MLP to each season the following results were achieved:It can be observed that those MLP has achieved the best prediction rate, and the prediction rate of the MLP 3-15-2 is the same of the linear regression.ResultsThe results obtained in the methods were higher than the goal of the very naive majority vote classifier that was63.98%.The best prediction rate was achieved using the Multilayer Perceptron Method that achieved 68.44% predction rate. The Linear Regression has achieved a performance of 67.89% which was better than the likelihood method that achived a performance of 66.81%.The prediction of the season 2011 generally has lower results, maybe it is related with the fact that this season has had less games (990) than the others (1230). Comparing the results of all methods they were very consistent without any big distortion in the results.Obs.: It is noteworthy that the utilization of the principal component analysis was really important to obtain the MLP results and Linear Regression results.Discussion of resultsWe can observe that all the results obtained, including the ones in data analysis, are between 60-70% prediction rate. If we compare the results obtained using the machine learning methods and the ones obtained in data analysis we can verify that the difference of prediction rate is small. This fact leads us to the question: Are these methods really working?In order to try to answer this question, it is interesting to compare the results obtained in this project with some other people results. The table below is the results obtained by the NBA oracle (link for the article in the references). We can observe that the predictions rates obtained are in the same region and the best result achieved was 0.7009 with the linear regression. Moreover, the prediction rate of experts are also around 70%.The small difference obtained in the prediction rate actually corresponds to a considerable improvement when considering prediction of basketball games. Sports which involves teams, in general, has many variables associated with the game, for example an injured player. Such variables were not take into account in this project when using the methods, although an expert can take it into account. The point is that a prediction rate near experts prediction is actually good.However, it would be much more interesting if the prediction was higher than experts. Possible improvements in order to achieve this goal would be to increase the features with some variables as an injured player, also analyze the player individually and also try to improve the methods using new configurations or a mixture of methods for example. Obviously, all these improvements will be hard to be done and, probably, will not represent a very large improvement.ConclusionThis project was very worthy as it has exhibit the practical part of the machine learning. It has covered the majority part of the steps necessary to use machine learning methods as a important tool in an application: defining a problem (in our case, to predict NBA games); searching for data; studying the data; extracting the data from the source and also the features from the data and finally applying the methods.Some particular parts as the preparation of data needs a lot of attention as small mistakes can jeopardize all the data and, consequently, the methods. Other steps as implementing the methods needs patience to try many configurations. During this project, it became clear that many times the implementation of the methods is empirical as the math behind methods and the “pattern” behind the problem can be very complex.Finally, we can assert that the project has achieved the main purpose that was to consolidate and reinforce the concepts studied in class and also is a good look into the possibilities of the machines learning in many applications.References[1]Matthew Beckler, Hongfei Wang. NBA Oracle[2]Lori Hoffman Maria Joseph. A Multivariate Statistical Analysis of the NBA[3]Osama K. Solieman. Data Mining in Sports:A Research Overview。
MIT实验室人员是怎样做学问的
麻省理工学院人工智能实验室AI Working Paper 316 1988年10月来自MIT人工智能实验室：如何做研究？作者：人工智能实验室全体研究生编辑：David Chapman版本：1.3时间：1988年9月译者：柳泉波北京师范大学信息学院2000级博士生摘要本文的主旨是解释如何做研究。
我们提供的这些建议，对做研究本身（阅读、写作和程序设计），理解研究过程以及开始热爱研究（方法论、选题、选导师和情感因素），都是极具价值的。
Copyright 1987, 1988 作者版权所有备注：人工智能实验室的Working Papers用于内部交流，包含的信息由于过于初步或者过于详细而无法发表。
不像正式论文那样，会列出所有的参考文献。
1. 简介这是什么？并没有什么神丹妙药可以保证在研究中取得成功，本文只是列举了一些可能会有所帮助的非正式意见。
每篇文章度应该如此！目标人群决定了文章内容和行文风格。
目标读者是谁？本文档主要是为MIT人工智能实验室新入学的研究生而写，但对于其他机构的人工智能研究者也很有价值。
即使不是人工智能领域的研究者，也可以从中发现对自己有价值的部分。
如何使用？要精读完本文，太长了一些，最好是采用浏览的方式。
很多人觉得下面的方法很有效：先快速通读一遍，然后选取其中与自己当前研究项目有关的部分仔细研究。
本文档被粗略地分为两部分。
第一部分涉及研究者所需具备的各种技能：阅读，写作和程序设计，等等。
第二部分讨论研究过程本身：研究究竟是怎么回事，如何做研究，如何选题和选导师，如何考虑研究中的情感因素。
很多读者反映，从长远看，第二部分比第一部分更有价值，也更让人感兴趣。
{小节2 如何通过阅读打好AI研究的基础。
列举了重要的AI期刊，并给出了一些阅读的诀窍。
{小节3 如何成为AI研究领域的一员：与相关人员保持联系，他们可以使你保持对研究前沿的跟踪，知道应该读什么材料。
{小节4 学习AI相关领域的知识。
Mechatronics and Control Systems
Mechatronics and Control Systems Mechatronics and control systems are essential components of modern technology, playing a crucial role in various industries, including manufacturing, automotive, aerospace, and robotics. These systems integrate mechanical, electrical, and computer engineering to create innovative solutions for complex problems. However, despite their numerous benefits, they also present a range of challenges and limitations that need to be addressed. One of the primary issues in mechatronics and control systems is the complexity of integrating multiple engineering disciplines. This interdisciplinary nature often leads to communication barriers and conflicts between team members with different expertise. For example, mechanical engineers may prioritize the structural integrity of a system, while electrical engineers focus on power distribution and control, leading to potential conflicts in design priorities. Overcoming these challenges requires effective communication, collaboration, and a deep understanding of each discipline's requirements and constraints. Another significant problem in mechatronics and control systems is the need for continuous innovation to keep up with rapidly evolving technology. As new advancements emerge in areas such as artificial intelligence, machine learning, and sensor technology, engineers must adapt their designs and control algorithms to leverage these developments effectively. This constant need for innovation can be both exciting and daunting, as it requires engineers to stay updated with the latest trends and continuously refine their skills to remain competitive in the field. Moreover, mechatronics and control systems face challenges related to reliability and robustness. In safety-critical applications such as autonomous vehicles or medical devices, any malfunction or error in the control system can have severe consequences. Therefore, ensuring the reliability and robustness of these systems is of utmost importance. This involves rigorous testing, fault-tolerant design, and adherence to strict industrystandards and regulations. Despite these precautions, achieving 100% reliabilityis nearly impossible, and engineers must find a balance between risk and performance in their designs. Furthermore, mechatronics and control systems often encounter limitations in terms of cost and resource constraints. Developingcutting-edge mechatronic systems requires significant investment in research,development, and prototyping. Small and medium-sized enterprises may struggle to allocate sufficient resources to compete with larger corporations, limiting their ability to innovate and bring new products to market. Additionally, costconstraints may lead to compromises in the quality and performance of mechatronic systems, posing a challenge for engineers to deliver high-value solutions within budgetary constraints. Another critical issue in mechatronics and control systems is the ethical considerations surrounding their applications. As these systems become more autonomous and interconnected, concerns about privacy, security, and the potential for misuse or unintended consequences have become increasingly prevalent. For example, autonomous drones raise questions about surveillance and data privacy, while self-driving cars prompt discussions about liability andethical decision-making in the event of accidents. Engineers and policymakers must address these ethical dilemmas to ensure that mechatronic systems are developedand deployed responsibly. In conclusion, mechatronics and control systems offer tremendous potential for innovation and advancement across various industries. However, they also present a myriad of challenges, including interdisciplinary conflicts, the need for continuous innovation, reliability and robustness concerns, cost and resource limitations, and ethical considerations. Addressing these challenges requires a concerted effort from engineers, researchers, policymakers, and industry stakeholders to foster collaboration, promote responsible innovation, and ensure the safe and ethical deployment of mechatronic systems in society.。
语音控制车辆方案英文缩写
Voice-Controlled Vehicle System - An Overview In recent years, voice control technology has gained immense popularity due to its high level of convenience and ease of use. With advancements in machine learning and natural language processing, voice control has become an integral part of many consumer electronics products, including smart home devices and smartphones. Similarly, voice control has also found its way into the automotive industry, where it has shown promise in improving the safety and convenience of driving. In this document, we will take a closer look at the language, technology, and components involved in developing a voice-controlled vehicle system.IntroductionA voice-controlled vehicle system allows a driver or passenger to interact with various features and functions of a vehicle using natural language commands. It typically involves a combination of hardware and software components, including microphones, speakers, signal processing, and natural language understanding (NLU) software. The system can be integrated into a vehicle’s head unit, infotainment system, or steering wheel controls.Overview of TechnologyThe core technology behind a voice-controlled vehicle system is speech recognition, which is the process of converting spoken words into text. This involves several steps, including signal processing, feature extraction, acoustic modeling, and language modeling. The resulting text is then analyzed using natural language processing (NLP) techniques to extract meaning.The accuracy and robustness of the system depend on several factors, including ambient noise, speaker position, accent, and pronunciation. To improve accuracy, many systems use a machine learning approach, where the system is trained on a large dataset of speech samples and their corresponding text transcripts.In addition to speech recognition and NLP, a voice-controlled vehicle system may also include other technologies, such as machine vision, haptic feedback, and gesture recognition. These technologies can complement speech recognition to provide a more comprehensive user experience.Components of a Voice-Controlled Vehicle SystemA voice-controlled vehicle system typically consists of the following components:MicrophonesMicrophones are used to capture the user’s voice and convert it into an electrical signal that can be processed by the system. Multiple microphones can be used to improve the accuracy of speech recognition and to cancel out background noise.SpeakersSpeakers are used to provide audio feedback to the user, such as confirming a command or providing instructions.Signal ProcessingSignal processing is used to filter out background noise, normalize the audio signal, and extract relevant features for speech recognition.Natural Language Understanding (NLU) SoftwareNLU software is used to interpret the meaning of the user’s spoken commands and to generate appropriate responses. This typically involves several steps, such as entity recognition, intent classification, and dialogue management.Vehicle InterfaceThe vehicle interface is the component that enables the voice-controlled system to interact with other functions and features of the vehicle, such as the audio system, climate control, and navigation system.Benefits of Voice-Controlled Vehicle SystemsVoice-controlled vehicle systems offer several benefits over traditional manual controls, including:Hands-free OperationDrivers can control various functions of the vehicle without taking their hands off the steering wheel or eyes off the road, which can improve safety and reduce distractions.Improved AccessibilityVoice control can make it easier for drivers and passengers with disabilities, such as visual impairment, to operate the vehicle.PersonalizationVoice-controlled vehicle systems can learn the user’s preferences and adapt to their habits, providing a more personalized and seamless driving experience.ConclusionVoice control technology has the potential to transform the automotive industry, improving safety, convenience, and accessibility for drivers and passengers. While there are still technical challenges to overcome, such as accuracy and reliability, the future of voice-controlled vehicle systems is promising. As technology continues toadvance, we can expect to see more widespread adoption of this technology and an increasing number of innovative applications.。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Machine learning of plan robustness knowledgeabout instancesSergio Jim´e nez,Fernando Fern´a ndez ,and Daniel BorrajoDepartamento de Inform´a ticaUniversidad Carlos III de MadridAvda.de la Universidad,30.Legan´e s(Madrid).Spainsjimenez@inf.uc3m.es,fernando@,dborrajo@ia.uc3m.esAbstract.Classical planning domain representations assume all the ob-jects from one type are exactly the same.But when solving problems inthe real world systems,the execution of a plan that theoretically solvesa problem,can fail because of not properly capturing the special fea-tures of an object in the initial representation.We propose to capturethis uncertainty about the world with an architecture that integratesplanning,execution and learning.In this paper,we describe the PELAsystem(Planning-Execution-Learning Architecture).This system gen-erates plans,executes those plans in the real world,and automaticallyacquires knowledge about the behaviour of the objects to strengthen theexecution processes in the future.1IntroductionSuppose you have just been engaged as a project manager in an organization. The organization staffconsists of two programmers,A and B.Theoretically A and B can do the same work,but probably they will have different skills.So it would be common sense to evaluate their work in order to assign them tasks according to their worthy.In this example,it seems that the success of fulfilling a task depends on which worker performs which task,that is how actions are instantiated,rather than depending on what state the action is executed.The latter would be,basically, what reinforcement learning does in the case of mdp models(fully instanti-ated states and actions).It does not depend either on the initial characteristics of a given instance,because the values of these characteristics might not be known“a priori”.Otherwise,they could be modelled in the initial state.For instance,one could represent the level of expertise of programmers,as a pred-icate expertise-level(programmer,task,prob)where prob could be a num-ber,reflecting the uncertainty of the task to be carried out successfully by the programmer.Then,the robustness of each plan could be computed by cost-based planners.So,we would like to acquire knowledge only about the uncertainty as-sociated to instantiated actions,without knowing“a priori”the facts from the Fernando Fern´a ndez is currently with the Computer Science Department Carnegie Mellon University,funded by a MEC-Fulbright grant2state that should be true in order to influence the execution of an action.Exam-ples of this type of situations arise in many real world domains,such as project management,workflow domains,robotics,etc.We are currently working in the domain of planning tourist visits.In this domain,we want to propose plans that please the tourist as much as possible,and we have to deal with the uncertainty about which is the best day to visit a place.With an architecture that integrates planning,execution and learning we want to achieve a system that is able to learn some knowledge about the effects of actions execution,but managing a rich representation of the action model.Thus, the architecture can be used forflexible kinds of goals as in deliberative planning, together with knowledge about the expected future reward,as in Reinforcement Learning[1].A similar approach is followed in[2],but they learn the operators (actions models),while our goal is to acquire heuristics(as control knowledge)to guide the planner search towards more robust solutions.In a classical AI setting, our approach tries to separate the domain model that might be common to many different problems within the domain,from the control knowledge that can vary over time.And we propose to gradually and automatically acquire this type of control knowledge through repeated cycles of planning,execution and learning, as it is commonly done in most real world planning situations by humans.2The Planning-Execution-Learning ArchitectureThe aim of the architecture is to automatically acquire knowledge about the objects behaviour in the real world to generate plans whose execution will be more robust.Figure1shows a high level view of the proposed architecture.Fig.1.High level view of the planning-execution-learning architecture.To acquire this knowledge,the system begins with a deterministic knowledge of the world dynamics and observes the effects that the execution of its actions causes in the real world.The system registers whether an action execution is successful or not in the real world.So it has information about the possibility of3 succeed on executing an action in the real world.This is what we call the robust-ness of an action.To use this information,the system defines control knowledge that decides the instantiation of the actions.So,the planner will choose the best bindings for the actions according to the acquired robustness information.We have developed a preliminary prototype of such architecture,that we call PELA (Planning-Execution-Learning Architecture).To make this prototype come true, we have made the following assumptions(we describe how we plan to relax them in the future work section).1.A domain consists of a set of operators or actions,and a set of specificinstances that will be used as parameters of the actions.This is something relatively different from the way in which planning domains are handled, since they usually do not include specific instances,which appear in the planning problems.This assumption is only needed for learning and it is not really needed for deterministic planning purposes.2.As we are working in a preliminary prototype,the robustness of the executionof plan actions only depends on the instantiation of the actions parameters, and it does not depend on the states before applying the actions.2.1PlanningFor the planning task we have used the nonlinear backward chaining planner IPSS[3].The inputs to the planner are the usual ones(domain theory and problem definition),plus declarative control knowledge,described as a set of control rules.These control rules act as domain dependent heuristics.They are the main reason we have used this planner,given that they provide an easy method for declarative representation of automatically acquired knowledge[4]. IPSS planning-reasoning cycle involves as decision points:select a goal from the set of pending goals and subgoals;choose an operator to achieve a particular goal; choose the bindings to instantiate the chosen operator and apply an instantiated operator whose preconditions are satisfied or continue subgoaling on another unsolved goal.The output of the planner,as we have used it in this paper,is a total-ordered plan.2.2ExecutionThe system executes step by step the sequence of actions proposed by the planner to solve a problem.When the execution of a plan step is a failure the execu-tion process is aborted.To test the architecture,we have developed a module that simulates the execution of actions in the real world.This simulator module receives an action and returns whether the execution succeeded or failed.It is very simple for the time being as it doesn’t take care of the current state of the world.The simulator keeps a probability distribution function as a model of execution for each possible action.When the execution of an action has to be simulated,the simulator generates a random value following its corresponding distribution probability.If the generated random value satisfies the model,the action is considered successfully executed.42.3LearningThe process of acquiring the knowledge can be seen as a process of updating the robustness table.This table registers the estimation of success of an instan-tiated action in the real world.It is composed of tuples of the form<op-name, op-params,r-value>op-name is the action name,op-params is the list of the instantiated parameters and r-value is the robustness value.In the planning tourist visits domain,as we want to capture the uncertainty about which is the best day for a tourist to visit afixed place we register the robustness of the operator PREPARE-VISIT with the parameters PLACE and DAY.An example of the robustness-table for this domain is Table1.Action Parameters Robustnessprepare-visit(PRADO MONDAY) 5.0prepare-visit(PRADO TUESDAY) 6.0prepare-visit(PRADO WEDNESDAY)8.0prepare-visit(PRADO THURSDAY) 4.0prepare-visit(PRADO FRIDAY) 2.0prepare-visit(PRADO SATURDAY) 1.0prepare-visit(PRADO SUNDAY) 1.0prepare-visit(ROYAL-PALACE MONDAY) 2.0prepare-visit(ROYAL-PALACE TUESDAY)2.0...Table1.An example of a Robustness-Table for the planning tourist visits domain.We update the robustness value of the actions using the learning algorithm shown in Figure 2.According to this algorithm[5],when the action execution is successful,we increase the robustness of the action,but if the action execution is a failure,the new robustness value is the square root of the old robustness value.Function Learning(ai,r,Rob-Table):Rob-Tableai:executed actionr:execution outcome(failure or success)Rob-Table:Table with the robustness of the actionsif r=successThenrobustness(ai,Rob-Table)=robustness(ai,Rob-Table)+1Elserobustness(ai,Rob-Table)=robustness(ai,Rob−T able)Return Rob-Table;Fig.2.Algorithm that updates the robustness of one action.5 2.4Exploitation of acquired knowledgeControl rules guide the planner among all the possible actions,choosing the action bindings with the greatest robustness value in the Robustness Table.In the planning tourist visits domain,these control rules will make the planner prefer the most’robust’day to prepare the visit of the tourist<user-1>to the place<place-1>.An example of these control rules is shown in Figure3. Suppose that a tourist called Mike wants to visit the Prado museum,the system will decides to prepare the visit to the Prado museum on Wednesday,among all the possible instantiations,because PREPARE-VISIT PRADO WEDNESDAY8.0 is the tuple with the greatest robustness value in the Robustness-Table for the PRADO.To achieve a balance between exploration and exploitation the system only use the control rules in80%of the times.(control-rule prefer-bindings-prepare-visit(IF(and(current-goal(prepared-visit<user-1><place-1>))(current-operator prepare-visit)(true-in-state(current-time<user-1><day-1><time-1>))(true-in-state(current-time<user-1><day-2><time-2>))(diff<day-1><day-2>)(more-robustness-than(list’prepare-visit<place-1><day-1>)(list’prepare-visit<place-1><day-2>))))(THEN prefer bindings((<day>.<day-1>))((<day>.<day-2>))))Fig.3.Control rule for preferring the best day to visit a museum3Experiments and ResultsThe experiments carried out to evaluate the proposed architecture have been performed in the planning tourist visits domain.The used domain is a simpli-fication of the SAMAP project domain[6],but given that this is preliminary work,we have left out the part of path planning.We assume a tourist is always able to move from any zone in the city to another.The operators in this domain are MOVE,VISIT-PLACE and PREPARE-VISIT.In order to test the system we have developed a simulator that emulates the execution of the planned actions in the real world.The simulator decides whether visiting a place is a failure or not.It decides with probability0.1that the visit was successfully in Mondays,Tuesdays, Wednesdays or Thursdays.And with probability0.5when the visit happens on Fridays,Saturdays or Sunday.We have used a test problem set with100random generated problems with different complexity.The state of the random problems represents the free time6of the tourist for each day in the week,its available money and its initial location. The problem goals describe the places the tourist wants to visit.We measure the complexity of the problems in terms of the available time the user has to visit all the goals.For that purpose we have defined the following ratio:complexity=goals-time/available-timeWhere goals-time represents the time needed to visit all the goals and available-time represents the sum of the tourist free time.So,when a problem have a complexity ratio over1.0the planner will not be able tofind a solution.Fig.4.Steps of plan successfully executed in the planning tourist visits domain.Thefirst graph infigure4shows the evolution of the learning process.This graph presents the number of successfully executed actions for25epochs.This number converges quickly to approximately13-14steps.The average length of the plans that solve the problems from the test set is19,5.So,in terms of percentage,13-14steps executed succesfully represents approximately66-72% percentage of plan executed succesfully.The fast convergence is because of failure and success probabilities for an action don’t change with time.The second graph infigure4compares the behaviour of our system to the behaviour of a system that does not use the learned knowledge in the planning process.The number of successful actions is computed after25epochs of learning with a ten random problems train set.4Related WorkLearning to plan and act in uncertain domains is an important kind of machine learning task.Most of literature in thefield separates this task in two different phases:Afirst phase to capture the uncertainty and a second phase to plan dealing with it.1.Afirst phase when the uncertainty is captured,[2]propose to obtain theworld dynamics by learning from examples representing action models as probabilistic relational rules.A similar approach was previously used in7 propositional logic in[7].[8]proposes using Adaptive Dynamic Program-ming,this technique allows reinforcement learning agents to build the tran-sition model of an unknown environment whereas the agent is solving the Markov Decission Process through exploring the transitions.2.A second phase when problems are solved using planners able to handleactions with probabilistic effects.This kind of Planning is a well studied problem[9].We can also include in this second phase the systems that solve Markov Decision Processes.The standard Markov Decission Process algo-rithms seek a policy(a function to choose an action for every possible state) that guarantees the maximum expected utility.So,once the optimal policy is found planning under uncertainty can be considered as following the policy starting from the initial state[10].So,as our system propose the integration of these two phases,it presents several differences with the previous systems:–Our system does not learn a probabilistic action model,the system starts with a deterministic description of the actions.Then it explores the envi-ronment not to learn the whole world dynamics but to complete the domain theory.–We don’t assume completely the object abstraction as we are interested in domains where the execution of an action depends on the identity of the instances rather than on their type.–Our system uses the learnt information about instances as control knowledge so it keeps separately the domain model from the control knowledge.We have also found another architecture that integrates planning,executing and learning to in a similar way.[11]interleaves high-level task planning with real world robot execution and learns situation-dependent control rules from selecting goals to allow the planner to predict and avoid failures.The main differences between this architecture and ours,are that:we don’t learn control rules,control rules are part of the initial domain representation,what we learn is the robustness of the actions.And we don’t guide the planner choosing the goals but choosing the instantiations of the actions.5Future workWe plan to remove,when possible,the initial assumptions,mentioned in the in-troduction section.Relaxing thefirst assumption requires generating robustness knowledge with generalized instances and then mapping new problems instances to those used in the acquired knowledge.As we described in the introduction section,we believe this is not really needed in many domains,since one always has the same instances in all problems of the same domain.In that case,we have to assure that there is a unique mapping between real world instances and instance names in all problems.When new instances appear,their robustness values can be initialized to a specific value,and then gradually be updated with8the proposed learning mechanism.To relax the second assumption,we will use a more complex simulator that considers not only the instantiated action,but also the state before applying each action.We are planning to test the system with the simulator and the domains of the probabilistic track of the Interna-tional Planning Competition1.Thus,during learning,the reinforcement formula should also consider the state where it was executed.One could use standard re-inforcement learning techniques[12]for that purpose,but states in deliberative planning are represented as predicate logic formulae.One solution would consist on using relational reinforcement learning techniques[13].Andfinally,for the time being the learning algorithm and the exploration-exploitation strategy we use are very simple,both of them must be studied deeper[14]in order to obtain better results in more realistic domains. References1.Kaelbling,L.P.,Littman,M.L.,Moore,A.W.:Reinforcement learning:A survey.Journal of Artificial Intelligence Research4(1996)237–2852.Pasula,H.,Zettlemoyer,L.,Kaelbling,L.:Learning probabilistic relational plan-ning rules.In:Proceedings of the Fourteenth International Conference on Auto-mated Planning and Scheduling.(2004)3.Rodrguez-Moreno,M.D.,Borrajo,D.,Oddi,A.,Cesta,A.,Meziat,D.:Ipss:Aproblem solver that integrates planning and scheduling.Third Italian Workshop on Planning and Scheduling(2004)4.Veloso,M.,Carbonell,J.,P´e rez,A.,Borrajo,D.,Fink,E.,Blythe,J.:Integratingplanning and learning:The prodigy architecture.Journal of Experimental and Theoretical AI7(1995)81–1205.Nareyek,A.:Choosing search heuristics by non-stationary reinforcement learning(2003)6.Fern´a ndez,S.,Sebasti´a,L.,Fdez-Olivares,J.:Planning tourist visits adapted touser preferences.Workshop on Planning and Scheduling.ECAI(2004)7.Garca-Martnez,R.,Borrajo,D.:An integrated approach of learning,planning,and execution.Journal of Intelligent and Robotic Systems29(2000)47–788.Barto,A.,Bradtke,S.,Singh,S.:Real-time learning and control using asyn-chronous dynamic programming.Technical Report,Department of Computer Sci-ence,University of Massachusetts,Amherst(1991)91–579.Blythe,J.:Decision-theoretic planning.AI Magazine,Summer(1999)10.Koening,S.:Optimal probabilistic and decision-theoretic planning using marko-vian decision theory.Master’s Report,Computer Science Division University of California,Berkeley(1991)11.Haigh,K.Z.,Veloso,M.M.:Planning,execution and learning in a robotic agent.In:AIPS.(1998)120–12712.Watkins,C.J.C.H.,Dayan,P.:Technical note:Q-learning.Machine Learning8(1992)279–29213.Dzeroski,S.,Raedt,L.D.,Driessens,K.:Relational reinforcement learning.Ma-chine Learning43(2001)7–5214.Thrun,S.:Efficient exploration in reinforcement learning.Technical Report C,I-CS-92-102,Carnegie Mellon University(1992)1/。