英文翻译人工智能

【PT】[J].

【AU:】shambour,Qusai

Xu, Yisi

Lin, Qing

Zhang, Guangquan

【AB】The web provides excellent opportunities to businesses in various aspects of

development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking one-to-one e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users,particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new cold start item problems.

【题目】：基于Web的个性化推荐系统使用的业务合作伙伴---模糊语义技术

【刊登杂志】: 计算智能

【摘要】网站为企业在各方面的发展提供了极好的机会，例如找到一个在线的业务合作

伙伴。然而，随着网络信息的快速增长，商业用户正在和信息过载做斗争，并且在正确的时间找到正确的信息的难度在不断增加。同时，特别是中小型企业（中小企业），在当前竞争激烈的市场中从政府寻求的是一对一的电子服务。怎么为企业用户提供他们需要的的信息和服务，而不是一种未分化的海量信息？本文中就为个性化服务发展提出了一个有效的解决方法。推荐系统是实施个性化的全方位服务的一种有效的方法，近年来在电子商务中得到了广泛的提及。相应的，本文首先提出了一种混合模糊语义推荐（HFSR）的方法，这种方法结合了基于项目的模糊语义相似度和基于项目的模糊协同过滤（CF）相似的技术。本文就介绍了在一个智能推荐系统原型中该方法的实现，这个实现方法称为智能bizseeker，它可推荐相关个人商务用户的业务合作伙伴，特别是对中小企业。实验结果表明，HFSR方法可以帮助克服基于推荐的经典CF语义的限制方法，即稀疏性和冷开始新项目问题。

【PT】[J].

【AU】AU Amigoni, Francesco

Continanza, Luca

【题目】：基于网格的方法解决多智能体系统中招聘问题

【刊登杂志】: 计算智能

【摘要】多智能体系统构成的分布式计算和人工智能之间的交叉口的一个独立的课题。作为算法的技术和多智能体系统的应用已在过去的二十年中持续发展，达到显著的成熟阶段后，许多方法上的问题已经解决了。本文中我们的目的是通过考虑选择或招聘的问题来帮助该方法的评估，多代理系统代理的一个子集，从一组可用的代理来满足特定的要求。这个遇到的问题称之为补充的问题，比如在匹配和任务分配中。我们提出并研究招聘问题的一个新的正式的方法，基于网格的代数形式主义的方法。由此产生的正式框架可以支持自动招募算法的发展。

【PT】[S].

【AU】Zhao, Qiangfu

BE Madani, K

Dourado, A

Rosa, A

Filipe, J

【摘要】Artificial intelligence (AI) has been a dream of researchers for decades.

In 1982, Japan launched the 5th generation computer project,expecting to create AI in computers, but failed. Noting that logic approach alone is not enough, soft computing (e.g. neuro-computing,fuzzy logic and evolutionary computation) has attracted great attention since 1990s. After another 2 decades, however, we have not got any system that is as intelligent as a human, in the sense of "over-all performance". Instead of trying to create intelligence directly, we may try to create "awareness" first, and obtain intelligence "step-by-step".Briefly speaking, awareness is a mechanism for detecting any event which may or may not lead to complete understanding. Depending on the complexity of the events to detect, aware systems can be divided into many levels. Although low level aware systems may not be clever enough to provide understandable knowledge about an observation;they may provide important information for high level aware systems to make understandable decisions. In this paper we do not intend to provide a survey of existing results related to awareness computing. Rather, we will study this field from a new perspective, try to clarify some related terminologies, and propose some problems to solve for creating intelligence through computational awareness.

【题目】：计算意识：另一种方式走向智能化