人工智能与神经网络笔记.ppt
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Chapter 1 Introduction
Section 3 Applications of Intelligence Theory
Intelligent Communication Networks Intelligent Computer Intelligent Control Intelligent Information Services on Internet
Why is IT related to IS ?
Information is the raw material on the world Knowledge is a special product of information processing Intelligence is the activation of knowledge (information)
Lecture Notes on AI & NN
Chapter 1 Introduction to Intelligence Theory
Section 2 Intelligence Theory & Information Science
Basic Definitions
Information: The 来自百度文库tate, the way the state changes
3. How do you think of the relationship between intelligence theory and information science ?
Lecture Notes on AI & NN
Chapter 2 Mathematical Preparation
Knowledge: The states, the rule the states change
Intelligence: The ability to obtain the related information to process the information for knowledge to produce the intelligent strategy for solving the problem under the environment and achieving the goal.
Box Table A Table B
Situation Representation
1) AT (ROBOT, ALCOVE) EMPTYHANDED (ROBOT) ON (BOX, A) TABLE (A) TABLE (B)
2) AT (ROBOT, A) HOLDS (ROBOT, BOX) TABLE (A) TABLE (B)
Information Transferring
Information Knowledge Intelligent Strategy
Information Acquisition
Problem in Real World
Information Execution
Lecture Notes on AI & NN
HomeWorks:
1. Distinguish the following concept: information, knowledge, intelligence.
2. List possible applications of intelligence theory in your own field.
Lecture Notes on AI & NN
Chapter 4 Information Representation
A General Introduction
Objectives: 1. Make the information convenient to use 2. Make it recognizable to machines
Section 1 Logic Approach
Both propositional and predicate logic can be used. The latter is however more powerful than the former.
Examples: The Alcove
Robot
Operation Representation
Information
Knowledge
Intelligence
Information Refining
Knowledge Activation
Why is IT related to IS ?
Goal
Information Transferring
Information Proc. & Regen.
Section 1 Propositional Logic Section 2 Predicate Logic Section 3 Unification of Expressions Section 4 Conversion of Wff into Clauses Section 5 Formal Language & Automata
Requirements: 1. Powerful in expression 2. Easy to be understood by machine 3. Convenient to be manipulated
The Key Points of Representation: The states and the way of states varying should be suitably represented.
Lecture Notes on AI & NN
Chapter 3 Information Acquisition
Section 1 Section 2 Section 3 Section 4
Manual Approach Pattern Recognition Neural Networks Machine Learning
Section 3 Applications of Intelligence Theory
Intelligent Communication Networks Intelligent Computer Intelligent Control Intelligent Information Services on Internet
Why is IT related to IS ?
Information is the raw material on the world Knowledge is a special product of information processing Intelligence is the activation of knowledge (information)
Lecture Notes on AI & NN
Chapter 1 Introduction to Intelligence Theory
Section 2 Intelligence Theory & Information Science
Basic Definitions
Information: The 来自百度文库tate, the way the state changes
3. How do you think of the relationship between intelligence theory and information science ?
Lecture Notes on AI & NN
Chapter 2 Mathematical Preparation
Knowledge: The states, the rule the states change
Intelligence: The ability to obtain the related information to process the information for knowledge to produce the intelligent strategy for solving the problem under the environment and achieving the goal.
Box Table A Table B
Situation Representation
1) AT (ROBOT, ALCOVE) EMPTYHANDED (ROBOT) ON (BOX, A) TABLE (A) TABLE (B)
2) AT (ROBOT, A) HOLDS (ROBOT, BOX) TABLE (A) TABLE (B)
Information Transferring
Information Knowledge Intelligent Strategy
Information Acquisition
Problem in Real World
Information Execution
Lecture Notes on AI & NN
HomeWorks:
1. Distinguish the following concept: information, knowledge, intelligence.
2. List possible applications of intelligence theory in your own field.
Lecture Notes on AI & NN
Chapter 4 Information Representation
A General Introduction
Objectives: 1. Make the information convenient to use 2. Make it recognizable to machines
Section 1 Logic Approach
Both propositional and predicate logic can be used. The latter is however more powerful than the former.
Examples: The Alcove
Robot
Operation Representation
Information
Knowledge
Intelligence
Information Refining
Knowledge Activation
Why is IT related to IS ?
Goal
Information Transferring
Information Proc. & Regen.
Section 1 Propositional Logic Section 2 Predicate Logic Section 3 Unification of Expressions Section 4 Conversion of Wff into Clauses Section 5 Formal Language & Automata
Requirements: 1. Powerful in expression 2. Easy to be understood by machine 3. Convenient to be manipulated
The Key Points of Representation: The states and the way of states varying should be suitably represented.
Lecture Notes on AI & NN
Chapter 3 Information Acquisition
Section 1 Section 2 Section 3 Section 4
Manual Approach Pattern Recognition Neural Networks Machine Learning