This document provides an overview of artificial intelligence. It covers major topics in AI including problems, techniques, games, theorem proving, natural language processing, vision and speech processing, expert systems, search, abstraction, problem space and search, knowledge representation, representing facts in logic, rule-based systems, semantic networks, frames, learning, and expert systems. The document contains 8 units that describe these fundamental aspects of AI.
2007. Introduction to the panel 'Pragmatic Interfaces' organised by the authors at the International Pragmatics Conference (IPRA) in Goteborg (Sweden), July 2007. Didier Maillat and Louis de Saussure
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
2007. Introduction to the panel 'Pragmatic Interfaces' organised by the authors at the International Pragmatics Conference (IPRA) in Goteborg (Sweden), July 2007. Didier Maillat and Louis de Saussure
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
4. Contents
UNIT 1 WHAT IS ARTIFICIAL INTELLIGENCE 1
Artificial Intelligence: An Introduction
AI Problems
AI Techniques
Games
Theorem Proving
Natural Language Processing
Vision and Speech Processing
Expert System
Search Knowledge
Abstraction
UNIT 2 PROBLEM, PROBLEM SPACE AND SEARCH 21
Defining Problem as a State Space
Production System
Search Space Control Strategy
Breadth First Search and Depth First Search
Problem Characteristics
Heuristic Search Techniques
Generate and Test
Hill Climbing
Best First Search
Branch and Bound
Problem Reduction
Constraints Satisfaction
Means End Analysis
4
5. UNIT 3 KNOWLEGE REPRESENTATION 64
Representation and Mapping
Approaches to Knowledge Representation
The Frame Problem
UNIT 4 REPRESENTING SIMPLE FACTS IN LOGIC 87
Representing Simple Facts in Logic
Representing Instance and is a Relationships
Modus Pones
Resolutions (Skolemizing Queries)
Unification
Dependency Directed Backtracking
UNIT 5 RULE BASED SYSTEMS 125
Procedural Versus Declarative Knowledge
Forward Reasoning
Backward Reasoning
Conflict Resolution
Use of Non Backtrack
UNIT 6 STRUCTURES KNOWLEDGE REPRESENTATION SEMANTIC NET 139
Semantic Nets
Frames
Slots Exceptions
Handling Uncertainties
Probabilistic Reasoning
Use of Certainty Factors
Fuzzy Logic
UNIT 7 LEARNING 178
Concept of Learning
5
6. Learning Automation
Genetic Algorithm
Learning by Induction
Neural Networks
Learning in Neural Networks
Back Propagation Network
UNIT 8 EXPERT SYSTEMS 224
Need and Justification of Expert Systems
Knowledge Acquisition
Case Studies
MYCIN
RI
6
7. UNIT 3 KNOWLEGE REPRESENTATION 64
Representation and Mapping
Approaches to Knowledge Representation
The Frame Problem
UNIT 4 REPRESENTING SIMPLE FACTS IN LOGIC 87
Representing Simple Facts in Logic
Representing Instance and is a Relationships
Modus Pones
Resolutions (Skolemizing Queries)
Unification
Dependency Directed Backtracking
UNIT 5 RULE BASED SYSTEMS 125
Procedural Versus Declarative Knowledge
Forward Reasoning
Backward Reasoning
Conflict Resolution
Use of Non Backtrack
UNIT 6 STRUCTURES KNOWLEDGE REPRESENTATION SEMANTIC NET 139
Semantic Nets
Frames
Slots Exceptions
Handling Uncertainties
Probabilistic Reasoning
Use of Certainty Factors
Fuzzy Logic
UNIT 7 LEARNING 178
Concept of Learning
5