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Head of the Department Department of Computer Applications & Post Graduate Department of Information Technology Mazharul Uloom College, Ambur – 635 802, Tamil Nadu, INDIA.
Chapter -1 Introduction to Artificial Intelligence
1.1 Introduction 1.2 Foundation of Artificial Intelligence 1.3 History of Artificial Intelligence 1.4 What is Artificial Intelligence (AI)? 1.5 Other Definitions of AI 1.6 Understanding AI 1.7 Approaches to AI 1.7.1 Hard or Strong AI 1.7.2 Soft or Weak AI 1.7.3 Applied AI 1.7.4 Cognitive AI 1.8 General AI Goal 1.8.1 Engineering based AI Goal 1.8.2 Science based AI Goal 1.9 Practical Impact of AI 1.10 Typical AI problems 1.11 Applications of AI 1.11.1 Perception 1.11.1.1 Machine vision 1.11.1.2 Speech understanding 1.11.1.3 Touch ( tactile or haptic ) sensation 1.11.2 Robotics 1.11.3 Natural Language Processing 1.11.3.1 Natural language understanding 1.11.3.2 Natural language generation 1.11.3.3 Machine translation 1.11.4 Planning 1.11.5 Expert Systems 1.11.5.1 Benefits 1.11.6 Machine Learning 1.11.7 Theorem Proving 1.11.8 Symbolic Mathematics 1.11.9 Game Playing 1.12 AI Technique 1.12.1 Tic-Tac-Toe 1.12.1.1 The First Approach (simple) 1.12.1.2 The Second Approach
3.9.1 Control Strategies 3.10 Problem Characteristics 3.10.1 Problem Decomposition 3.10.2 Can Solution Steps be Ignored 3.10.3 Is the Problem Universe Predictable? 3.10.4 Is Good Solution Absolute or Relative? 3.11 Characteristics of Production Systems 3.12 Search Algorithms 3.12.1 Hierarchical Representation of Search Algorithms 3.12.2 Uninformed Search Techniques / Blind Search Strategies / Brue Force / Exhaustive Search 3.12.2.1 Breadth-first search 3.12.2.2 Depth-first search 3.12.2.3 Depth Limited Search 3.12.2.4 Iterative Deepening Search 3.12.3 Heuristic Search Techniques / Informed Search Techniques 3.12.3.1 Generate and Test 3.12.3.2 Hill Climbing / Greedy Local Search 3.12.3.3 Best First Search 3.12.3.4 A* Algorithm 3.12.3.5 Problem Reduction 3.12.3.6 Constraints Satisfaction 3.12.3.7 Means End Analysis 3.13 Characteristics of Heuristic Search 3.14 Uninformed Search Vs Heuristic Search Review Questions
Chapter 4 Knowledge Representation Issues
4.1 Introduction 4.2 Knowledge Representation 4.2.1 Approaches to AI Goals 4.2.2 Fundamental System Issues 4.2.3 Knowledge Progression 4.2.4 Knowledge Model 4.2.5 Knowledge Typology Map 4.3 Representation and Mappings 4.3.1 Framework of Knowledge Representation 4.3.2 Representation of Facts 4.3.3 Using Knowledge 4.4 Approaches to Knowledge Representation 4.4.1 Properties for Knowledge Representation Systems 4.4.1.1 Representational Adequacy 4.4.1.2 Inferential adequacy 4.4.1.3 Inferential efficiency 4.4.1.4 Acquisitional efficiency
4.4.1.5 Well-defined syntax and semantics 4.4.1.6 Naturalness 4.4.1.7 Frame problem 4.4.2 Simple Relational Knowledge 4.4.2.1 Inheritable knowledge 4.4.2.2 Inferential knowledge 4.4.2.3 Procedural knowledge 4.5 Issues in Knowledge Representation 4.5.1 Important Attributes and Relationships 4.5.2 Granularity 4.5.3 Representing Set of Objects 4.5.4 Finding Right Structure Review Questions
Chapter 5 Predicate Logic
5.1 Introduction 5.2 The Basics in Logic 5.2.1 Types of logic 5.2.1.1 Propositional logic 5.2.1.2 Predicate logic 5.3 The Role of Logic 5.4 Predicate Logic or First Order Logic 5.5 Representing Simple Facts in Logic 5.5.1 Logic 5.5.2 Logic as a Knowledge Representation Language 5.5.3 Propositional logic (PL) 5.6 Representing Instance and isa Relationships 5.7 Computable Functions and Predicates 5.8 Natural Deduction Review Questions
Chapter 6 Prepositional and Predicate Logic
6.1 Specific Instructional Objectives 6.2 Introduction 6.3 Logical Operators 6.4 Translating between English and Logic Notation 6.5 Truth Tables 6.6 Complex Truth Tables 6.7 Tautology 6.8 Equivalence 6.9 Propositional Logic 6.10 Predicate Calculus 6.11 First-Order Predicate Logic 6.12 Soundness
Chapter 11 Adversarial Search: Game Playing
11.1 Introduction 11.2 What is Game? 11.3 Definition: Game Playing 11.4 Characteristics of Game Playing 11.5 How to Play A Game in AI? 11.6 Minimax Search Procedure 11.7 Alpha - Beta Cutoffs 11.8 Additional Refinements 11.8.1 The Horizon Effect 11.9 Applications Review Questions
Chapter 12 Planning
12.1 Introduction to Planning 12.2 What is a Plan? 12.3 What is Planning? 12.4 What does planning involve? 12.5 Blocks World Planning Examples 12.5.1 Why Use the Blocks world as an example? 12.6 Planning System Components 12.7 Goal Stack Planning
Chapter 13 Introduction to Machine Learning
13.1 Specific Instructional Objectives 13.2 Introduction 13.3 Training 13.4 Rote Learning 13.5 Learning Concepts 13.6 General-to-Specific Ordering 13.7 A Simple Learning Algorithm 13.8 Version Spaces 13.9 Candidate Elimination 13.10 Inductive Bias 13.11 Decision-Tree Induction 13.12 The Problem of Overfitting 13.13 The Nearest Neighbor Algorithm 13.14 Learning Neural Networks 13.15 Supervised Learning 13.16 Unsupervised Learning 13.17 Reinforcement Learning Review Questions
Chapter 14 Expert System
14.1 Introduction 14.2 Expert Systems 14.3 Architecture of an Expert System 14.4 Capabilities of the Expert Systems 14.5 Characteristics of the Expert Systems 14.6 Limitations of the Expert Systems 14.7 Applications of Expert Systems Review Questions
Appendix A; Model Question Paper - I
