Contents

1. Introduction
• 1.1 Why Study AI?
• 1.2 The Turing Test
• 1.3 Mental and Physical Machines
• 1.4 A Quick Survey
2. Basic LISP Programming
• 2.1 General Features
• 2.2 Atoms
• 2.3 Functional Notation
• 2.3.1 List Diagrams
• 2.3.2 Evaluation-inhibiting Signal
• 2.4 Some Primitive List Operators
• 2.4.1 Shorthand Notation for CAR and CDR combinations
• 2.5 Boolean Values
• 2.6 Equality Tests in LISP
• 2.7 Functions
• 2.7.1 Function Definitions
• 2.7.2 Lambda Expressions
• 2.8 Conditional Expressions (COND's)
• 2.9 Recursive Functions
• 2.10 Dealing with Nested Lists("Trees")
• 2.11 The PROG Feature
• 2.12 The (Run-time) Symbol Table
• 2.13 The Run-Time Stack
• 2.14 Treatment of Locals (Function Entry/Exit)
• 2.15 EVAL
• 2.16 MAPCAR
• 2.17 APPLY
3. Problem Solving and Search
• 3.1 Water Container Problem
• 3.1.1 Control or Search Strategy
• 3.1.2 Production System
• 3.1.3 Standard Terminology
• 3.2 Heuristic Search (Informed Search)
• 3.3 Uninformed (Blind) Search
• 3.3.1 Depth First Search
• 3.3.2 Breadth First Search
• 3.3.3 Heuristic Search
4. Game Playing
• 4.1 Definitions
• 4.2 Game Trees
• 4.3 The Mini-Max Rule
• 4.3.1 Optimal Strategy
• 4.3.2 Grundy's Game
• 4.4 Static Evaluation Function
• 4.5 Alpha-Beta Pruning
5. Knowledge Acquisition
• 5.1 Definiton
• 5.2 Potential Sources
• 5.3 Human Problem Solving
• 5.3.1 Human Information Processing
6. Knowledge Representation
• 6.1 Introduction
• 6.2 Knowledge Types
• 6.3 Knowledge Representation Methods
• 6.3.1 Predicate Calculus
• 6.3.2 Frames
• 6.3.3 Semantic Networks
• 6.3.4 Object-Attribute-Value(O-A-V) Triplets
• 6.3.5 Production Rules
• 6.3.6 Neural Networks
7. Inference and Control
• 7.1 Inference
• 7.1.1 Modus Ponens
• 7.1.2 Modus Tollens
• 7.2 Reasoning About Uncertainty
• 7.3 Forward Chaining
• 7.4 Backward Chaining
• 7.5 Mixed Chaing
8. Uncertainity in Knowledge-based Systems
• 8.1 Bayesian Approach
• 8.2 Dempster-Shafer Theory
• 8.3 Fuzzy Logic
• 8.4 Interval Based Approaches
• 8.5 Logic-based Approaches