Please Note: These notes were generated a long time ago from a LaTeX version of the course, using LaTeX2html. Some of the graphics got lost in the process. If you want a relatively up to date version of the material with images complete you could get the book: Essence of Artificial Intelligence

Contents

• Contents
• Introduction
  • What is Artificial Intelligence?
  • Is AI Possible?
  • Some AI Tasks
  • AI Techniques
    • Knowledge Representation
    • Search
  • About This Course
• Artificial Intelligence Programming in Prolog
  • Artificial Intelligence Programming
    • Support for Symbolic Computation:
    • Support for Exploratory Programming
    • The Main AI Languages
  • The Basics of Prolog
  • Prolog Terms, Backtracking and Unification
    • Basic Data Structures and Syntax
    • More about Prolog Matching
    • Backtracking
    • Declarative and Procedural Views of Programs
    • Some Exercises
  • Recursion
    • Tracing Prolog Execution
      • Exercises:
  • List Processing
    • Exercises
  • Remaining Topics
• Knowledge Representation and Inference
  • Intoduction
  • Structured Objects
    • Semantic Nets
    • Frames
    • Exam-like questions
  • Predicate Logic
    • Review of Propositional Logic
    • Predicate Logic: Syntax
    • Predicate Logic: Semantics
    • Proving Things in Predicate Logic
    • Representing Things in Predicate Logic
    • Logic and Frames
    • Exam-like questions
  • Rule-Based Systems
    • Forward Chaining Systems
    • Backward Chaining Systems
      • Forwards vs Backwards Reasoning
    • Uncertainty in Rules
    • Exam-like questions
  • Advantages and Disadvantages of KR Languages
    • Exam-like questions
• Expert Systems
  • Introduction
  • Designing an Expert System
    • Expert System Architecture
    • Choosing a Problem
    • Knowledge Engineering
    • Exam-like questions
  • Rules and Expert Systems
    • A Simple Example
    • Explanation facilities
    • More Complex Systems
  • An Expert System Shell in Prolog
  • MYCIN: A Quick Case Study
• Using Search in Problem Solving
  • Introduction
    • Graph Theory
  • Basic Search Techniques
    • Simple Search Techniques
      • Depth first vs Breadth first Search
    • Exam-like questions
    • Heuristic Search
      • Hill Climbing
      • Best First Search
      • The A Algorithm
    • Exam-like questions
  • Problem Solving as Search
    • State Space Search
    • Exam-like question
    • Problem Reduction
      • State space search vs problem reduction
    • Problems with Simple Search Techniques: Combinatorial Explosions
    • Planning and Search
• Natural Language Processing
  • Introduction
  • The Problem
  • Syntax
    • Writing a Grammar
    • Grammars in Prolog
    • Parsers
    • Returning the Parse Tree
    • Multiple Parses
  • Semantics and Pragmatics
    • Semantics
    • Pragmatics
  • Generation
• Vision
  • Introduction
  • Edge Detection
    • Exam-like questions
  • Stereopsis
  • Other sources of depth information
  • Line Labelling
  • Towards a 3-D representation
    • Exam-like questions
  • Summary
• Dealing with Uncertainty and Change
  • Fancy Logics
    • Default Logics
    • Belief and Modal Logics
  • Probabalistic Reasoning
    • Probability and Bayes' Theorem
    • Certainty Factors and Rule-Based Systems
• About this document ...