Review

PSU CS441/541 Lecture 10
November 27, 2000

• Final Review
  • Lecture 1: Introduction
    • Conceptually, What Is AI?
    • Operationally, What Is AI?
    • What ``Works'' In AI?
    • The Pragmatist's Approach To AI
    • Specific Outstanding Hard Problems
  • Lecture 2: AI Problem Representation
    • Logical Problem Representation
    • Logic
    • Predicate Logic and Horn Databases
    • Cool, But What Happened To AI?
  • Lecture 3: Single Agent Search
    • Consider Standard Examples
    • What Is The Common Ground?
    • Blind Search
    • Heuristic Search
    • Nogoods
    • Local Search
  • Lecture 4: First-Order Logic
    • Quantifiers: the general case
    • Logic databases
    • Search and search control
  • Lecture 5: Adversary Search
    • The Domain: Games
    • Minimax
    • DFS In Minimax Game Trees
    • Practical Considerations
  • Lecture 6: Advanced KR---Three Topics
    • From Boolean Logic To General Formal Systems
    • Default Logics and Non-Monotonic Reasoning
    • Probabilistic Logic
    • Constraint Satisfaction
  • Lecture 7: Planning
    • Planning and Action
    • Planning: finding sequence of actions taking initial situation to goal situation
    • Situation calculus and theorem proving
    • STRIPS and the Blocks World
  • Lecture 8: Machine Learning
    • Machine Learning
    • ML and Systems
    • Modes Of Learning
    • Evaluating Learning: PAC
    • Basic ML Techniques
    • ML Example: Neural Net Backgammon
  • Lecture 9: Natural Language
    • Why AI?
    • Two facets: recognition and synthesis
    • levels of recognition