Item Description

What is Artificial Intelligence - A Definition, Underlying Assumption, What is an A.I. Technique, Level of the Model, Criteria for Success, Some General References, One Final Word;

PROBLEM SOLVING - Problems & Problem Spaces -- Defining the Problem as a State Space Search, Problem Characteristics, Production System Characteristics, Additional Problems; Basic Problem-Solving Methods -- Forward versus Backward Reasoning, Problem Trees versus Problem Graphs, Knowledge Representation & the Frame Problem, Matching, Heuristic Functions, Weak Methods, Analyzing Search Algorithms; Game Playing -- Overview, Minimax Search Procedure, Adding Alpha-Beta Cutoffs, Additional Refinements, Limitations of the Method;

KNOWLEDGE REPRESENTATION - Knowledge Representation Using Predicate Logic-- Intro to Representation, Representing Simple Facts in Logic, Augmenting the Representation with Computable Functions & Predicates, Resolution, Natural Deduction; Knowledge Representation Using Other Logics -- Nonmonotonic Reasoning, Statistical & Probabilistic Reasoning;Structural Representations of Knowledge -- Declarative Representations, Procedural Representation;

ADVANCED TOPICS - Advanced Problem-Solving Systems -- Planning, System Organization, Expert Systems; Natural Language Understanding-- Intro, Understanding Single Sentences, Understanding Multiple Sentences, Going the Other Way - Language Generation, Going Both Ways - Machine Translation; Perception - Why is Perception Hard, Techniques Used in Solving Perceptual Problems, Constraint Satisfaction - The Waltz Algorithm; Learning -- What is Learning, Random Learning & Neural Nets, Rote Learning, Learning by Parameter Adjustment, Learning by GPS, Concept Learning, Discovery as Learning -- AM, Learning by Analogy;

Implementing A.I. Systems- Languages & Machines -- A.I. Languages - The Important Characteristics, IPL, LISP, SAIL, PLANNER, KRL, PROLOG, Summary, Computer Architectures for A.I. Applications, Conclusion -- Components of An A.I. Program, more .