1. What in Artificial Intelligence:
The AI Problems, The underlying assumption, What is an AI technique.
2. Problems, Problem Spaces & Search:
Defining the problem as a state space search, Production system, and Problem characteristics.
3. Heuristics Search Techniques:
Generate and Test, Hill climbing, Best First Search, Problem Reduction, Constraint Satisfaction, Means-Ends Analysis.
4. Knowledge Representation Issues:
Representation and Mappings, Approaches to knowledge Representation, Issues in Knowledge representation.
5. Using Predicate Logic:
Representing simple facts in logic, Representing Instance and Isa relationships, Computable functions and predicates, Resolution.
6. Representing Knowledge Using Rules:
Procedural versus Declarative Knowledge, logic Programming, Forward versus Backward Reasoning, Matching.
7. Game Playing:
Overview, The Mimi ax Search Procedure, Adding Alpha-Beta cutoffs, Additional refinements, iterative deepening,
Overview, an example Domain: The Blocks World, Components of a planning system, Goal stack planning,
What is Understanding, What makes Understanding hard, Understanding as constraint satisfaction.
10. Natural Language Processing:
Introduction, Syntactic Processing, Semantic Analysis, Discourse and Pragmatic Processing.
11. Expert Systems:
Representing and using domain knowledge, Expert system shells explanation, Knowledge Acquisition.
12. AI Programming Language: