1. What is AI ? : The AI Problems, The Underlying Assumption, What Is An AI Techniques, The Level Of The Model, Criteria For Success, Some General References, One Final Word. (Chapter - 1) 2. Problems, State Space Search & Heuristic Search Techniques : Defining The Problems As A State Space Search, Production Systems, Production Characteristics, Production System Characteristics, And Issues In The Design Of Search Programs, Additional Problems. Generate-And-Test, Hill Climbing, Best-First Search, Problem Reduction, Constraint Satisfaction, Means-Ends Analysis. (Chapters - 2, 3) 3. Knowledge Representation Issues : Representations And Mappings, Approaches To Knowledge Representation. (Chapter - 4) 4. Using Predicate Logic : Representation Simple Facts In Logic, Representing Instance And Isa Relationships, Computable Functions And Predicates, Resolution. (Chapter - 5) 5. Representing Knowledge Using Rules : Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning. (Chapter - 6) 6. Symbolic Reasoning Under Uncertainty : Introduction To Non-monotonic Reasoning, Logics For Non-monotonic Reasoning. (Chapter - 7) 7. Statistical Reasoning : Probability And Bays’ Theorem, Certainty Factors And Rule-Base Systems, Bayesian Networks, Dempster-Shafer Theory, Fuzzy Logic. (Chapter - 8) 8. Weak Slot-and-Filler Structures : Semantic Nets, Frames. (Chapter - 9) 9. Strong Slot-and-Filler Structures : Conceptual Dependency, Scripts, CYC (Chapter - 10) 10. Game Playing: Overview, And Example Domain : Overview, MiniMax, Alpha-Beta Cut-off, Refinements, Iterative deepening, The Blocks World, Components Of A Planning System, Goal Stack Planning, Nonlinear Planning Using Constraint Posting, Hierarchical Planning, Reactive Systems, Other Planning Techniques. (Chapter - 11) 11. Understanding : What is understanding ? , What makes it hard?, As constraint satisfaction (Chapter - 12) 12. Natural Language Processing : Introduction, Syntactic Processing, Semantic Analysis, Discourse And Pragmatic Processing, Spell Checking (Chapter - 12) 13. Connectionist Models : Introduction: Hopfield Network, Learning In Neural Network, Application Of Neural Networks, Recurrent Networks, Distributed Representations, Connectionist AI And Symbolic AI. (Chapter - 13) 14. Introduction to Prolog : Introduction To Prolog: Syntax and Numeric Function, Basic List Manipulation Functions In Prolog, Functions, Predicates and Conditional, Input, Output and Local Variables, Iteration and Recursion, Property Lists and Arrays, Miscellaneous Topics, LISP and Other AI Programming Languages. (Chapter - 14)
