Syllabus of GTU Artificial Intelligence 8th Sem IT

Syllabus of GTU Artificial Intelligence 8th Sem IT







1. Problems And State Space Search

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.


2. Problems And State Space Search

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.


3. Heuristic Search Techniques

Generate-And-Test, Hill Climbing, Best-First Search, Problem Reduction, Constraint

Satisfaction, Means-Ends Analysis.


4. Knowledge Representation Issues

Representations And Mappings, Approaches To Knowledge Representation.


5. Using Predicate Logic :

Representation 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.


7. Symbolic Reasoning Under Uncertainty

Introduction To Non-monotonic Reasoning, Logics For Non-monotonic Reasoning.


8. Statistical Reasoning :

Probability And Bays’ Theorem, Certainty Factors And Rule-Base Systems, Bayesian

Networks, Dempster-Shafer Theory, Fuzzy Logic.


9. Weak Slot-And-Filler Structure :

Semantic Nets, Frames.


10. Game Playing: Overview, And Example Domain

The Blocks World, Components Of A Planning System, Goal Stack Planning,

Nonlinear Planning Using Constraint Posting, Hierarchical Planning, Reactive

Systems, Other Planning Techniques.


11. Natural Language Processing

Introduction, Syntactic Processing, Semantic Analysis, Semantic Analysis, Discourse

And Pragmatic Processing.


12. Connectionist Models

Introduction: Hopfield Networld, Learning In Neural Networld, Application Of Neural

Networks, Recurrent Networks, Distributed Representations, Connectionist AI And

Symbolic AI.


13. Expert Systems

An Introduction To Expert System, Explanation Facilities, Expert System

Developments Process, knowledge Acquisition.


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.



1 “Artificial Intelligence” -By Elaine Rich And Kevin Knight (2nd Edition) Tata Mcgraw-Hill

2 Introduction to Prolog Programming By Carl Townsend.



1. “Artificial Intelligence And Expert System, Development” -By D.W.Rolston Mcgraw-Hill International Edition.

2. “Artificial Intelligence And Expert Systems ” -By D.W.Patterson

3. “PROLOG Programming For Artificial Intelligence” -By Ivan Bratko( Addison-Wesley)

4. “Programming with PROLOG” –By Klocksin and Mellish.

5. “Artificial Intelligence” (Fifth Edition) –By George F Luger, Pearson Education.

6. “Artificial Intelligence” (Second Edition)–By Stuart Russell and Peter Norvig, Pearson Education.

7. Artificial Intelligence Application Programming, Tim Jones, Wiley India

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