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Artificial Intelligence (2180703)

Teaching Scheme (in Hours)

Theory Tutorial Practical Total
4 0 2 6

Subject Credit :  6

Examination Scheme (in marks)

PA (M)
ESE Viva (V)
PA (I)
70 30 30 20 150

Syllabus Content    Download

Unit-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.

Unit-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

Unit-3:  Knowledge Representation Issues

Representations And Mappings, Approaches To Knowledge Representation

Unit-4:  Using Predicate Logic

Representation Simple Facts In Logic, Representing Instance And Isa Relationships, Computable Functions And Predicates, Resolution.

Unit-5:  Representing Knowledge Using Rules

Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning

Unit-6:  Symbolic Reasoning Under Uncertainty

Introduction To Nonmonotonic Reasoning, Logics For Non- onotonic Reasoning.

Unit-7:  Statistical Reasoning

Probability And Bays’ Theorem, Certainty Factors And Rule-Base Systems, Bayesian Networks, DempsterShafer Theory, Fuzzy Logic.

Unit-8:  Weak Slot-and-Filler Structures

Semantic Nets, Frames.

Unit-9:  Strong Slot-and-Filler Structures

Conceptual Dependency, Scripts, CYC

Unit-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.

Unit-11:  Understanding

What is understanding? , What makes it hard?, As constraint satisfaction

Unit-12:  Natural Language Processing

Introduction, Syntactic Processing, Semantic Analysis, Semantic Analysis, Discourse And Pragmatic Processing, Spell Checking

Unit-13:  Connectionist Models

Introduction: Hopfield Network, Learning In Neural Network, Application Of Neural Networks, Recurrent Networks, Distributed Representations, Connectionist AI And Symbolic AI

Unit-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.

Reference Books

Sr. Title Author Publication Amazon Link
1 Artificial Intelligence Elaine Rich And Kevin Knight Tata Mcgraw-Hill
2 Artificial Intelligence: A Modern Approach, Stuart Russel, Peter Norvig, PHI
3 Introduction to Prolog Programming Carl Townsend
4 PROLOG Programming For Artificial Intelligence Ivan Bratko
5 Programming with PROLOG Klocksin and Mellish

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