mca Syllabus

Artificial Intelligence
IT 803D
Contact: 3L
Credit: 3
Allotted Hrs: 39L
Introduction [2L] Overview of AI, Problems of AI, AI techniques; Problem Solving - Problem space and search, Defining the problem as state space search, Problem characteristics; Tic-Tac-Toe problem.
AI languages [4L] Basic knowledge of programming languages like Prolog and Lisp.
Basic Search Techniques [4L] Solving problems by searching; Uniform search strategies: breadth first search, depth first search, depth limited search, bidirectional search, comparing search strategies in terms of complexity.
Special Search Techniques [6L] : Heuristic Search- greedy best-first search, A* search; Hill climbing search, Simulated annealing search; Genetic algorithms; Constraint satisfaction problems; Adversarial search - Games, Optimal decisions and strategies in games, Minimax search, Alpha-beta pruning.
Symbolic Logic [5L] Syntax and semantics for propositional logic, Syntax and semantics of FOPL, Properties of WFF, Clausal form, Unification, Resolution.
Reasoning Under Inconsistencies and Uncertainties [3L] Non-monotonic reasoning, Truth maintenance systems, Default reasoning & closed world assumption, Predicate completion and circumscription, Fuzzy logic.
Probabilistic Reasoning [3L] Bayesian probabilistic inference, Representation of knowledge in uncertain domain, Semantics of Bayesian networks, Dempster-Shafer theory.
Structured Knowledge [4L] Associative networks, Conceptual graphs, Frame structures.
Expert Systems [4L] Rule based systems, Nonproduction systems: decision tree architectures, blackboard system architectures, neural network architectures.
Learning [4L] Types of learning, general learning model, Learning by induction: generalization, specialization; example of inductive learner.

Books:
1. Artificial Intelligence, Ritch & Knight, TMH
2. Introduction to AI & Expert Systems, Patterson, PHI
3. Artificial Intelligence: A Modern Approach, Russel and Norvig, PE
4. Logic & Prolog Programming, Saroj Kaushik, New Age