Module1:

Introduction- What is intelligence? Foundations of artificial intelligence (AI). History of AI;

Problem Solving- Formulating problems, problem types, states and operators, state space, search strategies.

Module2:

Informed Search Strategies- Best first search, A* algorithm, heuristic functions, Iterative deepening

A*(IDA), small memory A*(SMA); Game playing - Perfect decision game, imperfect decision game,

evaluation function, alpha-beta pruning

Module3

: Reasoning-Representation, Inference, Propositional Logic, predicate logic (first order logic), logical

reasoning, forward chaining, backward chaining; AI languages and tools - Lisp, Prolog, CLIPS

Module4:

Planning- Basic representation of plans, partial order planning, planning in the blocks world,

heirarchical planning, conditional planning, representation of resource constraints, measures, temporal

constraints

Module5:

Uncertainty - Basic probability, Bayes rule, Belief networks, Default reasoning, Fuzzy sets and

fuzzy logic; Decision making- Utility theory, utility functions, Decisiontheoretic expert systems.

Module 6

: Inductive learning - decision trees, rule based learning, current-best-hypothesis search, leastcommitment

search , neural networks, reinforcement learning, genetic algorithms; Other learning methods -

neural networks, reinforcement learning, genetic algorithms.

Module7:

Communication - Communication among agents, natural language processing, formal grammar,

parsing, grammar