Introduction: 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 : Basic knowledge of programming languages like Prolog and Lisp.
Basic Search Techniques: 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 : 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: Syntax and semantics for propositional logic, Syntax and semantics of FOPL,
Properties of WFF, Clausal form, Unification, Resolution.
Reasoning Under Inconsistencies and Uncertainties: Non-monotonic reasoning, Truth maintenance
systems, Default reasoning & closed world assumption, Predicate completion and circumscription, Fuzzy
logic.
Probabilistic Reasoning: Bayesian probabilistic inference, Representation of knowledge in uncertain
domain, Semantics of Bayesian networks, Dempster-Shafer theory.
Structured Knowledge:Associative networks, Conceptual graphs, Frame structures.
Expert Systems:Rule based systems, Nonproduction systems: decision tree architectures, blackboard
system architectures, neural network architectures.
Learning: Types of learning, general learning model, Learning by induction: generalization,
specialization; example of inductive learner.