Unit-1
Artificial intelligence-introduction ,foundations, history
Intelligent agents-introduction,structure,environments
Solving problems by searching-problem solving agents, formulating problems searching for solutions, search strategies, avoiding repetitions, constraint satisfaction
Informed search methods-best-first heuristic functions, Memory Boundary Search, Iterative Improvement.



Unit-II

Agents that reason logically – Knowledge based agent, Representation, reasoning and logic, Prepositional logic.
First-Order Logic – Syntax and semantics, Extensions and Notational variations, Using first-order logic, Simple reflex agents, representing change, deducing hidden properties, references.

Inference in first-order logic – Generalized Modus Ponens, Forward
And Backward chaining, completeness, resolution

Unit – III

Languages for AI – LISP, PROLOG

Unit – IV
Uncertain Knowledge and reasoning – Uncertainty, Basic Probability notations, Axioms of probabilities, Baye’s rule and its use
Probabilistic reasoning systems - Representation , Belief Networks – Semantics, Inference, multiply connected belief networks, knowledge engineering, other approaches to uncertain reasoning
Making simple decisions – Basis of utility theory, Utility functions, Multi attribute utility functions, decision networks, value of information, decision – theoretic expert systems Making complex decisions – Sequential decision problems, value iteration, policy iteration, decision theoretic agent design, dynamic networks.

Unit – V

Learning from observations – General model of learning agents, inductive learning, learning decision trees, learning general logical descriptions, computational learning theory.

Learning in neural and belief networks – Neural networks, perceptrons, multi-layer feed forward, Applications, Bayesian methods for learning belief networks
Reinforcement learning – Passive learning, Active learning, exploration, Action-value function, Generalization in reinforcement learning
Knowledge in learning – examples, Explanation-based learning, Learning using relevance information, Inductive logic programming

Suggested Readings :
1. Stuart Russell, Peter Novig, Artificial Intelligence – a modern approach, PH1995
2. George F Lugar - Artificial Intelligence – Structures and strategies for complex problem solving, 4th Edition, Pearson 2002

References :
Elaine Ritchie - Artificial Intelligence – 2nd Edition, McGraw 1993