introductions – AI problems – Problem Characteristics –Agents – Structure of an agent –Problem formulation– uninformed search strategies– heuristics –informed search strategies–constraint satisfaction.

Logical agents – propositional logic – inferences – first-order logic – inferences in first order logic – propositional Vs. first order inference –unification &lifts –forward chaining–backward chaining– resolution.

Planning with state-space search–partial-order planning–planning graphs–planning and acting in the real world.

Uncertainty – review of probability - probabilistic Reasoning – Semantic networks –Bayesian networks – inferences in Bayesian networks– Temporal models – HiddenMarkovmodels.

Learning from observation–Inductive learning–Decision trees–Explanation based learning – Statistical Learning methods –Reinforcement Learning– Neural net learning &Geneticlearning. Case Study: Security in AI - Home Security, Crime prevention Camera, Military Reconnaissance, Offshore oil & Gas threat detection