Introduction – 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 – Hidden Markov models.

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