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 – propositional theorem proving – propositional model checking – agents based on propositional logic - inferences – first-order logic – inferences in first order logic – propositional Vs. first order inference – unification &lifts – forward chaining – backward chaining – resolution. Case Study: Wumpus Problem

Classical planning – algorithms for classical planning – heuristics for planning – hierarchical planning – non-deterministic domains – time, schedule, and resources – analysis – Knowledge Representation. Case Study: Weapons selling to hostile nations

Uncertainty – review of probability - probabilistic Reasoning – Semantic networks – Bayesian networks – inferences in Bayesian networks – Temporal models – Hidden Markov models. Case Study: Prediction on Weather

Learning from observation – Inductive learning – Decision trees – Explanation based learning – Statistical Learning methods –Reinforcement Learning. Case Study: Chat bot System