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Introduction to AI – Agents and Environments – concept of rationality – nature of environments – structure of agents. Problem solving agents – search algorithms – uninformed search strategies.
Heuristic search strategies – heuristic functions. Local search and optimization problems – local search in continuous space – search with non-deterministic actions – search in partially observable environments – online search agents and unknown environments
Game theory – optimal decisions in games – alpha-beta search – monte-carlo tree search – stochastic games – partially observable games. Constraint satisfaction problems – constraint propagation – backtracking search for CSP – local search for CSP – structure of CSP.
Knowledge-based agents – propositional logic – propositional theorem proving – propositional model checking – agents based on propositional logic. First-order logic – syntax and semantics – knowledge representation and engineering – inferences in first-order logic – forward chaining – backward chaining – resolution.
Acting under uncertainty – Bayesian inference – naïve Bayes models. Probabilistic reasoning – Bayesian networks – exact inference in BN – approximate inference in BN – causal networks.
Reference Book:
1. Dan W. Patterson, “Introduction to AI and ESâ€, Pearson Education,2007 2. Kevin Night, Elaine Rich, and Nair B., “Artificial Intelligenceâ€, McGraw Hill, 2008 3. Patrick H. Winston, "Artificial Intelligence", Third Edition, Pearson Education, 2006 4. Deepak Khemani, “Artificial Intelligenceâ€, Tata McGraw Hill Education, 2013. 5. http://nptel.ac.in/
Text Book:
1. Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approachâ€, Fourth Edition, Pearson Education, 2021.