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.
Classical planning – algorithms for classical planning – heuristics for planning – hierarchical planning – non-deterministic domains – time, schedule, and resources – analysis – Knowledge Representation
UNCERTAIN KNOWLEDGE AND REASONING
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. Case Study: Chat bot System
Reference Book:
1 G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem solving”, Sixth Edition, Pearson Education, 2008.
2 Elaine Rich, Kevin Knight, “Artificial Intelligence”, Third Edition, Tata McGraw Hill, 2009
3 Anindita Das, “Artificial Intelligence & Soft Computing for Beginners”, First Edition, Shroff Publishers & Distributors Pvt Ltd, 2013.
4 Wolfgang Ertel, “Introduction to Artificial Intelligence”, 1st Edition, Springer, 2017.
5 David L. Poole and Alan K. Mackworth, “Artificial Intelligence: Foundations of Computational Agents”, 2nd Edition, Cambridge University Press, 2010.
Text Book:
S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Third Edition, Pearson Education, 2010
