Connected successfully
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.
Reference Book:
1 G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem solvingâ€, Fourth Edition, Pearson Education, 2002. 2 Elaine Rich , Kevin Knight, “Artificial Intelligenceâ€, Third Edition, Tata McGraw Hill, 2009. 3 Anindita Das, “Artificial Intelligence & Soft Computing for Beginnersâ€, First Edition, Shroff Publishers & Distributors Pvt Ltd, 2013. 4 Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approachâ€, Third Edition, Pearson Education, 2009.
Text Book:
1 S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approachâ€, Third Edition, Pearson Education, 2013. 2 David Poole, Alan Mackworth, Randy Goebel, “Computational Intelligence: A Logical Approachâ€, Second Edition, Oxford University Press, 2004.