Course Information

CS 344: Artificial Intelligence

Basics of problem-solving : problem representation paradigms, state space, satisfiability vs optimality, pattern classification problems, example domains. Search Techniques : Problem size, complexity, approximation and search; depth, breadth and best search; knowledge based problem solving, artificial neural networks. Knowledge representation : First order and non-monotonic logic; rule based, frame and semantic network approaches. Knowledge Acquisition : Learnability theory, approaches to learning. Uncertainty Treatment : formal and empirical approaches including Bayesian theory, belief functions, certainty factors, and fuzzy sets. Detailed Discussion from Example Domains : Industry, Language, Medicine, Verification, Vision, Knowledge Based Systems. Languages and Machines : AI languages and systems, special purpose architectures.

George F.Luger and William A. Stubblefield, AI: Strcutures and Strategies for Complex problem solving, 2nd edition, Benjamin Cummins Publishers, 1997. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall Series in AI, 1995. Mark Stefik, Introduction to Knowledge Systems, Morgan Kaufmann, 1995. Winston P.H., Artificial Intelligence, 3rd edition, Addison Wesley, 1995. E. Rich and K.Knight, Artificial Intelligence, Tata McGraw Hill, 1992. E. Charniack and D. Mcdermott, Artificial Intelligence, Addison Wesley, 1987. N.J.Nilsson, Principles of Artificial Intelligence, Morgan Kaufmann, 1985.
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Duration : Full Semester Total Credit : 6
Type : Theory
Current Semester (Autumn 2017-18)

Status : Not Offered Instructor : ---
Next Semester (Spring 2017-18)

Status : Offered Instructor : Prof. Shivaram Kalyanakrishnan

Last Modified Date: 09-May-2016


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