project related to ARTIFICIAL INTELLIGENCE

Overview: foundations, scope, problems, and approaches of AI. Intelligent agents: reactive, deliberative, goal-driven, utility-driven, and learning agents. Problem-solving through Search: forward and backward, state-space, blind, heuristic, A, A*, stochastic, and evolutionary search algorithms, sample applications. Knowledge Representation and Reasoning: ontologies, foundations of knowledge representation and reasoning, representing and reasoning about objects, relations, events, actions, time, and space; situation calculus, description logics, reasoning with defaults, reasoning about knowledge, sample applications. (including all knowlegde representation techniques). Planning: Planning as search, partial order planning, Goal Stack Planning, existing expert systems like MYCIN, Expert system shells. Representing and Reasoning with Uncertain Knowledge: probability, connection to logic, independence, Bayes rule, Bayesian networks, probabilistic inference, sample applications. Decision-Making: basics of utility theory, decision theory, sequential decision problems, sample applications Machine Learning and Knowledge Acquisition: Learning nearest neighbor, naive Bayes,Model Evaluation, Introduction to Machine Learning. Languages for AI problem solving: Introduction to PROLOG syntax and data structures, representing objects and relationships, built-in predicates. Introduction to LISP- Basic and intermediate LISP programming. Expert Systems: Architecture of an expert system, existing expert systems like MYCIN, Expert system shells.