An intelligent agent needs to be able to solve problems in its world. The ability to create representations of the domain of interest and reason with these representations is a key to intelligence. In this course we explore a variety of representation formalisms and the associated algorithms for reasoning. We start with a simple language of propositions, and move on to first order logic, and then to representations for reasoning about action, change, situations, and about other agents in incomplete information situations. This course is a companion to the course “Artificial Intelligence: Search Methods for Problem Solving” that was offered recently and the lectures for which are available online.
Week 1: Introduction, Propositional Logic, Syntax and Semantics Week 2: Proof Systems, Natural Deduction, Tableau Method, Resolution Method Week 3: First Order Logic (FOL), Syntax and Semantics, Unification, Forward Chaining Week 4: The Rete Algorithm, Rete example, Programming Rule Based Systems Week 5: Representation in FOL, Categories and Properties, Reification, Event Calculus Week 6: Conceptual Dependency (CD) Theory, Understanding Natural Language Week 7: Deductive Retrieval, Backward Chaining, Logic Programming with Prolog Week 8: Resolution Refutation in FOL, FOL with Equality, Complexity of Theorem Proving Week 9: Semantic Nets, Frames, Scripts, Goals and Plans Week 10: Description Logic (DL), Structure Matching, Classification Week 11: Extensions of DL, The ALC Language, Inheritance in Taxonomies Week 12: Default Reasoning, Circumscription, The Event Calculus Revisited Week 13: Default Logic, Autoepistemic Logic, Epistemic Logic, Multi Agent Scenarios
Complete knowledge, quality content accompanied by good teaching. This course actually one of the three modules taught by Prof.Deepak Khemani. Other two are AI: Search method and problem solving and AI: constraint satisfaction. By taking these courses, it will help build solid foundation in AI.