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. INTENDED AUDIENCE : BE/ME/MS/MSc/PhD studentsPREREQUISITES : Some exposure to formal languages, logic and programmingINDUSTRY SUPPORT : Software companies dealing with knowledge and reasoning, including the semantic web and semantic search.
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: Deductive Retrieval, Backward Chaining, Logic Programming with Prolog Week 7: Resolution Refutation in FOL, FOL with Equality, Complexity of Theorem Proving Week 8: Description Logic (DL), Structure Matching, Classification Week 9: Extensions of DL, The ALC Language, Inheritance in Taxonomies Week 10: Default Reasoning, Circumscription, The Event Calculus Revisited Week 11: Default Logic, Autoepistemic Logic, Epistemic Logic, Multi Agent Scenarios
Optional Topics A:Conceptual Dependency (CD) Theory, Understanding Natural Language Optional Topics B:Semantic Nets, Frames, Scripts, Goals and Plans