Artificial Intelligence: Knowledge Representation And Reasoning

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