AI: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.

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