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, Proof Systems
Week 2 Tableau Method, Resolution Method, First Order Logic (FOL)
Week 3 FOL Semantics, Unification, Forward Chaining with the Rete Algorithm
Week 4 Rete example, Reification, Event Calculus, Conceptual Dependency (CD) Theory
Week 5 Conceptual Dependency Theory, Mapping from Natural Language, Goal Trees
Week 6 Logic Programming with Prolog, Resolution Refutation in FOL
Week 7 SLD Resolution, Frames, Scripts
Week 8 Goals and Plans, Description Logic (DL), Structure Matching
Week 9 Classification in DL, A-Box Reasoning, Extensions of DL, ALC
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.