The following courses relevant to the group's areas of research are offered at UIUC:
CS598EA - Decision-making under uncertainty
The course covers decision-making algorithms and systems in situations of partial knowledge. The focus is on decision making in dynamic situations when the state of the world is not completely observable, and that state has to be estimated using partial knowledge of the way the world evolves (in response to actions or otherwise) and the way world states and actions give rise to observations.
CS591EA - KRR Seminar
In each session of the seminar, one of the participants presents a recent paper pertaining to the general theme of knowledge representation and reasoning, with subsequent discussion.
CS498EA - Knowledge Representation and Reasoning
The course covers knowledge representation and reasoning algorithms in artificial intelligence. On the knowledge representation side it mixes logical and probabilistic knowledge, and discusses representations that involve time, space, and beliefs about self and other agents' knowledge. On the inference side it discusses inference and decision making with logical languages, probabilistic systems, and dynamic systems. It covers both exact and approximate techniques for reasoning, and emphasizes applications of these techniques in vision, robotics, virtual worlds, and others.
CS591 - Advanced Reasoning in Artificial Intelligence
The seminar covers research papers that discuss reasoning algorithms in artificial intelligence. The focus this semester is on logical automated reasoning. We will discuss logical languages, dynamic systems, and decision making with these languages and systems. We will cover both exact and approximate techniques for reasoning, and will emphasize applications of these techniques in vision, robotics, virtual worlds, and others
CS497EA - Reasoning in Artificial Intelligence
The course covers reasoning algorithms and their application to problems in artificial intelligence. The focus is on reasoning with propositional and first-order logical knowledge, use of structure and monte-carlo techniques in automated reasoning, reasoning with directed and undirected probabilistic graphical models, inference in first-order probabilistic models, inference in dynamic systems, and filtering (state estimation). The course will cover both exact and approximate techniques for reasoning with those kinds of knowledge, and will emphasize applications of these techniques in vision, robotics, virtual worlds, and others.