Terina.Jett@B.GP.CS.CMU.EDU (07/20/87)
PROBABILITY AND ARTIFICIAL INTELLIGENCE Offered by: Department of Philosophy, CMU Instructor: Kevin T. Kelly Grad Course No: 80-312 Undergrad Course No: 80-811 Place: Porter Hall, 126-B Time: Tuesday, Thursday, 3:00-4:00 Intended Audience: Graduate students ans sophisticated undergraduates interested in inductive methods, the philosophy of science, mathematical logic, statistics, computer science, artificial intelligence, and cognitive science. Prerequisites: Familiarity with mathematical logic, computation, and probability theory. Course Focus: There are several ways in which the combined system of a rational agent and its environment can be stochastic. The agent's hypotheses may make claims about probabilities, the agent's environment may be stochastic, and the agent itself may be stochastic, in any com- bination. In this course, we will examine efforts to study computational proposals from the point of view of logic and probability theory. Example topics are Bayesian systems, Dempster/Shafer theory, medical expert systems, computationally tractable learnability, automated linear causal modelling, and Osherson and Weinstein's results concerning limitations on effective Bayesians. Course Format: The grade will be based on frequent exercises and possibly a final project. There will be no examinations if the class keeps up with the material. -------