Terina.Jett@B.GP.CS.CMU.EDU (07/17/87)
SEMINAR IN LOGIC AND COMPUTABILITY ARTIFICIAL INTELLIGENCE AND FORMAL LEARNING THEORY Offered by: Department of Philosophy Instructor: Kevin T. Kelly Grad Listing: 80-510 Undergrad Listing: 80-510 Place: Baker Hall 131-A Time: Wed, 1:30 - 4:30 Intended Audience: Graduate students and sophisticated undergraduates interested in inductive methods, the philosophy of science, mathematical logic, statistics, computer science, artificial intelligence, and cogni- tive science. Prerequisites: A good working knowledge of mathematical logic and comp- utation theory. Course Focus: Convergent realism is the philosophickal thesis that the point of inquiry is to converge (in some sense) to the truth (or to something like it). Formal learning theory is a growing body of precise results concerning the possible circumstances under which this ideal is attainable. The basic idea was developed by Hilary Putnam in the early 1960's, and was extended to questions in theoretical linguistics by E. Mark Gold. The main text fo the seminar will be Osherson and Weinstein's recent book Systems That Learn. But we will also examine more recent efforts by Osherson, Weinstein, Glymour and Kelly to apply the theory to the inductive inference of theories expressed in logical languages. From this general standpoint, we will move to more detailed projects such as the recent results of Valiant, Pitt, and Kearns on polynomials learn- abilitly. Finally, we will examine the extent to which formal learning theory can assist in the demonstrable improvement of learning systems published in the A.I. machine learning literature. There is ample opportunity to break new ground here. Thesis topics abound. Course Format: Serveral introductory lectures, Seminar reports, and a novel r thdssc,es aes ae