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
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