Larry A. Rendell

Learn More
This paper has two major parts. The first is an extensive analysis of the problem of induction, and the second part is a detailed study of selective induction. Throughout the paper we integrate a number of notions, mainly from artificial intelligence, but also from pattern recognition and cognitive psychology. The result is a synthetic view which exploits(More)
This paper addresses a problem of induction (generalization learning) which is more difficult than any comparable work in AI . The subject of the present research is a hard problem of new terms, a task of realistic constructive induction. While the approach is quite general, the system is analyzed and tested in an environment of heuristic search where noise(More)
The intrinsic accuracy of an inductive problem is the accuracy achieved by exhaustive table look-up. Intrinsic accuracy is the upper bound for any inductive method. Hard concepts are concepts that have high intrinsic accuracy. but which cannot be learned effectively with traditional inductive methods. To learn hard concepts, we must use conslructive(More)
Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved(More)