Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. We evaluate our approach on two program induction domains: real-world string transformations and building Lego structures. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.

We formulate Dempster Shafer Belief functions in terms of Propositional Logic using the implicit notion of provability underlying Dempster Shafer Theory. Given a set of propositional clauses, assigning weights to certain propositional literals enables the Belief functions to be explicitly computed using Network Reliability techniques. Also, the logical procedure corresponding to updating Belief functions using Dempster's Rule of Combination is shown. This analysis formalizes the implementation of Belief functions within an Assumption-based Truth Maintenance System (ATMS). We describe the extension of an ATMS-based visual recognition system, VICTORS, with this logical formulation of Dempster Shafer theory. Without Dempster Shafer theory, VICTORS computes all possible visual interpretations (i.e. all logical models) without determining the best interpretation(s). Incorporating Dempster Shafer theory enables optimal visual interpretations to be computed and a logical semantics to be maintained.

Raymond Reiter' Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S-1A4 Johan de Kleer Intelligent Systems Laboratory XEROX Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 ABSTRACT In this paper we (1) define the concept of a Clause Managetnent System (CMS) A Problem-Solving Architecture Figure 1 illustrates an architecture for a problem solving system consisting of a domain dependent Reasoner coupled to a domain independent Clause Management System (CMS). For our present purposes, the Reasoner is a black box which, m the process of doing whatever it does, occasionally transmits a propositional clause 2 to the CMS.