Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach

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 theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

An Expert System Shell Performing the Generic Task of Hierarchical Classification

Any expert system shell that performs with the generic task of hierarchical classificiation must deal explicitly with the issues of knowledge representations, control strategies, inductive learning, and ways of handling uncertainty, ambiguity, and contradictions. This resesarch is mainly concerned about the creation of the expert system shell HICLASS. Aspects crucial to this task are challenged from btoh a theoretical and an implementational point of view. The principles of generic tasks and hierarchical classification are described. Important concepts of HICLASS are introducted, followed by a detailed description of the knowledge representation and local control strategies developed for the system, including a discussion of special problems and respective solutions. IT is described how HICLASS handles uncertainty. Important issues like concluding values, explanation, learning, incorporating metaknowledge, and the global control strategy of HICLASS are discussed. Then, the actual implementation of the table editor HIEDIT as well as HICLASS is described in detail. It is show that HICLASS is a genuine tool for the generic task for hierarchical classification. The system is compared to two well-known tools for hierarchical classification. Using the ideas raised for HICLASS, the development of a hierarchical hypothesis matcher, HIHYPO, is proposed. Essential features of HIHYPO are introducted. A theoretic overview about algorithms for inductive learning is followed by the description of an inductive learning algorithm developed for HIHYPO. Appendix B provides an overview about software engineering methods, and a discussioin about methods actually used to create the HICLASS package. In Appendix C, the definitions of all modules developed for the package are shown