Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach

TL;DR

This paper introduces a flexible, constructive induction-based approach to theory revision that addresses representation and structural limitations, enabling more accurate and complex domain theories.

Contribution

It proposes a new theory-guided constructive induction system that enhances flexibility in representation and structure for improved theory revision.

Findings

Shows improved accuracy over previous systems

Demonstrates effectiveness across three domains

Analyzes limitations and potential of the approach

Abstract

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.