Exploiting Irrelevance Reasoning to Guide Problem Solving

Abstract

Identifying that parts of a knowledge base (KB) are irrelevant to a specific query is a powerful method of controlling search during problem solving. However, finding methods of such irrelevance reasoning and analyzing their utility are open problems. We present a framework based on a proof-theoretic analysis of irrelevance that enables us to address these problems. Within the framework , we focus on a class of strong-irrelevance claims and show that they have several desirable properties. For example , in the context of Horn-rule theories , we show that strong-irrelevance claims can be derived efficiently either by examining the KB or as logical consequences of other strongirrelevance claims. An important aspect is that our algorithms reason about irrelevance using only a small part of the KB . Consequently, the reasoning is efficient and the derived irrelevance claims are independent of changes to other parts of the KB .