Abductive Knowledge Base Updates for Contextual Reasoning

Abstract

We show in this paper how procedures that update knowledge bases can naturally be adapted to a number of problems related to contextual reasoning. The fact that the update procedures are abductive in nature is favourably exploited to tackle problems related to human-computer dialogue systems. We consider as examples aspects of pronoun resolution,goal formulation , and the problem of restoring the consistency of a knowledge base after some knowledge update is carried out. We state these problems in terms of the update problem and abductive reasoning and show how procedures that update knowledge bases yield some interesting results. We also explain how these procedures can naturally be used to model various forms of hypothetical reasoning such as hypothesizing inconsistencies and performing some “look ahead” form of reasoning. We do not claim thaT the problems presented here are solved entirely within the update framework. However, we believe that the flexibility of the representation and of the problem-solving approach suggest that the problems could be solved by adding more details about each problem. What is most interesting in our understanding is that all the aforementioned problems are expressed and tackled within the same framework.