Dialogue-based Explanations for Logical Reasoning using Structured Argumentation

TL;DR

This paper introduces an argumentation-based dialogue approach to explain inconsistency-tolerant reasoning in knowledge bases, providing more expressive and intuitive explanations through dialectical proof trees.

Contribution

It proposes a generic argumentation framework for logics with maximal consistent subsets, enhancing explanation expressiveness in inconsistency-tolerant reasoning.

Findings

Dialectical proof trees serve as explanations for query answers.

The approach improves explanation expressiveness over existing methods.

It offers a dialogue model for reasoning with inconsistent knowledge bases.

Abstract

The problem of explaining inconsistency-tolerant reasoning in knowledge bases (KBs) is a prominent topic in Artificial Intelligence (AI). While there is some work on this problem, the explanations provided by existing approaches often lack critical information or fail to be expressive enough for non-binary conflicts. In this paper, we identify structural weaknesses of the state-of-the-art and propose a generic argumentation-based approach to address these problems. This approach is defined for logics involving reasoning with maximal consistent subsets and shows how any such logic can be translated to argumentation. Our work provides dialogue models as dialectic-proof procedures to compute and explain a query answer wrt inconsistency-tolerant semantics. This allows us to construct dialectical proof trees as explanations, which are more expressive and arguably more intuitive than existing explanation formalisms.