Dialogue-based Explanations for Logical Reasoning using Structured Argumentation

2025-02-16

AI Summary

Existing approaches to inconsistency-tolerant reasoning over knowledge bases lack interpretability—particularly in characterizing non-binary conflicts and identifying critical inference grounds. Method: This paper introduces the first dialogue-based explanatory framework grounded in structured argumentation. It innovatively maps logic-based maximal consistent subset (MCS) techniques onto a unified structured argumentation model, formalizes dialectical proof as a dialogical game, and devises an algorithm for automated generation of dialectical proof trees. Contribution/Results: The framework enables stepwise, intuitive, and verifiable explanation generation for complex non-binary conflicts—achieving, for the first time, rigorous formal correctness on standard KB benchmarks. It significantly enhances explanatory expressiveness, comprehensibility, and traceability while preserving logical fidelity.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Explaining inconsistency-tolerant reasoning in KBs
• Addressing structural weaknesses in explanations
• Creating expressive dialogue-based explanation models

Innovation

• Dialogue models for logical reasoning
• Argumentation-based inconsistency-tolerant semantics
• Dialectical proof trees as explanations