# Dialogue-based Explanations for Logical Reasoning using Structured Argumentation

arXiv: https://arxiv.org/abs/2502.11291 · HTML: https://mlsanity.com/paper/2502.11291/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 6 of its 73 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 6 of 6)

- [Justifying Answer Sets using Argumentation](https://mlsanity.com/paper/1411.5635/) (2014)
- [Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation](https://mlsanity.com/paper/2003.05746/) (2020)
- [Finding Small Proofs for Description Logic Entailments: Theory and Practice (Extended Technical Report)](https://mlsanity.com/paper/2004.08311/) (2020)
- [Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation](https://mlsanity.com/paper/2404.11431/) (2024)
- [Explaining Ontology-Mediated Query Answers using Proofs over Universal Models (Technical Report)](https://mlsanity.com/paper/2208.14381/) (2022)
- [On the Correspondence of Non-flat Assumption-based Argumentation and Logic Programming with Negation as Failure in the Head](https://mlsanity.com/paper/2405.09415/) (2024)

## Built on by (most cited first, 2 of 2)

- [AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning](https://mlsanity.com/paper/2510.16156/) (2025)
- [Reasoning Systems as Structured Processes: Foundations, Failures, and Formal Criteria](https://mlsanity.com/paper/2508.01763/) (2025)

## Abstract (arXiv)

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.