# Dialectical Reconciliation via Structured Argumentative Dialogues

arXiv: https://arxiv.org/abs/2306.14694 · HTML: https://mlsanity.com/paper/2306.14694/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 26 Jun 2023.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [Interactive Explanations by Conflict Resolution via Argumentative Exchanges](https://mlsanity.com/paper/2303.15022/) (2023)
- [On Exploiting Hitting Sets for Model Reconciliation](https://mlsanity.com/paper/2012.09274/) (2020)
- [ABA+: Assumption-Based Argumentation with Preferences](https://mlsanity.com/paper/1610.03024/) (2016)
- [On Model Reconciliation: How to Reconcile When Robot Does not Know Human's Model?](https://mlsanity.com/paper/2208.03091/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs](https://mlsanity.com/paper/2410.22209/) (2024)
- [On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios](https://mlsanity.com/paper/2405.19229/) (2024)
- [How Do People Revise Inconsistent Beliefs? Examining Belief Revision in Humans with User Studies](https://mlsanity.com/paper/2506.09977/) (2025)
- [Approximating Human Models During Argumentation-based Dialogues](https://mlsanity.com/paper/2405.18650/) (2024)
- [A Study of Belief Revision Postulates in Multi-Agent Systems (Extended Version)](https://mlsanity.com/paper/2605.02249/) (2026)

## Abstract (arXiv)

We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting a structured argumentation-based dialogue paradigm, our framework enables dialectical reconciliation to address knowledge discrepancies between an explainer (AI agent) and an explainee (human user), where the goal is for the explainee to understand the explainer's decision. We formally describe the operational semantics of our proposed framework, providing theoretical guarantees. We then evaluate the framework's efficacy ``in the wild'' via computational and human-subject experiments. Our findings suggest that our framework offers a promising direction for fostering effective human-AI interactions in domains where explainability is important.