Dialectical Reconciliation via Structured Argumentative Dialogues
This addresses the problem of enhancing human-AI interaction for users in domains requiring explainability, but it appears incremental as it extends existing model reconciliation approaches.
The paper tackles the problem of knowledge discrepancies between AI agents and human users by proposing a structured argumentation-based dialogue framework for dialectical reconciliation, with evaluation showing it offers a promising direction for effective human-AI interactions in explainability domains.
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.