Year: 2024
Type: conference-paper
Abstract: 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.
DOI: 10.24963/kr.2024/73
Authors: Stylianos Loukas Vasileiou, Ashwin Kumar, William Yeoh, Tran Cao Son, Francesca Toni
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: Artificial Intelligence for Arid Land Agriculture (AIALA), CREST: Interdisciplinary Center for Research Excellence in Design of Intelligent Technologies for Smartgrids Phase II, Collaborative Research: RI: Small: End-to-end Learning of Fair and Explainable Schedules for Court Systems, Argumentation-based Deep Interactive EXplanations(ADIX)
OpenAlex ID: W4403792810