DR-HAI: argumentation-based dialectical reconciliation in human-AI interactions
File(s)XAI2023_paper_20.pdf (242.74 KB)
Accepted version
Author(s)
Vasileiou, Stylianos Loukas
Kumar, Ashwin
Yeoh, William
Son, Tran Cao
Toni, Francesca
Type
Conference Paper
Abstract
In this paper, we introduce DR-HAI – a novel
argumentation-based framework designed to extend model reconciliation approaches, commonly
used in explainable AI planning, for enhanced
human-AI interaction. By adopting a multi-shot
reconciliation paradigm and not assuming a-priori
knowledge of the human user’s model, DR-HAI enables interactive reconciliation to address knowledge discrepancies between an explainer and an explainee. We formally describe the operational semantics of DR-HAI, and provide theoretical guarantees related to termination and success
argumentation-based framework designed to extend model reconciliation approaches, commonly
used in explainable AI planning, for enhanced
human-AI interaction. By adopting a multi-shot
reconciliation paradigm and not assuming a-priori
knowledge of the human user’s model, DR-HAI enables interactive reconciliation to address knowledge discrepancies between an explainer and an explainee. We formally describe the operational semantics of DR-HAI, and provide theoretical guarantees related to termination and success
Date Issued
2024-05-01
Date Acceptance
2023-06-05
Citation
ICAPS 2023 Workshop on Human-Aware Explainable Planning, 2024
Journal / Book Title
ICAPS 2023 Workshop on Human-Aware Explainable Planning
Copyright Statement
Copyright © 2023 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://openreview.net/forum?id=LZHcNjVXA51
Source
IJCAI 2023
Publication Status
Published
Start Date
2023-08-19
Finish Date
2023-08-25
Coverage Spatial
Macao, S.A.R
Date Publish Online
2023-05-01