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Explanation through Argumentation

Authors:

Elizabeth I. Sklar,

Mohammad Q. AzharAuthors Info & Claims

HAI '18: Proceedings of the 6th International Conference on Human-Agent Interaction

Pages 277 - 285

https://doi.org/10.1145/3284432.3284470

Published: 04 December 2018 Publication History

Abstract

Computational Argumentation is a logical model of reasoning that has its origins in philosophy and provides a means for organising evidence for (or against) particular claims (or decisions). Argumentation-based Dialogue is a related methodology that is used for structuring interactions between two (or more) agents and has been explored within the Multi-Agent Systems community as an extended form of negotiation where agents can not only exchange claims, but also their reasons for believing (or disbelieving) those claims. Recently, the Artificial Intelligence (AI) community has become intrigued by the notion of "Explainable AI", in which intelligent systems are able to explain predictions or decisions to (human) users. There is a natural pairing between Explainable AI and Argumentation: the first requires the need to clarify and defend decisions and the second provides a method for linking any decision to the evidence supporting it. In this paper, we describe how the two are connected and illustrate the utility of argumentation-based dialogue as a technique for implementing Explainable AI in a human-robot system.

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Cited By

Rizzo LVerda DBerretta SLongo L(2024)A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AIMachine Learning and Knowledge Extraction10.3390/make60301016:3(2049-2073)Online publication date: 12-Sep-2024
https://doi.org/10.3390/make6030101
Meyer LZhu J(2024)Slide to Explore 'What If': An Analysis of Explainable InterfacesAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685416(1-6)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685416
Kampik TPotyka NYin XČyras KToni F(2024)Contribution functions for quantitative bipolar argumentation graphsInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109255173:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.ijar.2024.109255
Show More Cited By

Index Terms

Explanation through Argumentation
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies

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Published In

cover image ACM Conferences

HAI '18: Proceedings of the 6th International Conference on Human-Agent Interaction

December 2018

402 pages

ISBN:9781450359535

DOI:10.1145/3284432

General Chairs:
Michita Imai
Keio University, Japan
,
Tim Norman
University of Southampton, UK
,
Program Chairs:
Elizabeth Sklar
King's College London, UK
,
Takanori Komatsu
Meiji University, Japan

Copyright © 2018 ACM.

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Published: 04 December 2018

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HAI '18: 6th International Conference on Human-Agent Interaction

December 15 - 18, 2018

Southampton, United Kingdom

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HAI '18 Paper Acceptance Rate 40 of 92 submissions, 43%;

Overall Acceptance Rate 121 of 404 submissions, 30%

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Cited By

Rizzo LVerda DBerretta SLongo L(2024)A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AIMachine Learning and Knowledge Extraction10.3390/make60301016:3(2049-2073)Online publication date: 12-Sep-2024
https://doi.org/10.3390/make6030101
Meyer LZhu J(2024)Slide to Explore 'What If': An Analysis of Explainable InterfacesAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685416(1-6)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685416
Kampik TPotyka NYin XČyras KToni F(2024)Contribution functions for quantitative bipolar argumentation graphsInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109255173:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.ijar.2024.109255
Baroni PCerutti FGiacomin M(2024)On Generalized Notions of Consistency and Reinstatement and their Preservation in Formal ArgumentationArtificial Intelligence10.1016/j.artint.2024.104202(104202)Online publication date: Aug-2024
https://doi.org/10.1016/j.artint.2024.104202
Winikoff M(2024)Towards Engineering Explainable Autonomous SystemsEngineering Multi-Agent Systems10.1007/978-3-031-71152-7_9(144-155)Online publication date: 5-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-71152-7_9
Winikoff MSidorenko GAgmon NAn BRicci AYeoh W(2023)Evaluating a Mechanism for Explaining BDI Agent BehaviourProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598908(2283-2285)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598908
Stepin IBudzynska KCatala APereira-Fariña MAlonso-Moral J(2023)Information-seeking dialogue for explainable artificial intelligence: Modelling and analyticsArgument & Computation10.3233/AAC-22001115:1(49-107)Online publication date: 22-May-2023
https://doi.org/10.3233/AAC-220011
Guo YYu TBai LTang JRuan YZhou Y(2023)Argumentative Explanation for Deep Learning: A Survey2023 IEEE International Conference on Unmanned Systems (ICUS)10.1109/ICUS58632.2023.10318322(1738-1743)Online publication date: 13-Oct-2023
https://doi.org/10.1109/ICUS58632.2023.10318322
Ramantas KBikos ANitzold WPollin SKsentini AMayrargue STheodorou VChristofi LGardikis GRahman MChawla AIbañez FChochliouros INicholson DMario Montagudand Shojaeifard APagkotzidis AVerikoukis C(2023)6G-BRICKS: Building Reusable Testbed Infrastructures for Cloud-to-Device Breakthrough Technologies2023 IEEE Globecom Workshops (GC Wkshps)10.1109/GCWkshps58843.2023.10465223(751-756)Online publication date: 4-Dec-2023
https://doi.org/10.1109/GCWkshps58843.2023.10465223
Prentzas NPattichis CKakas A(2023)Explainable Machine Learning via ArgumentationExplainable Artificial Intelligence10.1007/978-3-031-44070-0_19(371-398)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-3-031-44070-0_19
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