Explainable Reasoning in Face of Contradictions: From Humans to Machines

Timotheus Kampik, Dov Gabbay

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A well-studied trait of human reasoning and decision-making is the ability to not only make decisions in the presence of contradictions, but also to explain why a decision was made, in particular if a decision deviates from what is expected by an inquirer who requests the explanation. In this paper, we examine this phenomenon, which has been extensively explored by behavioral economics research, from the perspective of symbolic artificial intelligence. In particular, we introduce four levels of intelligent reasoning in face of contradictions, which we motivate from a microeconomics and behavioral economics perspective. We relate these principles to symbolic reasoning approaches, using abstract argumentation as an exemplary method. This allows us to ground the four levels in a body of related previous and ongoing research, which we use as a point of departure for outlining future research directions.

Original languageEnglish
Title of host publicationExplainable and Transparent AI and Multi-Agent Systems - 3rd International Workshop, EXTRAAMAS 2021, Revised Selected Papers
EditorsDavide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling
PublisherSpringer Science and Business Media Deutschland GmbH
Pages280-295
Number of pages16
ISBN (Print)9783030820169
DOIs
StatePublished - 2021
Event3rd International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021 - Virtual, Online
Duration: 3 May 20217 May 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12688 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021
CityVirtual, Online
Period3/05/217/05/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Funding

Acknowledgments. The authors thank Amro Najjar, Michele Persiani, and the anonymous reviewers for their useful feedback. This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

FundersFunder number
Knut och Alice Wallenbergs Stiftelse

    Keywords

    • Explainable artificial intelligence
    • Non-monotonic reasoning
    • Symbolic artificial intelligence

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