Abstract
Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known. It is even more important when the AI system makes decisions in multi-agent environments where the human does not know the systems' goals since they may depend on other agents' preferences. In such situations, explanations should aim to increase user satisfaction, taking into account the system's decision, the user's and the other agents' preferences, the environment settings and properties such as fairness, envy and privacy. Generating explanations that will increase user satisfaction is very challenging; to this end, we propose a new research direction: Explainable decisions in Multi-Agent Environments (xMASE). We then review the state of the art and discuss research directions towards efficient methodologies and algorithms for generating explanations that will increase users' satisfaction from AI systems' decisions in multi-agent environments.
Original language | English |
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Title of host publication | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
Publisher | AAAI press |
Pages | 13534-13538 |
Number of pages | 5 |
ISBN (Electronic) | 9781577358350 |
State | Published - 2020 |
Event | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States Duration: 7 Feb 2020 → 12 Feb 2020 |
Publication series
Name | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
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Conference
Conference | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 |
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Country/Territory | United States |
City | New York |
Period | 7/02/20 → 12/02/20 |
Bibliographical note
Publisher Copyright:© 2020, Association for the Advancement of Artificial Intelligence.
Funding
∗This work was partly supported by Volkswagen Foundation. Copyright ©c 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funders | Funder number |
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Volkswagen Foundation |