TY - GEN
T1 - Ai for explaining decisions in multi-agent environments
AU - Kraus, Sarit
AU - Azaria, Amos
AU - Fiosina, Jelena
AU - Greve, Maike
AU - Hazon, Noam
AU - Kolbe, Lutz
AU - Lembcke, Tim Benjamin
AU - Müller, Jörg P.
AU - Schleibaum, Sören
AU - Vollrath, Mark
N1 - Publisher Copyright:
© 2020, Association for the Advancement of Artificial Intelligence.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097378516&partnerID=8YFLogxK
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AN - SCOPUS:85097378516
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 13534
EP - 13538
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -