AI for Explaining Decisions in Multi-Agent Environments

被引：0

作者：

Kraus, Sarit ^{[1
]}

Azaria, Amos ^{[2
]}

Fiosina, Jelena ^{[3
]}

Greve, Maike ^{[4
]}

Hazon, Noam ^{[2
]}

Kolbe, Lutz ^{[4
]}

Lembcke, Tim-Benjamin ^{[4
]}

Mueller, Joerg P. ^{[3
]}

Schleibaum, Soeren ^{[3
]}

Vollrath, Mark ^{[5
]}

机构：

[1] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel

[2] Ariel Univ, Dept Comp Sci, Ariel, Israel

[3] Tech Univ Clausthal, Dept Informat, Clausthal Zellerfeld, Germany

[4] Georg August Univ Gottingen, Chair Informat Management, Gottingen, Germany

[5] TU Braunschweig, Chair Engn & Traff Psychol, Braunschweig, Germany

来源：

THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2020年 / 34卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：13534 / 13538

页数：5

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