AI for Explaining Decisions in Multi-Agent Environments

M uller, 3 S oren Schleibaum, 3 Mark V ollrath 5 1 Department of Computer Science, Bar-Ilan University, Israel (email: sarit@cs.biu.ac.il) 2 Department of Computer Science, Ariel University, Israel 3 Department of Informatics, TU Clausthal, Germany 4 Chair of Information Management, Georg-August-Universitat G ottingen, Germany 5 Chair of Engineering and Traffic Psychology, TU Braunschweig, Germany 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 system's decisions in multi-agent environments. Introduction Many AI systems need to make decisions in multi-agent environments where the agents, including people and robots, have possibly conflicting preferences. The system should balance between these preferences when making decisions regarding all agents.