Modeling how Humans Reason about Others with Partial Information

Abstract

Computer agents participate in many collaborative and competitive multi-agent domains in which humans make decisions. For computer agents to interact successfully with people in such environments, an understanding of human reasoning is beneficial. In this paper, we investigate the question of how people reason strategically about others under uncertainty and the implications of this question for the design of computer agents. Using a situated partial-information negotiation game, we conduct human-subjects trials to obtain data on human play. We then construct a hierarchy of models that explores questions about human reasoning: Do people explicitly reason about other players in the game? If so, do people also consider the possible states of other players for which only partial information is known? Is it worth trying to capture such reasoning with computer models and subsequently utilize them in computer agents? We further address these questions by constructing computer agents that use our models; we deploy our agents in further human-subjects trials for evaluation. Our results indicate that people do reason about other players in our game and that the computer agents that best model human play obtain superior scores.