Modeling Human Ad Hoc Coordination

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration.Comment: AAAI 201

Agent-Human Coordination with Communication Costs Under Uncertainty

Coordination in mixed agent-human environments is an important, yet not a simple, problem. Little attention has been given to the issues raised in teams that consist of both computerized agents and people. In such situations different considerations are in order, as people tend to make mistakes and they are affected by cognitive, social and cultural factors. In this paper we present a novel agent designed to proficiently coordinate with a human counterpart. The agent uses a neural network model that is based on a pre-existing knowledge base which allows it to achieve an efficient modeling of a human's decisions and predict their behavior. A novel communication mechanism which takes into account the expected effect of communication on the other member will allow communication costs to be minimized. In extensive simulations involving more than 200 people we investigated our approach and showed that our agent achieves better coordination when involved, compared to settings in which only humans or another state-of-the-art agent are involved