Cooperation through communication in a distributed problem-solving network
Cognition, computing, and cooperation
Knowledge and common knowledge in a distributed environment
PODC '84 Proceedings of the third annual ACM symposium on Principles of distributed computing
Negotiation and task sharing among autonomous agents in cooperative domains
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
A decision-theoretic approach to coordinating multiagent interactions
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Learning in multi-level stochastic games with delayed information
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
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When intelligent agents who have different knowledge and capabilities must work together, they must communicate the right information to coordinate their actions. Developing techniques for deciding what to communicate, however, is problematic, because it requires an agent to have a model of a message recipient and to infer the impact of a message on the recipient based on that model. We have developed a method by which agents build recursive models of each other, where the models are probabilistic and decision-theoretic. In this paper, we show how an agent can compute the impact of a message in terms of how it increases (or decreases) its expected utility. By treating the imperfect communication channel probabilistically, our method allows agents to account for risk in committing to nonintuitive courses of action, and to compute the utility of acknowledging messages.