Rational Communication in Multi-Agent Environments

  • Authors:

  • Piotr J. Gmytrasiewicz;Edmund H. Durfee

  • Affiliations:

  • Computer Science and Engineering, University of Texas at Arlington, TX 76013/piotr@cse.uta.edu;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 durfee@umich.edu

  • Venue:
  • Autonomous Agents and Multi-Agent Systems
  • Year:
  • 2001

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Abstract

We address the issue of rational communicative behavior among autonomous self-interested agents that have to make decisions as to what to communicate, to whom, and how. Following decision theory, we postulate that a rational speaker should design a speech act so as to optimize the benefit it obtains as the result of the interaction. We quantify the gain in the quality of interaction in terms of the expected utility, and we present a framework that allows an agent to compute the expected utilities of various communicative actions. Our framework uses the Recursive Modeling Method as the specialized representation used for decision-making in a multi-agent environment. This representation includes information about the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents' beliefs, and so on. Decision-theoretic pragmatics of a communicative act can be then defined as the transformation the act induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of interaction, expressed in terms of the expected utilities of the agent's best actions before and after the communicative act. We analyze decision-theoretic pragmatics of a number of important kinds of communicative acts and investigate their expected utilities using examples. Finally, we report on the agreement between our method of message selection and messages that human subjects choose in various circumstances, and show an implementation and experimental validation of our framework in a simulated multi-agent environment.