How communication can improve the performance of multi-agent systems

K. C. Jim, C. L. Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


We analyze a general model of multi-agent communication in which all agents learn to communicate simultaneously to a message board. We show that the communicating multiagent system is equivalent to a Mealy finite state machine whose states are determined by the agents' usage of the learned language. Increasing the language size increases the number of possible states in the Mealy machine, and can improve the performance of the multi-agent system. We introduce the term semantic density to describe the average number of meanings assigned to each word of a language. Using semantic density, a simple rule is presented that provides a pessimistic estimate of the minimum language size that should be used for any multi-agent problem in which the agents communicate simultaneously. Simulations on a version of the predator-prey pursuit problem, a simplified version of problems seen in warfare scenarios, validate these predictions. The communicating predators evolved using a genetic algorithm perform significantly b etter than all previous work on similar preys.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Autonomous Agents
EditorsJ.P. Muller, E. Andre, S. Sen, C. Frasson
Number of pages8
StatePublished - 2001
EventFifth International Conference on Autonomous Agents - Montreal, Que., Canada
Duration: May 28 2001Jun 1 2001


OtherFifth International Conference on Autonomous Agents
CityMontreal, Que.

All Science Journal Classification (ASJC) codes

  • General Engineering


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