Learning communication for multi-agent systems

C. Lee Giles, Kam Chuen Jim

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to learn multi-agent languages for the predator agents in a version of the predator-prey problem. The resulting evolved behavior of the communicating multi-agent system is equivalent to that of a Mealy machine whose states are determined by the evolved language. We also constructed non-learning predators whose capture behavior was designed to take advantage of prey behavior known a priori. Simulations show that introducing noise to the decision process of the hard-coded predators allow them to significantly ourperform all previously published work on similar preys. Furthermore, the evolved communicating predators were able to perform significantly better than the hard-coded predators, which indicates that the system was able to learn superior communicating strategies not readily available to the human designer.

Original languageEnglish (US)
Pages (from-to)377-390
Number of pages14
JournalLecture Notes in Computer Science
StatePublished - 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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