Abstract
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.
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Giles, C.L., Jim, KC. (2003). Learning Communication for Multi-agent Systems. In: Truszkowski, W., Hinchey, M., Rouff, C. (eds) Innovative Concepts for Agent-Based Systems. WRAC 2002. Lecture Notes in Computer Science(), vol 2564. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45173-0_29
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DOI: https://doi.org/10.1007/978-3-540-45173-0_29
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