Volume 7, Issue 1
  • ISSN 1572-0373
  • E-ISSN: 1572-0381
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Previous simulation work on the evolution of communication has not shown how a large signal repertoire could emerge in situated agents. We present an artificial life simulation of agents, situated in a two-dimensional world, that must search for other agents with whom they can trade resources. With strong restrictions on which resources can be traded for others, initially non-communicating agents evolve/learn a signal system that describes the resource they seek and the resource they are willing to offer in return. A large signal repertoire emerges mainly through an evolutionary process. Agents whose production and comprehension abilities rely on a single mechanism fare best, although learning enables agents with separate mechanisms to achieve some measure of success. These results demonstrate that substantial signaling repertoires can evolve in situated multi-agent systems, and suggest that simulated social interactions such as trading may provide a useful context for further computational studies of the evolution of communication.


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