Evolving reactive agents with SignalGP

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1 Ecology, Evolutionary Biology, and Behavior program, Michigan State University, East Lansing, Michigan, United States
2 Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States
3 BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, United States
DOI
10.7287/peerj.preprints.26921v1
Published
Accepted
Subject Areas
Adaptive and Self-Organizing Systems, Agents and Multi-Agent Systems, Artificial Intelligence
Keywords
evolutionary computation, genetic programming, SignalGP, event-driven programming, tags
Copyright
© 2018 Lalejini et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Lalejini A, Ofria C. 2018. Evolving reactive agents with SignalGP. PeerJ Preprints 6:e26921v1

Abstract

We introduce SignalGP, a technique for creating digital organisms that harnesses the event-driven programming paradigm. These organisms can evolve to automatically react to signals from the environment or from other agents in a biologically-inspired manner. In addition to introducing SignalGP, we summarize previous results demonstrating the value of the event-driven paradigm in environments dominated by agent-agent and agent-environment interaction. Our full introduction to SignalGP will be published in the proceedings of the 2018 Genetic and Evolutionary Computation Conference (pre-print: https://arxiv.org/pdf/1804.05445.pdf).

Author Comment

This submission is a peer-reviewed extended abstract to appear in the proceedings of 2018 Conference on Artificial Life (ALIFE). In this extended abstract, we introduce SignalGP to the Artificial Life community, summarizing our full introduction to SignalGP (pre-print: https://arxiv.org/pdf/1804.05445.pdf).