Environmental robustness in multi-agent teams

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Terence Soule and Robert B. Heckendorn",
  title =        "Environmental robustness in multi-agent teams",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "177--184",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1569926",
  abstract =     "Evolution has proven to be an effective method of
                 training heterogeneous multi-agent teams of autonomous
                 agents to explore unknown environments. Autonomous,
                 heterogeneous agents are able to go places where humans
                 are unable to go and perform tasks that would be
                 otherwise dangerous or impossible to complete. However,
                 a serious problem for practical applications of
                 multi-agent teams is how to move from training
                 environments to real-world environments. In particular,
                 if the training environment cannot be made identical to
                 the real-world environment how much will performance
                 suffer? In this research we investigate how differences
                 in training and testing environments affect
                 performance. We find that while in general performance
                 degrades from training to testing, for difficult
                 training environments performance improves in the test
                 environment. Further, we find distinct differences
                 between the performance of different training
                 algorithms with Orthogonal Evolution of Teams (OET)
                 producing the best overall performance.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",

Genetic Programming entries for Terence Soule Robert B Heckendorn