Evolving Spatiotemporal Coordination in a Modular Robotic System

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Prokopenko:2006:SAB,
  author =       "Mikhail Prokopenko and Vadim Gerasimov and 
                 Ivan Tanev",
  title =        "Evolving Spatiotemporal Coordination in a Modular
                 Robotic System",
  booktitle =    "From Animals to Animats 9: 9th International
                 Conference on the Simulation of Adaptive Behavior (SAB
                 2006)",
  year =         "2006",
  editor =       "Stefano Nolfi and Gianluca Baldassarre and 
                 Raffaele Calabretta and John C. T. Hallam and Davide Marocco and 
                 Jean-Arcady Meyer and Orazio Miglino and 
                 Domenico Parisi",
  volume =       "4095",
  series =       "Lecture Notes in Computer Science",
  pages =        "558--569",
  address =      "Rome, Italy",
  month =        "25-29 " # sep,
  publisher =    "Springer",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming,
                 spatiotemporal coordination, entropy",
  ISBN =         "3-540-38608-4",
  URL =          "http://www.ict.csiro.au/staff/mikhail.prokopenko/Publications/Agents/snakebot-corrected.pdf",
  DOI =          "doi:10.1007/11840541_46",
  size =         "12 pages",
  abstract =     "In this paper we present a novel information-theoretic
                 measure of spatiotemporal coordination in a modular
                 robotic system, and use it as a fitness function in
                 evolving the system. This approach exemplifies a new
                 methodology formalising co-evolution in multi-agent
                 adaptive systems: information-driven evolutionary
                 design. The methodology attempts to link together
                 different aspects of information transfer involved in
                 adaptive systems, and suggests to approximate direct
                 task-specific fitness functions with intrinsic
                 selection pressures. In particular, the
                 information-theoretic measure of coordination employed
                 in this work estimates the generalised correlation
                 entropy K2 and the generalized excess entropy E2
                 computed over a multivariate time series of actuators'
                 states. The simulated modular robotic system evolved
                 according to the new measure exhibits regular
                 locomotion and performs well in challenging terrains.",
  notes =        "http://www.sab06.org/",
}

Genetic Programming entries for Mikhail Prokopenko Vadim Gerasimov Ivan T Tanev

Citations