Generic Control Ssystem in MultiAgent Domain

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Stephane Calderoni",
  title =        "Generic Control Ssystem in MultiAgent Domain",
  booktitle =    "World Multiconference on Systemics, Cybernetics and
                 Informatics SCI-99",
  year =         "1999",
  volume =       "7",
  keywords =     "genetic algorithms, genetic programming, Multiagent
                 Systems, Control Systems, Reinforcement Learning",
  URL =          "",
  citeseer-isreferencedby = "oai:CiteSeerPSU:26950",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:247844",
  rights =       "unrestricted",
  abstract =     "This paper reports on-going works dealing with
                 collective learning in autonomous agents context. We
                 propose a methodology to design robust and flexible
                 adaptive behavior with both genetic and reinforcement
                 learning techniques.The originality of this
                 contribution relies on the ability of the agents to
                 manage themselves their learning task. Indeed, rather
                 than coming from the environment, as it is implemented
                 in many programs, we consider that the reinforcement
                 must be intrinsically deduced by the agent itself, from
                 satisfaction and disapointment indicators. We show that
                 in such a way, the agents are capable of robustness
                 facing with unexpected situations. A collective
                 regulation problem is presented to help in clarify the
                 different issues tackled in this paper. A software
                 toolkit has been developped as a support for these

Genetic Programming entries for Stephane Calderoni