Prof. Dr. Michal Pechoucek, Prague

Lecture
                                VORTRAG
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Oesterreichisches Forschungsinstitut fuer Artificial Intelligence(OFAI)
                      Freyung 6/6, A-1010 Wien
 Tel.: +43-1-53361120,  Fax: +43-1-5336112-77,  Email: sec@oefai.at
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  Prof.Dr. Michal Pechoucek,
  Czech Technical University and
  Gerstner Laboratory, Prague


        THE USE OF ACQUAINTANCE MODELS FOR EFFICIENT CONTRACTION
                 IN COMPLEX COMPETITIVE ENVIRONMENTS

  Intelligent coordination in complex multi-agent environments requires
  sophisticated mechanisms for suboptimal task decomposition and
  efficient task delegation among agents.

  Besides the quality of coordination (i.e. efficiency of decomposition
  and delegation) we need to handle also computational efficiency
  restriction such as fast response time and limited communication
  traffic among agents but also the amount of disclosed private
  knowledge while the semi-collaborative agents negotiate collaboration.
  We present a novel contraction mechanism based on the use of the
  acquaintance model, a structure where agents store the information
  about possible collaborators. We suggest an approach of iterative
  construction of partially-linear acquaintance models that is
  beneficial mainly in complex agent communities. The Use of the
  suggested approaches is an alternative to socially uniformed
  contraction mechanisms such as a contract-net-protocol or various
  private value auction techniques, that proved to be inadequate in
  complex negotiation domains.

  This approach has shown to be useful in various application domains,
  especially for collaboration in semi-trusted virtual environments
  such as planning humanitarian aid logistics.

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  Zeit:   Mittwoch, 18. Mai 2005, 18:30 Uhr pktl.

  Ort:    Oesterreichisches Forschungsinstitut
          fuer Artificial Intelligence, OFAI
          Freyung 6, Stiege 6, 1010 Wien.


  OESTERREICHISCHES FORSCHUNGSINSTITUT
  FUER ARTIFICIAL INTELLIGENCE


  o.Univ.-Prof. Ing. Dr. Robert Trappl