-------- Original-Nachricht --------
Betreff: 20050518.VORTRAG: Prof.Dr. Michal Pechoucek, Prague
Datum: Fri, 6 May 2005 13:26:17 +0200
Von: OeFAI Information Server <oefaiinf(a)ai.univie.ac.at>
An: undisclosed-recipients:;
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(a)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