Bottleneck Analysis as a Heuristic for Self-Adaption in Multi-Agent Societies
-Extended Abstract -

Christian Gerber
  German Research Center for Artificial Intelligence (DFKI)
  Stuhlsatzenhausweg 3, 66123 Saarbruecken, Germany
  Tel. +49 681 302 4578, Fax +49 681 2235
  gerber@dfki.de


During recent years, due to advances in the theory of multi-agent systems,
 but also due to the explosion of hardware power, more and more complex
multi-agent systems can be realized. This movement induces the need to
introduce structures into such large systems which do not violate the
paradigm of agent automony since this paradigm has been proven, tried
and tested.

Introducing a structure to a group of agents that pursue a common
goal, leads to the definition of quantities such as the size of the
group, distribution of specialized agents, command structures, types
of communication protocols, etc.  However, for the sake of agent autonomy,
the agents' reasoning processes  should be left untouched.

As multi-agent systems are usably intended to operate for a rather
long period of time (sometimes even ad infinitum), a system which
was originally adjusted to work at a high performance level may lose
its performance as the environment changes. Therefore, anautomated
self-adaption mechanism which reorganizes the system is of great value.
An approach uniting macro-level (i.e., society) aspects and micro-level.
To achieve higher performance, heuristics can be integrated to such a
mechanism. In this paper, we demonstrate how to incorporate  bottleneck
analysis as a heuristic.

Traditional bottleneck analysis approaches try to derive a rather
abstract mathematical model of a complex technical system and the work
flow within that system. Usually, a directed graph is used to
represent such a system: atomic system components are expressed by
nodes; work flow is represented by arcs. Operations Research methods
can then be applied on that model in order to detect local overloads.

Such an approach can easily be used to analyze a multi-agent society
by interpreting agents as nodes, agent cooperation for achieving a
common goal as arcs in the graph and agent  actions as work load
transitions. However, this kind of approach has some drawbacks: Only
agent overloads can be detected; underloads leading to waste of agent
power remain uncovered. Furthermore, this approach bases on estimation
of the system's behavior: faulty estimations may lead to inaccurate
system modifications.  Therefore, we propose a new approach to
incorporate bottleneck analysis to a agent society self-adaption
mechanism: for multi-agent systems we do not need to built up a
mathematical model of the system, we can collect bottleneck data
directly from the agents representing components or component groups.

--
Christian Gerber
German Research Center for
  Artificial Intelligence(DFKI)   Office:Room 221, Building 36 (CS)
Project MAS                       Phone: +49-681-302-4578
Stuhlsatzenhausweg 3              Fax:   +49-681-302-2235
D-66123 Saarbruecken              email: gerber@dfki.de
Germany                           http://www.dfki.de/~gerber/

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