Resolving Conflicts in Distributed Diagnosis
P. Fröhlich, and W. Nejdl
Abstract
Current research in the area of diagnosis with multiple models focuses
on a single central diagnostic agent using a hierarchy of system
models with different abstraction levels. In this approach the system
models must have certain strong properties, which can only be assured,
if all the models are created by the same group of experts. This
assumption is a severe limitation in complex systems containing
devices from several companies, as for example in communication
networks. The current paper therefore discusses the use of a set of
heterogeneous agents in diagnosis, the notion of conflict arising in
this environment and possible solutions to resolve these conflicts. We
identify two forms of distribution and argue that each has its
specific definition of a conflict.
Keywords: Diagnosis, Distributed AI
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