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Alban Grastien and Marie-Odile Cordier and Christine Largouët
Automata Slicing for Diagnosing Discrete-Event Systems with Partially Ordered Observations
, AIIA'05 (Congress of the Italian Association for Artificial Intelligence)
, Milan, Italy
, Septembre
, 2005
, Document
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Abstract
When dealing with real systems, it is unrealistic to suppose that
observations can be totally ordered according to their emission
dates. The partially orde red observations and the system are thus
both represented as finite-state machines (or automata) and the
diagnosis formally defined as the synchronized composition of the
model with the observations. The problem we deal with in this paper
is that, taking into account partially ordered observations rather
than sequential ones, it becomes difficult to consider the
observations one after the other and to incrementally compute the
global diagnosis.
In this paper, we rely on a slicing of the observation automata and
propose to compute diagnosis slices (for each observation slice)
before combining them to get the global diagnosis. In order to reach
this objective, we introduce the concept of automata chain and define
the computation of the diagnosis using this chain, first in a modular
way and then, more efficiently, in an incremental way. These results
are then extended to the case where observations are sliced according
to temporal windows. This study is done in an off-line context. It is
a first and necessary step before considering the on-line context
which is discussed in the conclusion.
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