Chronicle-based Diagnosis, and Diagnosability Test using Simulation

Frank Su

ARTIFICIAL INTELLIGENCE SEMINAR PhD monitoring

DATE: 2013-11-19
TIME: 12:00:00 - 12:30:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU
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ABSTRACT:
In the literature, diagnosis of discrete event systems (DES) is performed by computing the paths on the complete model that generate the observations received on the system, or equivalently the belief state. This belief state can be computed on-line, by iteratively computing the set of states can be reached from the current set through transitions that would produce exactly the next observation. If this is done explicitly, the number of these states makes the approach inapplicable for many real-world problems. Symbolic approaches have been proposed when the states are represented in propositional logic, but this representation is also subject to exponential blow-up. Finally, the potential belief states can be pre-computed, but their number is double exponential. We have proposed windows-based diagnosis, which slices the observations into windows, each diagnosed independently. Without time windows, it would be infeasible for a diagnosis engine to go back to the time origin. Although windows-based diagnosis may have imprecise results, in this talk we are going to show how to verify diagnosability of a diagnosis algorithm, through simulation, which is a modified model that simulates how a diagnosis algorithm computes diagnosis. We are going to extend our approach of diagnosability test using simulation to chronicle-based diagnosis. We start from chronicle-based diagnosis and we propose a new approach to test diagnosability of chronicle-based diagnosis which builds a Chronicle Automaton.
BIO: