Model-based reasoning using answer set programming

Diagnosis, i.e., the detection and identification of faults, provides the basis for bringing systems back to normal operation in case of a fault. Diagnosis is a very important task of our daily live, assuring safe and reliable behavior of systems. The automation of diagnosis has been a successful re...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 52; H. 15; S. 16993 - 17011
Hauptverfasser: Wotawa, Franz, Kaufmann, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2022
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Automation
Boolean
Circuits
Computer Science
Declarative programming
Digital electronics
Emerging topics in Applied Intelligence selected from IEA/AIE2020
Fault detection
Fault diagnosis
Industrial applications
Knowledge
Machines
Manufacturing
Mathematical programming
Mechanical Engineering
Processes
Reasoning
Modeling for diagnosis
Experimental evaluation
Answer set programming
Model-based diagnosis
ISSN:0924-669X, 1573-7497
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Zusammenfassung:Diagnosis, i.e., the detection and identification of faults, provides the basis for bringing systems back to normal operation in case of a fault. Diagnosis is a very important task of our daily live, assuring safe and reliable behavior of systems. The automation of diagnosis has been a successful research topic for several decades. However, there are limitations due to complexity issues and lack of expressiveness of the underlying reasoning mechanisms. More recently logic reasoning like answer set programming has gained a lot of attention and practical use. In this paper, we tackle the question whether answer set programming can be used for automating diagnosis, focusing on industrial applications. We discuss a formalization of the diagnosis problem based on answer set programming, introduce a general framework for modeling systems, and present experimental results of an answer set programming based diagnosis algorithm. Past limitations like not being able to deal with numerical operations for modeling can be solved to some extent. The experimental results indicate that answer set programming is efficient enough for being used in diagnosis applications, providing that the underlying system is of moderate size. For digital circuits having less than 500 components, diagnosis time has been less than one second even for computing triple fault diagnoses.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-022-03272-2