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AAAI
2012

Learning Behavior Models for Hybrid Timed Systems

12 years 1 months ago
Learning Behavior Models for Hybrid Timed Systems
A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with the algorithmic learning of a system’s behavior model given a sample of observations. In particular, we consider real-world production plants where the learned model must capture timing behavior, dependencies between system variables, as well as mode switches—in short: hybrid system’s characteristics. Usually, such model formation tasks are solved by human engineers, entailing the well-known bunch of problems including knowledge acquisition, development cost, or lack of experience. Our contributions to the outlined field are as follows. (1) We present a taxonomy of learning problems related to model formation tasks. As a result, an important open learning problem for the domain of production system is identified: The learning of hybrid timed automata. (2) For this class of models, the learning algorithm HyBUT...
Oliver Niggemann, Benno Stein, Asmir Vodencarevic,
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where AAAI
Authors Oliver Niggemann, Benno Stein, Asmir Vodencarevic, Alexander Maier, Hans Kleine Büning
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