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...

• Type: Journal
• Year: 2012
• Where: AAAI
• Authors: Oliver Niggemann, Benno Stein, Asmir Vodencarevic, Alexander Maier, Hans Kleine Büning
• Keywords: AAAI 2012, Anomaly Detection, Intelligent Agents, Learned Model, Mode Switches