Diagnostics of Distributed Intelligent Control Systems: Reasoning Using Ontologies and Hidden Markov Models

Abstract The distributed intelligent control systems based on multi-agent systems paradigm bring many important features, including their flexibility and extensibility. These features are even more apparent when the agents use ontologies as a base for their knowledge management – such ontologies can be regarded as models that to some degree drive the operation of the system. However, much of the information from knowledge bases of agents can be used also for other important ability of a control system – the diagnostics. In this paper, we demonstrate and discuss two approaches to diagnostics – one based on description logic reasoning and the other one based on Hidden Markov Models. Both of these approaches are illustrated on sample scenario from a transportation system.