Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Many systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.

CPSDebug: Automatic failure explanation in CPS models

Debugging cyber-physical system (CPS) models is a cumbersome and costly activity. CPS models combine continuous and discrete dynamics—a fault in a physical component manifests itself in a very different way than a fault in a state machine. Furthermore, faults can propagate both in time and space before they can be detected at the observable interface of the model. As a consequence, explaining the reason of an observed failure is challenging and often requires domain-specific knowledge. In this paper, we propose approach, a novel CPSDebug that combines testing, specification mining, and failure analysis, to automatically explain failures in Simulink/Stateflow models. In particular, we address the hybrid nature of CPS models by using different methods to infer properties from continuous and discrete state variables of the model. We evaluate CPSDebug on two case studies, involving two main scenarios and several classes of faults, demonstrating the potential value of our approach.