Many complex systems today, such as robotic networks, automobiles and automated factories, consist of hard- ware components whose functionality is extended or con- trolled by embedded software and which exhibit contin- uous dynamics. We address the problem of monitoring and control in such systems with a twofold contribution. First, we extend Probabilistic Hierarchical Constraint Au- tomata (PHCA), introduced in previous work as a means to compactly describe uncertain hardware and complex software behavior, to hybrid PHCA (HyPHCA). These al- low to model continuous behavior in the form of differ- ential equations. Continuous behavior can be conserva- tively approximated with discrete Markov chains, and in previous work we showed how to transform PHCA moni- toring into a constraint optimization problem that can be solved using off-the-shelf reasoners. Our second contri- bution is to show how to combine these and additional known methods to use a HyPHCA to monitor the internal state and plan for contingencies in a rich class of mixed hardware/software, discrete/continuous systems. Prelimi- nary results of our approach for an industrial filling sta- tion scenario demonstrate its feasibility.     «

Many complex systems today, such as robotic networks, automobiles and automated factories, consist of hard- ware components whose functionality is extended or con- trolled by embedded software and which exhibit contin- uous dynamics. We address the problem of monitoring and control in such systems with a twofold contribution. First, we extend Probabilistic Hierarchical Constraint Au- tomata (PHCA), introduced in previous work as a means to compactly describe uncertain hardware and complex softwa...     »