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Implicit Reward Structures for Implicit Reliability Models
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AI
DOI：10.1109/TR.2022.3190915.png)
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Abstract
En 中文
A new methodology for effective definition and efficient evaluation of dependability-related properties is proposed. The analysis targets the systems composed of a large number of components, each one modeled implicitly through high-level formalisms, such as stochastic Petri nets. Since the component models are implicit, the reward structure that characterizes the dependability properties has to be implicit as well. Therefore, we present a new formalism to specify those reward structures. The focus here is on component models that can be mapped to stochastic automata with one or several absorbing states so that the system model can be mapped to a stochastic automata network with one or several absorbing states. Correspondingly, the new reward structure defined on each component's model is mapped to a reward vector so that the dependability-related properties of the system are expressed through a newly introduced measure defined starting from those reward vectors. A simple, yet representative, case study is adopted to show the feasibility of the method.
Keywords:
Markov processes
Transient analysis
Synchronization
Petri nets
Context modeling
Analytical models
Absorption
Markov process
implicit modeling
reliability modeling
tensor trains
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