Logic and Model Checking by Imprecise Probabilistic Interpreted Systems

Abstract

Stochastic multi-agent systems raise the necessity to extend probabilistic model checking to the epistemic domain. Results in this direction have been achieved by epistemic extensions of Probabilistic Computation Tree Logic and related Probabilistic Interpreted Systems. The latter, however, suffer of an important limitation: they require the probabilities governing the system's behaviour to be fully specified. A promising way to overcome this limitation is represented by imprecise probabilities. In this paper we introduce imprecise probabilistic interpreted systems and present a related logical language and model-checking procedures based on recent advances in the study of imprecise Markov processes.

Copying safety and liveness properties of computational artefacts

“…Future steps of this research will aim at extending the proposed ontological, epistemological, and formal analyses of the various relations of copy of physical artefacts for probabilistic systems (see e.g. Termine et al (2021a)) as they are implemented in AI technologies, e.g. in the digital twin vision in industry.…”