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Probabilistic Model Checking and Autonomy
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AI
DOI：10.1146/annurev-control-042820-010947.png)
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摘要
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The design and control of autonomous systems that operate in uncertain or adversarial environments can be facilitated by formal modeling and analysis. Probabilistic model checking is a technique to automatically verify, for a given temporal logic specification, that a system model satisfies the specification, as well as to synthesize an optimal strategy for its control. This method has recently been extended to multiagent systems that exhibit competitive or cooperative behavior modeled via stochastic games and synthesis of equilibria strategies. In this article, we provide an overview of probabilistic model checking, focusing on models supported by the PRISM and PRISM-games model checkers. This overview includes fully observable and partially observable Markov decision processes, as well as turn-based and concurrent stochastic games, together with associated probabilistic temporal logics. We demonstrate the applicability of the framework through illustrative examples from autonomous systems. Finally, we highlight research challenges and suggest directions for future work in this area.
Keyword:
probabilistic modeling
temporal logic
model checking
strategy synthesis
stochastic games
equilibria
期刊
A
IF:
14
论文数:
98
被引数:
2.3K
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引用论文
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