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• W3217383739 abstract "We present a new approach for synthesising Paretooptimal Markov decision process (MDP) policies that satisfy complex combinations of quality-of-service (QoS) software requirements. These policies correspond to optimal designs or configurations of software systems, and are obtained by translating MDP models of these systems into parametric Markov chains, and using multi-objective genetic algorithms to synthesise Pareto-optimal parameter values that define the required MDP policies. We use case studies from the service-based systems and robotic control software domains to show that our MDP policy synthesis approach can handle a wide range of QoS requirement combinations unsupported by current probabilistic model checkers. Moreover, for requirement combinations supported by these model checkers, our approach generates better Pareto-optimal policy sets according to established quality metrics." @default.
• W3217383739 created "2021-12-06" @default.
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• W3217383739 date "2021-11-01" @default.
• W3217383739 modified "2023-09-26" @default.
• W3217383739 title "Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies" @default.
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• W3217383739 doi "https://doi.org/10.1109/ase51524.2021.9678727" @default.
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