Gerasimou, Simos orcid.org/0000-0002-2706-5272, Camara Moreno, Javier orcid.org/0000-0001-6717-4775, Calinescu, Radu orcid.org/0000-0002-2678-9260 et al. (3 more authors) (2022) Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies. In: 36th IEEE/ACM International Conference on Automated Software Engineering. IEEE
Abstract
We present a new approach for synthesising Pareto- optimal 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EPSRC EP/V026747/1 |
Depositing User: | Pure (York) |
Date Deposited: | 27 Sep 2021 10:50 |
Last Modified: | 07 Mar 2025 00:02 |
Published Version: | https://doi.org/10.1109/ASE51524.2021.9678727 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ASE51524.2021.9678727 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178508 |
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Description: MDPPolicySynthesis-ASE2021