An iterative decision-making scheme for Markov decision processes and its application to self-adaptive systems
- Submitting institution
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Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1493
- Type
- E - Conference contribution
- DOI
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10.1007/978-3-662-49665-7_16
- Title of conference / published proceedings
- Fundamental Approaches to Software Engineering: 19th International Conference, FASE 2016, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2016, Eindhoven, The Netherlands, April 2-8, 2016, Proceedings
- First page
- 269
- Volume
- -
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- URL
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http://eprints.mdx.ac.uk/19199/
- Supplementary information
-
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes the development of an Iterative Decision-Making Scheme that infers both point and interval estimates for the undetermined transition probabilities in a Markov Decision Process based on sampled data. It iteratively computes a confidently optimal scheduler from a given finite subset of schedulers. This paper is significant because it supports a tradeoff among three important metrics in practical runtime decision-making problems: accuracy, data usage and computational overhead. This supports the development of optimal schedulers that adapts to runtime phenomena. This led to the EPSRC grant Perturbation Analysis for Probabilistic Verification EP/P00430X/2.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -