Efficient Synthesis of Robust Models for Stochastic Systems
- Submitting institution
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University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 62777346
- Type
- D - Journal article
- DOI
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10.1016/j.jss.2018.05.013
- Title of journal
- Journal of Systems and Software
- Article number
- -
- First page
- 140
- Volume
- 143
- Issue
- -
- ISSN
- 0164-1212
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- 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)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This collaboration between the Universities of York and Oxford pioneered the concept of sensitivity-aware Pareto dominance. It established a new line of research for synthesising robust Pareto-optimal Markov models, delivering one of four pillars of a £307K ORCA Hub/EPSRC grant (PI Calinescu, https://orcahub.org/engagement/partnership-fund/cove). It impacted on recent research in the areas of parameter synthesis for Markov models (Junges et al., Aachen) and counterexample-driven synthesis for probabilistic programs (Ceska et al., Brno/Aachen).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -