Compositional strategy synthesis for stochastic games with multiple objectives
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2012
- Type
- D - Journal article
- DOI
-
10.1016/j.ic.2017.09.010
- Title of journal
- Information and Computation
- Article number
- -
- First page
- 536
- Volume
- 261
- Issue
- 3
- ISSN
- 0890-5401
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2017
- 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
-
2
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the journal version of conference papers that appeared in CONCUR�14 and TACAS�15, which developed novel algorithms for stochastic games and established their complexity. This work enabled, for the first time, scalable compositional automated strategy synthesis that guarantees the satisfaction of multi-objective mean payoff properties. The techniques, presented in keynotes (ICALP�16 and ECC�16), were implemented in the software tool PRISM-games 2.0 (https://www.prismmodelchecker.org/games/), and used to support the development of autonomous transport and energy management systems, including human-in-the-loop UAV mission planning (see https://doi.org/10.1109/TASE.2016.2530623; https://doi.org/10.1109/TASE.2016.2530623) and maximising uptime in an aircraft electrical power network (https://doi.org/10.1007/978-3-662-46681-0_22), both with University of Pennsylvania.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -