SEA-PARAM: Exploring Schedulers in Parametric MDPs
| Autor: | Ana Sokolova, Ezio Bartocci, Sebastian Arming |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Mathematical optimization Computer Science - Logic in Computer Science Computer science lcsh:Mathematics PARAM 020207 software engineering 02 engineering and technology lcsh:QA1-939 lcsh:QA75.5-76.95 Logic in Computer Science (cs.LO) Range (mathematics) Reachability Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering lcsh:Electronic computers. Computer science Markov decision process Focus (optics) Parametric statistics |
| Zdroj: | QAPL@ETAPS Electronic Proceedings in Theoretical Computer Science, Vol 250, Iss Proc. QAPL 2017, Pp 25-38 (2017) |
| Popis: | We study parametric Markov decision processes (PMDPs) and their reachability probabilities "independent" of the parameters. Different to existing work on parameter synthesis (implemented in the tools PARAM and PRISM), our main focus is on describing different types of optimal deterministic memoryless schedulers for the whole parameter range. We implement a simple prototype tool SEA-PARAM that computes these optimal schedulers and show experimental results. In Proceedings QAPL 2017, arXiv:1707.03668 |
| Databáze: | OpenAIRE |
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