par Bruyère, Véronique;Filiot, Emmanuel 
;Randour, Mickael ;Raskin, Jean-François
Référence Information and computation, 254, 2, page (259-295)
Publication Publié, 2017-06
;Randour, Mickael ;Raskin, Jean-François Référence Information and computation, 254, 2, page (259-295)
Publication Publié, 2017-06
Article révisé par les pairs
| Résumé : | Classical analysis of two-player quantitative games involves an adversary (modeling the environment of the system) which is purely antagonistic and asks for strict guarantees while Markov decision processes model systems facing a purely randomized environment: the aim is then to optimize the expected payoff, with no guarantee on individual outcomes. We introduce the beyond worst-case synthesis problem, which is to construct strategies that guarantee some quantitative requirement in the worst-case while providing a higher expected value against a particular stochastic model of the environment given as input. We study the beyond worst-case synthesis problem for two important quantitative settings: the mean-payoff and the shortest path. In both cases, we show how to decide the existence of finite-memory strategies satisfying the problem and how to synthesize one if one exists. We establish algorithms and we study complexity bounds and memory requirements. |
