Name
Abstract | ||
|---|---|---|
(Priced) timed games are two-player quantitative games involving an environment assumed to be completely antogonistic. Classical analysis consists in the synthesis of strategies ensuring safety, time-bounded or cost-bounded reachability objectives. Assuming a randomized environment, the (priced) timed game essentially defines an infinite-state Markov (reward) decision proces. In this setting the objective is classically to find a strategy that will minimize the expected reachability cost, but with no guarantees on worst-case behaviour. In this paper, we provide efficient methods for computing reachability strategies that will both ensure worst case time-bounds as well as provide (near-) minimal expected cost. Our method extends the synthesis algorithms of the synthesis tool Uppaal-Tiga with suitable adapted reinforcement learning techniques, that exhibits several orders of magnitude improvements w.r.t. previously known automated methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11936-6_10 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 8837 | 0302-9743 |
Citations | PageRank | References |
10 | 0.58 | 24 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre David | 1 | 1667 | 76.52 |
| Peter Gjøl Jensen | 2 | 32 | 9.38 |
| Kim Guldstrand Larsen | 3 | 4434 | 346.88 |
| Axel Legay | 4 | 2982 | 181.47 |
| didier lime | 5 | 787 | 46.02 |
| Mathias Grund Sørensen | 6 | 25 | 2.27 |
| Jakob Haahr Taankvist | 7 | 34 | 4.23 |