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- Type of Document: Ph.D. Dissertation
- Language: Farsi
- Document No: 53116 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Movaghar Rahimabadi, Ali
- Abstract:
- Continuous-time Stochastic Game (CTSG) can be seen as a proper model to analyze probabilistic, nondeterministic, and competitive behaviors as found in Stochastic Multi-Player Game (SMG). The difference is that in an SMG, the system under design is analyzed in the discrete-time setting. In comparison, in a CTSG, the system transitions occur in the continuous-time setting with exponentially distributed delays. This thesis focuses on the model checking of CTSGs with bounded transition rates. We present a uniformization-based algorithm to approximate the optimal time-bounded reachability probabilities with an arbitrary error bound. To illustrate the strength points of our algorithm, we compare our results with the ones from an existing algorithm which is based on discretization. Moreover, the thesis presents a new algorithm to approximate the optimal steady-state probability, the optimal probability of being in target states in the long-run, for CTSGs in which every state is almost surely reachable under all strategies. We address the problem for cases with the singleton and non-singleton set of target states, respectively and compare our experimental results with ones from an existing algorithm approximating the optimal expected long-run average reward in discrete-time stochastic games. Experimental results demonstrate the efficiency of our algorithms. We extend an existing logic to be able to reason about the optimal steady-state probability and the optimal time-bounded reachability probability under a coalition of players in a CTSG. Also, two case studies are presented investigating AIDS progression and bitcoinNG protocol
- Keywords:
- Stochastic Behavior ; Steady-State Probability ; Probabilistic Model Checking ; Continuous-Time Stochastic Game (CTSG) ; Competitive Behavior ; Time Bounded Reachability Probability