In this paper, we consider multi-dimensional maximal cost-bounded reachability probability over continuous-time Markov decision processes (CTMDPs). Our major contributions are as follows. Firstly, we derive an integral characterization which states that the maximal cost-bounded reachability probability function is the least fixed-point of a system of integral equations. Secondly, we prove that the maximal cost-bounded reachability probability can be attained by a measurable deterministic cost-positional scheduler. Thirdly, we provide a numerical approximation algorithm for maximal cost-bounded reachability probability. We present these results under the setting of both early and late schedulers. Besides, we correct a fundamental proof error in the PhD Thesis by Martin Neuhäußer on maximal time-bounded reachability probability by completely new proofs for the more general case of multi-dimensional maximal cost-bounded reachability probability. © 2014 Springer-Verlag.
CITATION STYLE
Fu, H. (2014). Maximal cost-bounded reachability probability on continuous-time Markov decision processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8412 LNCS, pp. 73–87). Springer Verlag. https://doi.org/10.1007/978-3-642-54830-7_5
Mendeley helps you to discover research relevant for your work.