Costs and rewards are important ingredients for cyberphysical systems, modelling critical aspects like energy consumption, task completion, repair costs, and memory usage. This paper introduces Markov reward automata, an extension of Markov automata that allows the modelling of systems incorporating rewards (or costs) in addition to nondeterminism, discrete probabilistic choice and continuous stochastic timing. Rewards come in two flavours: action rewards, acquired instantaneously when taking a transition; and state rewards, acquired while residing in a state. We present algorithms to optimise three reward functions: the expected accumulative reward until a goal is reached; the expected accumulative reward until a certain time bound; and the long-run average reward. We have implemented these algorithms in the SCOOP/IMCA tool chain and show their feasibility via several case studies.
|Place of Publication||Enschede|
|Publisher||Centre for Telematics and Information Technology (CTIT)|
|Number of pages||35|
|Publication status||Published - 2014|
|Name||CTIT Technical Report Series|
|Publisher||University of Twente, Centre for Telematics and Information Technology (CTIT)|
- EC Grant Agreement nr.: FP7/295261
- Process Algebra
- Quantitative analysis
- EC Grant Agreement nr.: FP7/318490
- Markov Automata
- EC Grant Agreement nr.: FP7/2007-2013
Guck, D., Timmer, M., Hatefi, H., Ruijters, E. J. J., & Stoelinga, M. I. A. (2014). Modelling and analysis of Markov reward automata (extended version). (CTIT Technical Report Series; No. TR-CTIT-14-06). Enschede: Centre for Telematics and Information Technology (CTIT).