@misc{5549,
  abstract     = {This repository contains the experimental part of the CAV 2015 publication Counterexample Explanation by Learning Small Strategies in Markov Decision Processes.
We extended the probabilistic model checker PRISM to represent strategies of Markov Decision Processes as Decision Trees.
The archive contains a java executable version of the extended tool (prism_dectree.jar) together with a few examples of the PRISM benchmark library.
To execute the program, please have a look at the README.txt, which provides instructions and further information on the archive.
The archive contains scripts that (if run often enough) reproduces the data presented in the publication.},
  author       = {Fellner, Andreas},
  keywords     = {Markov Decision Process, Decision Tree, Probabilistic Verification, Counterexample Explanation},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Experimental part of CAV 2015 publication: Counterexample Explanation by Learning Small Strategies in Markov Decision Processes}},
  doi          = {10.15479/AT:ISTA:28},
  year         = {2015},
}

@inproceedings{1603,
  abstract     = {For deterministic systems, a counterexample to a property can simply be an error trace, whereas counterexamples in probabilistic systems are necessarily more complex. For instance, a set of erroneous traces with a sufficient cumulative probability mass can be used. Since these are too large objects to understand and manipulate, compact representations such as subchains have been considered. In the case of probabilistic systems with non-determinism, the situation is even more complex. While a subchain for a given strategy (or scheduler, resolving non-determinism) is a straightforward choice, we take a different approach. Instead, we focus on the strategy itself, and extract the most important decisions it makes, and present its succinct representation.
The key tools we employ to achieve this are (1) introducing a concept of importance of a state w.r.t. the strategy, and (2) learning using decision trees. There are three main consequent advantages of our approach. Firstly, it exploits the quantitative information on states, stressing the more important decisions. Secondly, it leads to a greater variability and degree of freedom in representing the strategies. Thirdly, the representation uses a self-explanatory data structure. In summary, our approach produces more succinct and more explainable strategies, as opposed to e.g. binary decision diagrams. Finally, our experimental results show that we can extract several rules describing the strategy even for very large systems that do not fit in memory, and based on the rules explain the erroneous behaviour.},
  author       = {Brázdil, Tomáš and Chatterjee, Krishnendu and Chmelik, Martin and Fellner, Andreas and Kretinsky, Jan},
  location     = {San Francisco, CA, United States},
  pages        = {158 -- 177},
  publisher    = {Springer},
  title        = {{Counterexample explanation by learning small strategies in Markov decision processes}},
  doi          = {10.1007/978-3-319-21690-4_10},
  volume       = {9206},
  year         = {2015},
}