---
res:
  bibo_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.\r\nThe 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.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Tomáš
      foaf_name: Brázdil, Tomáš
      foaf_surname: Brázdil
  - foaf_Person:
      foaf_givenName: Krishnendu
      foaf_name: Chatterjee, Krishnendu
      foaf_surname: Chatterjee
      foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-4561-241X
  - foaf_Person:
      foaf_givenName: Martin
      foaf_name: Chmelik, Martin
      foaf_surname: Chmelik
      foaf_workInfoHomepage: http://www.librecat.org/personId=3624234E-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Andreas
      foaf_name: Fellner, Andreas
      foaf_surname: Fellner
      foaf_workInfoHomepage: http://www.librecat.org/personId=42BABFB4-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Jan
      foaf_name: Kretinsky, Jan
      foaf_surname: Kretinsky
      foaf_workInfoHomepage: http://www.librecat.org/personId=44CEF464-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-8122-2881
  bibo_doi: 10.1007/978-3-319-21690-4_10
  bibo_volume: 9206
  dct_date: 2015^xs_gYear
  dct_identifier:
  - UT:000364182900010
  dct_language: eng
  dct_publisher: Springer@
  dct_title: Counterexample explanation by learning small strategies in Markov decision
    processes@
...