---
res:
  bibo_abstract:
  - 'The theory of graph games with ω-regular winning conditions is the foundation
    for modeling and synthesizing reactive processes. In the case of stochastic reactive
    processes, the corresponding stochastic graph games have three players, two of
    them (System and Environment) behaving adversarially, and the third (Uncertainty)
    behaving probabilistically. We consider two problems for stochastic graph games:
    the qualitative problem asks for the set of states from which a player can win
    with probability 1 (almost-sure winning); and the quantitative problem asks for
    the maximal probability of winning (optimal winning) from each state. We consider
    ω-regular winning conditions formalized as Müller winning conditions. We present
    optimal memory bounds for pure almost-sure winning and optimal winning strategies
    in stochastic graph games with Müller winning conditions. We also present improved
    memory bounds for randomized almost-sure winning and optimal strategies.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Krishnendu
      foaf_name: Krishnendu Chatterjee
      foaf_surname: Chatterjee
      foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-4561-241X
  bibo_doi: 10.1007/978-3-540-71389-0_11
  bibo_volume: 4423
  dct_date: 2007^xs_gYear
  dct_publisher: Springer@
  dct_title: Optimal strategy synthesis in stochastic Müller games@
...