Variations on the Stochastic Shortest Path Problem

  • Mickael Randour
  • Jean-François Raskin
  • Ocan Sankur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8931)


In this invited contribution, we revisit the stochastic shortest path problem, and show how recent results allow one to improve over the classical solutions: we present algorithms to synthesize strategies with multiple guarantees on the distribution of the length of paths reaching a given target, rather than simply minimizing its expected value. The concepts and algorithms that we propose here are applications of more general results that have been obtained recently for Markov decision processes and that are described in a series of recent papers.


Short Path Polynomial Time Target State Markov Decision Process Short Path Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mickael Randour
    • 1
  • Jean-François Raskin
    • 2
  • Ocan Sankur
    • 2
  1. 1.LSV, CNRS & ENS CachanFrance
  2. 2.Département d’InformatiqueUniversité Libre de Bruxelles (U.L.B.)Belgium

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