Efficient qualitative analysis of classes of recursive markov decision processes and simple stochastic games

  • Authors:
  • Kousha Etessami;Mihalis Yannakakis

  • Affiliations:
  • LFCS, School of Informatics, University of Edinburgh;Department of Computer Science, Columbia University

  • Venue:
  • STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
  • Year:
  • 2006

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Abstract

Recursive Markov Decision Processes (RMDPs) and Recursive Simple Stochastic Games (RSSGs) are natural models for recursive systems involving both probabilistic and non-probabilistic actions. As shown recently [10], fundamental problems about such models, e.g., termination, are undecidable in general, but decidable for the important class of 1-exit RMDPs and RSSGs. These capture controlled and game versions of multi-type Branching Processes, an important and well-studied class of stochastic processes. In this paper we provide efficient algorithms for the qualitative termination problem for these models: does the process terminate almost surely when the players use their optimal strategies? Polynomial time algorithms are given for both maximizing and minimizing 1-exit RMDPs (the two cases are not symmetric). For 1-exit RSSGs the problem is in NP∩coNP, and furthermore, it is at least as hard as other well-known NP∩coNP problems on games, e.g., Condon's quantitative termination problem for finite SSGs ([3]). For the class of linearly-recursive 1-exit RSSGs, we show that the problem can be solved in polynomial time.