Controller synthesis and verification for Markov decision processes with qualitative branching time objectives

被引:0
|
作者
Brazdil, Tomas [1 ]
Forejt, Vojtech [1 ]
Kucera, Antonin [1 ]
机构
[1] Masaryk Univ, Fac Informat, Brno 60200, Czech Republic
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We show that the controller synthesis and verification problems for Markov decision processes with qualitative PECTL* objectives are 2-EXPTIME complete. More precisely, the algorithms are polynomial in the size of a given Markov decision process and doubly exponential in the size of a given qualitative PECTL* formula. Moreover, we show that if a given qualitative PECTL* objective is achievable by some strategy, then it is also achievable by an effectively constructible one-counter strategy, where the associated complexity bounds are essentially the same as above. For the fragment of qualitative PCTL objectives, we obtain EXPTIME completeness and the algorithms are only singly exponential in the size of the formula.
引用
收藏
页码:148 / 159
页数:12
相关论文
共 50 条
  • [31] Polynomial Time Algorithms for Branching Markov Decision Processes and Probabilistic Min(Max) Polynomial Bellman Equations
    Etessami, Kousha
    Stewart, Alistair
    Yannakakis, Mihalis
    AUTOMATA, LANGUAGES, AND PROGRAMMING, ICALP 2012 PT I, 2012, 7391 : 314 - 326
  • [32] Polynomial Time Algorithms for Branching Markov Decision Processes and Probabilistic Min (Max) Polynomial Bellman Equations
    Etessami, Kousha
    Stewart, Alistair
    Yannakakis, Mihalis
    MATHEMATICS OF OPERATIONS RESEARCH, 2020, 45 (01) : 34 - 62
  • [33] Verification of Markov Decision Processes with Risk-Sensitive Measures
    Cubuktepe, Murat
    Topcu, Ufuk
    2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 2371 - 2377
  • [34] Learning Weighted Assumptions for Compositional Verification of Markov Decision Processes
    He, Fei
    Gao, Xiaowei
    Wang, Miaofei
    Wang, Bow-Yaw
    Zhang, Lijun
    ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 2016, 25 (03)
  • [35] Model-Free Reinforcement Learning for Branching Markov Decision Processes
    Hahn, Ernst Moritz
    Perez, Mateo
    Schewe, Sven
    Somenzi, Fabio
    Trivedi, Ashutosh
    Wojtczak, Dominik
    COMPUTER AIDED VERIFICATION, PT II, CAV 2021, 2021, 12760 : 651 - 673
  • [36] Toward Implicit Learning for the Compositional Verification of Markov Decision Processes
    Bouchekir, Redouane
    Boukala, Mohand Cherif
    VERIFICATION AND EVALUATION OF COMPUTER AND COMMUNICATION SYSTEMS, 2018, 11181 : 200 - 217
  • [37] SOME RESULTS ON MULTITYPE CONTINUOUS TIME MARKOV BRANCHING PROCESSES
    ATHREYA, KB
    ANNALS OF MATHEMATICAL STATISTICS, 1968, 39 (02): : 347 - &
  • [38] Markov decision processes with a stopping time constraint
    Horiguchi, M
    MATHEMATICAL METHODS OF OPERATIONS RESEARCH, 2001, 53 (02) : 279 - 295
  • [39] A time aggregation approach to Markov decision processes
    Cao, XR
    Ren, ZY
    Bhatnagar, S
    Fu, M
    Marcus, S
    AUTOMATICA, 2002, 38 (06) : 929 - 943
  • [40] Some analytical properties of time inhomogeneous Markov branching processes
    Goryainov, V
    ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 1996, 76 : 439 - 440