References

  1. Rajeev Alur, Thomas A. Henzinger, Orna Kupferman & Moshe Y. Vardi (1998): Alternating Refinement Relations. In: CONCUR, LNCS 1466. Springer, pp. 163–178. Available at http://dx.doi.org/10.1007/BFb0055622.
  2. Michael Benedikt, Rastislav Lenhardt & James Worrell (2013): LTL Model Checking of Interval Markov Chains. In: TACAS, LNCS 7795. Springer, pp. 32–46. Available at http://dx.doi.org/10.1007/978-3-642-36742-7_3.
  3. Patrick Billingsley (1979): Probability and Measure. John Wiley and Sons, New York, Toronto, London.
  4. Stefano Cattani & Roberto Segala (2002): Decision Algorithms for Probabilistic Bisimulation. In: CONCUR, LNCS 2421, pp. 371–385. Available at http://dx.doi.org/10.1007/3-540-45694-5_25.
  5. Donald R. Chand & Sham S. Kapur (1970): An Algorithm for Convex Polytopes. J. ACM 17(1), pp. 78–86. Available at http://dx.doi.org/10.1145/321556.321564.
  6. Krishnendu Chatterjee, Siddhesh Chaubal & Pritish Kamath (2012): Faster Algorithms for Alternating Refinement Relations. In: CSL, LIPIcs 16. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, pp. 167–182. Available at http://dx.doi.org/10.4230/LIPIcs.CSL.2012.167.
  7. Krishnendu Chatterjee, Koushik Sen & Thomas A. Henzinger (2008): Model-Checking omega-Regular Properties of Interval Markov Chains. In: FoSSaCS, LNCS 4962. Springer, pp. 302–317. Available at http://dx.doi.org/10.1007/978-3-540-78499-9_22.
  8. Taolue Chen, Tingting Han & Marta Z. Kwiatkowska (2013): On the complexity of model checking interval-valued discrete time Markov chains. Inf. Process. Lett. 113(7), pp. 210–216. Available at http://dx.doi.org/10.1016/j.ipl.2013.01.004.
  9. Benoît Delahaye, Joost-Pieter Katoen, Kim G. Larsen, Axel Legay, Mikkel L. Pedersen, Falak Sher & Andrzej Wasowski (2011): Abstract Probabilistic Automata. In: VMCAI, LNCS 6538. Springer, pp. 324–339. Available at http://dx.doi.org/10.1007/978-3-642-18275-4_23.
  10. Benoît Delahaye, Joost-Pieter Katoen, Kim G. Larsen, Axel Legay, Mikkel L. Pedersen, Falak Sher & Andrzej Wasowski (2011): New Results on Abstract Probabilistic Automata. In: ACSD. IEEE, pp. 118–127. Available at http://doi.ieeecomputersociety.org/10.1109/ACSD.2011.10.
  11. Benoît Delahaye, Kim G. Larsen, Axel Legay, Mikkel L. Pedersen & Andrzej Wasowski (2011): Decision Problems for Interval Markov Chains. In: LATA, LNCS 6638. Springer, pp. 274–285. Available at http://dx.doi.org/10.1007/978-3-642-21254-3_21.
  12. Harald Fecher, Martin Leucker & Verena Wolf (2006): Don't Know in Probabilistic Systems. In: SPIN, LNCS 3925. Springer, pp. 71–88. Available at http://dx.doi.org/10.1007/11691617_5.
  13. Robert Givan, Sonia M. Leach & Thomas L. Dean (2000): Bounded-parameter Markov decision processes. Artif. Intell. 122(1-2), pp. 71–109. Available at http://dx.doi.org/10.1016/S0004-3702(00)00047-3.
  14. Ernst Moritz Hahn, Tingting Han & Lijun Zhang (2011): Synthesis for PCTL in Parametric Markov Decision Processes. In: NASA Formal Methods, LNCS 6617. Springer, pp. 146–161. Available at http://dx.doi.org/10.1007/978-3-642-20398-5_12.
  15. Hans Hansson & Bengt Jonsson (1994): A Logic for Reasoning about Time and Reliability. Formal Asp. Comput. 6(5), pp. 512–535. Available at http://dx.doi.org/10.1007/BF01211866.
  16. Vahid Hashemi, Hassan Hatefi & Jan Krčál (2014): Probabilistic Bisimulations for PCTL Model Checking of Interval MDPs. AVACS Technical Report No. 97. SFB/TR 14 AVACS. ISSN: 1860-9821, http://www.avacs.org..
  17. Garud N. Iyengar (2005): Robust Dynamic Programming. Math. Oper. Res. 30(2), pp. 257–280. Available at http://dx.doi.org/10.1287/moor.1040.0129.
  18. Bengt Jonsson & Kim Guldstrand Larsen (1991): Specification and Refinement of Probabilistic Processes. In: LICS. IEEE Computer Society, pp. 266–277. Available at http://dx.doi.org/10.1109/LICS.1991.151651.
  19. Paris C. Kanellakis & Scott A. Smolka (1990): CCS Expressions, Finite State Processes, and Three Problems of Equivalence. Inf. Comput. 86(1), pp. 43–68. Available at http://dx.doi.org/10.1016/0890-5401(90)90025-D.
  20. Joost-Pieter Katoen, Daniel Klink & Martin R. Neuhäußer (2009): Compositional Abstraction for Stochastic Systems. In: FORMATS, LNCS 5813. Springer, pp. 195–211. Available at http://dx.doi.org/10.1007/978-3-642-04368-0_16.
  21. Joost-Pieter Katoen, Jaco van de Pol, Mariëlle Stoelinga & Mark Timmer (2010): A Linear Process-Algebraic Format for Probabilistic Systems with Data. In: ACSD. IEEE Computer Society, pp. 213–222. Available at http://doi.ieeecomputersociety.org/10.1109/ACSD.2010.18.
  22. Igor Kozine & Lev V. Utkin (2002): Interval-Valued Finite Markov Chains. Reliable Computing 8(2), pp. 97–113. Available at http://dx.doi.org/10.1023/A:1014745904458.
  23. Kim Guldstrand Larsen & Arne Skou (1991): Bisimulation through Probabilistic Testing. Inf. Comput. 94(1), pp. 1–28. Available at http://dx.doi.org/10.1016/0890-5401(91)90030-6.
  24. Robin Milner (1989): Communication and concurrency. PHI Series in computer science. Prentice Hall.
  25. Arnab Nilim & Laurent El Ghaoui (2005): Robust Control of Markov Decision Processes with Uncertain Transition Matrices. Operations Research 53(5), pp. 780–798. Available at http://dx.doi.org/10.1287/opre.1050.0216.
  26. Robert Paige & Robert E. Tarjan (1987): Three Partition Refinement Algorithms. SIAM J. on Computing 16(6), pp. 973–989. Available at http://dx.doi.org/10.1137/0216062.
  27. Amir Pnueli (1977): The Temporal Logic of Programs. In: FOCS. IEEE Computer Society, pp. 46–57. Available at http://doi.ieeecomputersociety.org/10.1109/SFCS.1977.32.
  28. Alberto Puggelli, Wenchao Li, Alberto L. Sangiovanni-Vincentelli & Sanjit A. Seshia (2013): Polynomial-Time Verification of PCTL Properties of MDPs with Convex Uncertainties. In: CAV, LNCS 8044. Springer, pp. 527–542. Available at http://dx.doi.org/10.1007/978-3-642-39799-8_35.
  29. Martin L. Puterman (1994): Markov Decision Processes: Discrete Stochastic Dynamic Programming, 1st edition. John Wiley & Sons, Inc., New York, NY, USA. Available at http://dx.doi.org/10.1002/9780470316887.
  30. Matthias Ringwald (2009): Reducing Uncertainty in Wireless Sensor Networks - Network Inspection and Collision-Free Medium Access. ETH Zurich, Zurich, Switzerland.
  31. Roberto Segala (1995): Modeling and Verification of Randomized Distributed Real-Time Systems. Laboratory for Computer Science, Massachusetts Institute of Technology.
  32. Roberto Segala & Nancy A. Lynch (1994): Probabilistic Simulations for Probabilistic Processes. In: CONCUR, LNCS 836, pp. 481–496. Available at http://dx.doi.org/10.1007/BFb0015027.
  33. Koushik Sen, Mahesh Viswanathan & Gul Agha (2006): Model-Checking Markov Chains in the Presence of Uncertainties. In: TACAS, LNCS 3920. Springer, pp. 394–410. Available at http://dx.doi.org/10.1007/11691372_26.
  34. K. Subramani (2009): On the Complexities of Selected Satisfiability and Equivalence Queries over Boolean Formulas and Inclusion Queries over Hulls. JAMDS 2009. Available at http://dx.doi.org/10.1155/2009/845804.
  35. Mark Timmer (2011): SCOOP: A Tool for SymboliC Optimisations of Probabilistic Processes. In: QEST. IEEE Computer Society, pp. 149–150. Available at http://doi.ieeecomputersociety.org/10.1109/QEST.2011.27.
  36. Eric M. Wolff, Ufuk Topcu & Richard M. Murray (2012): Robust control of uncertain Markov Decision Processes with temporal logic specifications. In: CDC. IEEE, pp. 3372–3379. Available at http://dx.doi.org/10.1109/CDC.2012.6426174.
  37. Di Wu & Xenofon D. Koutsoukos (2008): Reachability analysis of uncertain systems using bounded-parameter Markov decision processes. Artif. Intell. 172(8-9), pp. 945–954. Available at http://dx.doi.org/10.1016/j.artint.2007.12.002.
  38. Wang Yi (1994): Algebraic Reasoning for Real-Time Probabilistic Processes with Uncertain Information. In: FTRTFT, LNCS 863. Springer, pp. 680–693. Available at http://dx.doi.org/10.1007/3-540-58468-4_190.
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