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Parametric and Reconfigurable Markov Chains. CoRR, abs/1804.01872, 2018.
42. Chin Pang Ho and Marek Petrik. Fast Bellman Updates for Robust MDPs. In
ICML, 2018.
43. Yamilet R. Serrano Llerena, Marcel B¨ ohme, Marc Br¨ unink, Guoxin Su, and David S.
Rosenblum. Verifying the Long-run Behavior of Probabilistic System Models in the
Presence of Uncertainty. In ESEC/SIGSOFT FSE, pages 587–597. ACM, 2018.
44. Lauren N Steimle, David L Kaufman, and Brian T Denton. Multi-Model Markov
Decision Processes. Optimization Online, 2018.
45. Giovanni Bacci, Mikkel Hansen, and Kim Guldstrand Larsen. Model Checking
Constrained Markov Reward Models with Uncertainties. In QEST, pages 37–51,
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46. Sebastian Junges, Erika ´
Abrah´ am, Christian Hensel, Nils Jansen, Joost-Pieter
Katoen, Tim Quatmann, and Matthias Volk. Parameter Synthesis for Markov
Models. CoRR, abs/1903.07993, 2019.
47. Tobias Winkler, Sebastian Junges, Guillermo A. P´ erez, and Joost-Pieter Katoen.
On the complexity of reachability in parametric markov decision processes. In
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48. Christel Baier and Joost-Pieter Katoen. Principles of Model Checking. MIT Press,
2008.
49. S. Basu, R. Pollack, and M.F. Roy. Algorithms in Real Algebraic Geometry. Springer,
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50. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004.
51. Martin L Puterman. Markov Decision Processes: Discrete Stochastic Dynamic
Programming. John Wiley & Sons, 2014.
52. Stuart J Russell and Peter Norvig. Artificial Intelligence: A Modern Approach.
2016.
53. Richard S Sutton and Andrew G Barto. Reinforcement Learning: An Introduction.
MIT press, 2018.
Open Access This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/),
which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s
Creative Commons license, unless indicated otherwise in a credit line to the material. If
material is not included in the chapter’s Creative Commons license and your intended
use is not permitted by statutory regulation or exceeds the permitted use, you will need
to obtain permission directly from the copyright holder.
305
40. Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen, and Ufuk
Topcu. Synthesis in pmdps: A tale of 1001 parameters. In ATVA, volume 11138 of
LNCS, pages 160–176. Springer, 2018.
41. Paul Gainer, Ernst Moritz Hahn, and Sven Schewe. Incremental Verification of
Parametric and Reconfigurable Markov Chains. CoRR, abs/1804.01872, 2018.
42. Chin Pang Ho and Marek Petrik. Fast Bellman Updates for Robust MDPs. In
ICML, 2018.
43. Yamilet R. Serrano Llerena, Marcel B¨ ohme, Marc Br¨ unink, Guoxin Su, and David S.
Rosenblum. Verifying the Long-run Behavior of Probabilistic System Models in the
Presence of Uncertainty. In ESEC/SIGSOFT FSE, pages 587–597. ACM, 2018.
44. Lauren N Steimle, David L Kaufman, and Brian T Denton. Multi-Model Markov
Decision Processes. Optimization Online, 2018.
45. Giovanni Bacci, Mikkel Hansen, and Kim Guldstrand Larsen. Model Checking
Constrained Markov Reward Models with Uncertainties. In QEST, pages 37–51,
2019.
46. Sebastian Junges, Erika ´
Abrah´ am, Christian Hensel, Nils Jansen, Joost-Pieter
Katoen, Tim Quatmann, and Matthias Volk. Parameter Synthesis for Markov
Models. CoRR, abs/1903.07993, 2019.
47. Tobias Winkler, Sebastian Junges, Guillermo A. P´ erez, and Joost-Pieter Katoen.
On the complexity of reachability in parametric markov decision processes. In
CONCUR, volume 140 of LIPIcs, pages 14:1–14:17. Schloss Dagstuhl - LeibnizZentrum f¨ ur Informatik, 2019.
48. Christel Baier and Joost-Pieter Katoen. Principles of Model Checking. MIT Press,
2008.
49. S. Basu, R. Pollack, and M.F. Roy. Algorithms in Real Algebraic Geometry. Springer,
2010.
50. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004.
51. Martin L Puterman. Markov Decision Processes: Discrete Stochastic Dynamic
Programming. John Wiley & Sons, 2014.
52. Stuart J Russell and Peter Norvig. Artificial Intelligence: A Modern Approach.
2016.
53. Richard S Sutton and Andrew G Barto. Reinforcement Learning: An Introduction.
MIT press, 2018.
Open Access This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/),
which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s
Creative Commons license, unless indicated otherwise in a credit line to the material. If
material is not included in the chapter’s Creative Commons license and your intended
use is not permitted by statutory regulation or exceeds the permitted use, you will need
to obtain permission directly from the copyright holder.