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@inproceedings{1649039, author = {Křetínský, Jan and Meggendorfer, Tobias}, address = {New York, NY, USA}, booktitle = {Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS '18)}, doi = {http://dx.doi.org/10.1145/3209108.3209176}, keywords = {conditional value-at-risk; Markov chains; Markov decision processes; reachability; mean-payoff}, howpublished = {elektronická verze "online"}, language = {eng}, location = {New York, NY, USA}, isbn = {978-1-4503-5583-4}, pages = {609-618}, publisher = {ACM}, title = {Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes}, year = {2018} }
TY - JOUR ID - 1649039 AU - Křetínský, Jan - Meggendorfer, Tobias PY - 2018 TI - Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes PB - ACM CY - New York, NY, USA SN - 9781450355834 KW - conditional value-at-risk KW - Markov chains KW - Markov decision processes KW - reachability KW - mean-payoff N2 - We present the conditional value-at-risk (CVaR) in the context of Markov chains and Markov decision processes with reachability and mean-payoff objectives. CVaR quantifies risk by means of the expectation of the worst p-quantile. As such it can be used to design risk-averse systems. We consider not only CVaR constraints, but also introduce their conjunction with expectation constraints and quantile constraints (value-at-risk, VaR). We derive lower and upper bounds on the computational complexity of the respective decision problems and characterize the structure of the strategies in terms of memory and randomization. ER -
KŘETÍNSKÝ, Jan and Tobias MEGGENDORFER. Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes. Online. In \textit{Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS '18)}. New York, NY, USA: ACM, 2018, p.~609-618. ISBN~978-1-4503-5583-4. Available from: https://dx.doi.org/10.1145/3209108.3209176.
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