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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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