Publication details

Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes



Year of publication 2018
Type Article in Proceedings
Conference Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS '18)
MU Faculty or unit

Faculty of Informatics

Keywords conditional value-at-risk; Markov chains; Markov decision processes; reachability; mean-payoff
Description 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.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info