Paper Info
Title
Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes.
Abstract
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.
Year
DOI
Venue
2018
10.1145/3209108.3209176
LICS'18: PROCEEDINGS OF THE 33RD ANNUAL ACM/IEEE SYMPOSIUM ON LOGIC IN COMPUTER SCIENCE
DocType
Volume
Citations 
Conference
abs/1805.02946
0
PageRank 
References 
Authors
0.34
20
2
Name
Order
Citations
PageRank
Jan Kretínský115916.02
Tobias Meggendorfer2153.90