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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00108288" target="_blank" >RIV/00216224:14330/18:00108288 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1145/3209108.3209176" target="_blank" >http://dx.doi.org/10.1145/3209108.3209176</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3209108.3209176" target="_blank" >10.1145/3209108.3209176</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language 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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA18-11193S" target="_blank" >GA18-11193S: Algorithms for Infinite-State Discrete Systems and Games</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS '18)

  • ISBN

    9781450355834

  • ISSN

    1043-6871

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    609-618

  • Publisher name

    ACM

  • Place of publication

    New York, NY, USA

  • Event location

    New York, NY, USA

  • Event date

    Jan 1, 2018

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article