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Unifying Two Views on Multiple Mean-Payoff Objectives 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%2F15%3A00080917" target="_blank" >RIV/00216224:14330/15:00080917 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Unifying Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes

  • Original language description

    We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives. There exist two different views: (i)~the expectation semantics, where the goal is to optimize the expected mean-payoff objective, and (ii)~the satisfaction semantics, where the goal is to maximize the probability of runs such that the mean-payoff value stays above a given vector. We consider optimization with respect to both objectives at once, thus unifying the existing semantics. Precisely, the goal is to optimize the expectation while ensuring the satisfaction constraint. Our problem captures the notion of optimization with respect to strategies that are risk-averse (i.e., ensure certain probabilistic guarantee). Our main results are as follows: First, we present algorithms for the decision problems, which are always polynomial in the size of the MDP.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GBP202%2F12%2FG061" target="_blank" >GBP202/12/G061: Center of excellence - Institute for theoretical computer science (CE-ITI)</a><br>

  • Continuities

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

Others

  • Publication year

    2015

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Thirtieth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS)

  • ISBN

    9781479988754

  • ISSN

    1043-6871

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    244-256

  • Publisher name

    IEEE

  • Place of publication

    Los Alamitos, California

  • Event location

    Los Alamitos, California

  • Event date

    Jan 1, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article