<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
         xmlns:dc="http://purl.org/dc/terms/"
         xmlns:foaf="http://xmlns.com/foaf/0.1/"
         xmlns:bibo="http://purl.org/ontology/bibo/"
         xmlns:fabio="http://purl.org/spar/fabio/"
         xmlns:owl="http://www.w3.org/2002/07/owl#"
         xmlns:event="http://purl.org/NET/c4dm/event.owl#"
         xmlns:ore="http://www.openarchives.org/ore/terms/">

    <rdf:Description rdf:about="https://research-explorer.ista.ac.at/record/5420">
        <ore:isDescribedBy rdf:resource="https://research-explorer.ista.ac.at/record/5420"/>
        <dc:title>The value 1 problem for concurrent mean-payoff games</dc:title>
        <bibo:authorList rdf:parseType="Collection">
            <foaf:Person>
                <foaf:name></foaf:name>
                <foaf:surname></foaf:surname>
                <foaf:givenname></foaf:givenname>
            </foaf:Person>
            <foaf:Person>
                <foaf:name></foaf:name>
                <foaf:surname></foaf:surname>
                <foaf:givenname></foaf:givenname>
            </foaf:Person>
        </bibo:authorList>
        <bibo:abstract>We consider concurrent mean-payoff games, a very well-studied class of two-player (player 1 vs player 2) zero-sum games on finite-state graphs where every transition is assigned a reward between 0 and 1, and the payoff function is the long-run average of the rewards. The value is the maximal expected payoff that player 1 can guarantee against all strategies of player 2. We consider the computation of the set of states with value 1 under finite-memory strategies for player 1, and our main results for the problem are as follows: (1) we present a polynomial-time algorithm; (2) we show that whenever there is a finite-memory strategy, there is a stationary strategy that does not need memory at all; and (3) we present an optimal bound (which is double exponential) on the patience of stationary strategies (where patience of a distribution is the inverse of the smallest positive probability and represents a complexity measure of a stationary strategy).</bibo:abstract>
        <bibo:startPage>49</bibo:startPage>
        <bibo:endPage>49</bibo:endPage>
        <dc:publisher>IST Austria</dc:publisher>
        <dc:format>application/pdf</dc:format>
        <ore:aggregates rdf:resource="https://research-explorer.ista.ac.at/download/5420/5520/IST-2014-191-v1%2B1_main_full.pdf"/>
        <bibo:doi rdf:resource="10.15479/AT:IST-2014-191-v1-1" />
        <ore:similarTo rdf:resource="info:doi/10.15479/AT:IST-2014-191-v1-1"/>
    </rdf:Description>
</rdf:RDF>
