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

Kód výsledku v 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>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GBP202%2F12%2FG061" target="_blank" >GBP202/12/G061: Centrum excelence - Institut teoretické informatiky (CE-ITI)</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

9781479988754

ISSN

1043-6871

e-ISSN

—

Počet stran výsledku

13

Strana od-do

244-256

Název nakladatele

IEEE

Místo vydání

Los Alamitos, California

Místo konání akce

Los Alamitos, California

Datum konání akce

1. 1. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—