Multiple Mean-Payoff Optimization Under Local Stability Constraints
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00141698" target="_blank" >RIV/00216224:14330/25:00141698 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1609/aaai.v39i25.34856" target="_blank" >http://dx.doi.org/10.1609/aaai.v39i25.34856</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1609/aaai.v39i25.34856" target="_blank" >10.1609/aaai.v39i25.34856</a>
Alternative languages
Result language
angličtina
Original language name
Multiple Mean-Payoff Optimization Under Local Stability Constraints
Original language description
The long-run average payoff per transition (mean payoff) is the main tool for specifying the performance and dependability properties of discrete systems. The problem of constructing a controller (strategy) simultaneously optimizing several mean payoffs has been deeply studied for stochastic and game-theoretic models. One common issue of the constructed controllers is the instability of the mean payoffs, measured by the deviations of the average rewards per transition computed in a finite "window" sliding along a run. Unfortunately, the problem of simultaneously optimizing the mean payoffs under local stability constraints is computationally hard, and the existing works do not provide a practically usable algorithm even for non-stochastic models such as two-player games. In this paper, we design and evaluate the first efficient and scalable solution to this problem applicable to Markov decision processes.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
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 AAAI Conference on Artificial Intelligence, Vol. 39 No. 25: AAAI-25 Technical Tracks 25
ISBN
9781577358978
ISSN
2159-5399
e-ISSN
2374-3468
Number of pages
8
Pages from-to
26551-26558
Publisher name
AAAI
Place of publication
Palo Alto
Event location
Philadelphia, PA
Event date
Aug 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001477487000040