---
_id: '480'
abstract:
- lang: eng
text: Graph games provide the foundation for modeling and synthesizing reactive
processes. In the synthesis of stochastic reactive processes, the traditional
model is perfect-information stochastic games, where some transitions of the game
graph are controlled by two adversarial players, and the other transitions are
executed probabilistically. We consider such games where the objective is the
conjunction of several quantitative objectives (specified as mean-payoff conditions),
which we refer to as generalized mean-payoff objectives. The basic decision problem
asks for the existence of a finite-memory strategy for a player that ensures the
generalized mean-payoff objective be satisfied with a desired probability against
all strategies of the opponent. A special case of the decision problem is the
almost-sure problem where the desired probability is 1. Previous results presented
a semi-decision procedure for -approximations of the almost-sure problem. In this
work, we show that both the almost-sure problem as well as the general basic decision
problem are coNP-complete, significantly improving the previous results. Moreover,
we show that in the case of 1-player stochastic games, randomized memoryless strategies
are sufficient and the problem can be solved in polynomial time. In contrast,
in two-player stochastic games, we show that even with randomized strategies exponential
memory is required in general, and present a matching exponential upper bound.
We also study the basic decision problem with infinite-memory strategies and present
computational complexity results for the problem. Our results are relevant in
the synthesis of stochastic reactive systems with multiple quantitative requirements.
alternative_title:
- Proceedings Symposium on Logic in Computer Science
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Laurent
full_name: Doyen, Laurent
last_name: Doyen
citation:
ama: 'Chatterjee K, Doyen L. Perfect-information stochastic games with generalized
mean-payoff objectives. In: Vol 05-08-July-2016. IEEE; 2016:247-256. doi:10.1145/2933575.2934513'
apa: 'Chatterjee, K., & Doyen, L. (2016). Perfect-information stochastic games
with generalized mean-payoff objectives (Vol. 05-08-July-2016, pp. 247–256). Presented
at the LICS: Logic in Computer Science, New York, NY, USA: IEEE. https://doi.org/10.1145/2933575.2934513'
chicago: Chatterjee, Krishnendu, and Laurent Doyen. “Perfect-Information Stochastic
Games with Generalized Mean-Payoff Objectives,” 05-08-July-2016:247–56. IEEE,
2016. https://doi.org/10.1145/2933575.2934513.
ieee: 'K. Chatterjee and L. Doyen, “Perfect-information stochastic games with generalized
mean-payoff objectives,” presented at the LICS: Logic in Computer Science, New
York, NY, USA, 2016, vol. 05-08-July-2016, pp. 247–256.'
ista: 'Chatterjee K, Doyen L. 2016. Perfect-information stochastic games with generalized
mean-payoff objectives. LICS: Logic in Computer Science, Proceedings Symposium
on Logic in Computer Science, vol. 05-08-July-2016, 247–256.'
mla: Chatterjee, Krishnendu, and Laurent Doyen. Perfect-Information Stochastic
Games with Generalized Mean-Payoff Objectives. Vol. 05-08-July-2016, IEEE,
2016, pp. 247–56, doi:10.1145/2933575.2934513.
short: K. Chatterjee, L. Doyen, in:, IEEE, 2016, pp. 247–256.
conference:
end_date: 2016-07-08
location: New York, NY, USA
name: 'LICS: Logic in Computer Science'
start_date: 2016-07-05
date_created: 2018-12-11T11:46:42Z
date_published: 2016-07-05T00:00:00Z
date_updated: 2021-01-12T08:00:56Z
day: '05'
department:
- _id: KrCh
doi: 10.1145/2933575.2934513
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1604.06376
month: '07'
oa: 1
oa_version: Preprint
page: 247 - 256
project:
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 2581B60A-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '279307'
name: 'Quantitative Graph Games: Theory and Applications'
- _id: 25892FC0-B435-11E9-9278-68D0E5697425
grant_number: ICT15-003
name: Efficient Algorithms for Computer Aided Verification
publication_status: published
publisher: IEEE
publist_id: '7340'
quality_controlled: '1'
scopus_import: 1
status: public
title: Perfect-information stochastic games with generalized mean-payoff objectives
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 05-08-July-2016
year: '2016'
...