---
_id: '2211'
abstract:
- lang: eng
text: 'In two-player finite-state stochastic games of partial observation on graphs,
in every state of the graph, the players simultaneously choose an action, and
their joint actions determine a probability distribution over the successor states.
The game is played for infinitely many rounds and thus the players construct an
infinite path in the graph. We consider reachability objectives where the first
player tries to ensure a target state to be visited almost-surely (i.e., with
probability 1) or positively (i.e., with positive probability), no matter the
strategy of the second player. We classify such games according to the information
and to the power of randomization available to the players. On the basis of information,
the game can be one-sided with either (a) player 1, or (b) player 2 having partial
observation (and the other player has perfect observation), or two-sided with
(c) both players having partial observation. On the basis of randomization, (a)
the players may not be allowed to use randomization (pure strategies), or (b)
they may choose a probability distribution over actions but the actual random
choice is external and not visible to the player (actions invisible), or (c) they
may use full randomization. Our main results for pure strategies are as follows:
(1) For one-sided games with player 2 having perfect observation we show that
(in contrast to full randomized strategies) belief-based (subset-construction
based) strategies are not sufficient, and we present an exponential upper bound
on memory both for almost-sure and positive winning strategies; we show that the
problem of deciding the existence of almost-sure and positive winning strategies
for player 1 is EXPTIME-complete and present symbolic algorithms that avoid the
explicit exponential construction. (2) For one-sided games with player 1 having
perfect observation we show that nonelementarymemory is both necessary and sufficient
for both almost-sure and positive winning strategies. (3) We show that for the
general (two-sided) case finite-memory strategies are sufficient for both positive
and almost-sure winning, and at least nonelementary memory is required. We establish
the equivalence of the almost-sure winning problems for pure strategies and for
randomized strategies with actions invisible. Our equivalence result exhibit serious
flaws in previous results of the literature: we show a nonelementary memory lower
bound for almost-sure winning whereas an exponential upper bound was previously
claimed.'
article_number: '16'
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. Partial-observation stochastic games: How to win when
belief fails. ACM Transactions on Computational Logic (TOCL). 2014;15(2).
doi:10.1145/2579821'
apa: 'Chatterjee, K., & Doyen, L. (2014). Partial-observation stochastic games:
How to win when belief fails. ACM Transactions on Computational Logic (TOCL).
ACM. https://doi.org/10.1145/2579821'
chicago: 'Chatterjee, Krishnendu, and Laurent Doyen. “Partial-Observation Stochastic
Games: How to Win When Belief Fails.” ACM Transactions on Computational Logic
(TOCL). ACM, 2014. https://doi.org/10.1145/2579821.'
ieee: 'K. Chatterjee and L. Doyen, “Partial-observation stochastic games: How to
win when belief fails,” ACM Transactions on Computational Logic (TOCL),
vol. 15, no. 2. ACM, 2014.'
ista: 'Chatterjee K, Doyen L. 2014. Partial-observation stochastic games: How to
win when belief fails. ACM Transactions on Computational Logic (TOCL). 15(2),
16.'
mla: 'Chatterjee, Krishnendu, and Laurent Doyen. “Partial-Observation Stochastic
Games: How to Win When Belief Fails.” ACM Transactions on Computational Logic
(TOCL), vol. 15, no. 2, 16, ACM, 2014, doi:10.1145/2579821.'
short: K. Chatterjee, L. Doyen, ACM Transactions on Computational Logic (TOCL) 15
(2014).
date_created: 2018-12-11T11:56:21Z
date_published: 2014-04-01T00:00:00Z
date_updated: 2023-02-23T12:23:43Z
day: '01'
department:
- _id: KrCh
doi: 10.1145/2579821
external_id:
arxiv:
- '1107.2141'
intvolume: ' 15'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1107.2141
month: '04'
oa: 1
oa_version: Preprint
publication: ACM Transactions on Computational Logic (TOCL)
publication_status: published
publisher: ACM
publist_id: '4759'
quality_controlled: '1'
related_material:
record:
- id: '1903'
relation: earlier_version
status: public
- id: '2955'
relation: earlier_version
status: public
- id: '5381'
relation: earlier_version
status: public
scopus_import: 1
status: public
title: 'Partial-observation stochastic games: How to win when belief fails'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 15
year: '2014'
...