An abstraction-based method for verifying strategic properties in multi-agent systems with imperfect information
File(s)main.pdf (309.83 KB)
Published version
Author(s)
Belardinelli, Francesco
Lomuscio, Alessio
Malvone, Alessio
Type
Conference Paper
Abstract
We investigate the verification of Multi-agent Systems
against strategic properties expressed in Alternating-time
Temporal Logic under the assumptions of imperfect informa-
tion and perfect recall. To this end, we develop a three-valued
semantics for concurrent game structures upon which we de-
fine an abstraction method. We prove that concurrent game
structures with imperfect information admit perfect informa-
tion abstractions that preserve three-valued satisfaction. Fur-
ther, we present a refinement procedure to deal with cases
where the value of a specification is undefined. We illustrate
the overall procedure in a variant of the Train Gate Controller
scenario under imperfect information and perfect recall.
against strategic properties expressed in Alternating-time
Temporal Logic under the assumptions of imperfect informa-
tion and perfect recall. To this end, we develop a three-valued
semantics for concurrent game structures upon which we de-
fine an abstraction method. We prove that concurrent game
structures with imperfect information admit perfect informa-
tion abstractions that preserve three-valued satisfaction. Fur-
ther, we present a refinement procedure to deal with cases
where the value of a specification is undefined. We illustrate
the overall procedure in a variant of the Train Gate Controller
scenario under imperfect information and perfect recall.
Date Issued
2019-07-17
Date Acceptance
2018-10-31
Citation
2019, pp.6030-6037
Publisher
Association for the Advancement of Artificial Intelligence
Start Page
6030
End Page
6037
Copyright Statement
© 2019, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
Intelligence (www.aaai.org). All rights reserved.
Identifier
https://www.aaai.org/ojs/index.php/AAAI/article/view/4558
Source
Thirty-Third AAAI Conference on Artificial Intelligence
Publication Status
Published
Start Date
2019-01-27
Finish Date
2019-02-01
Coverage Spatial
Honolulu, Hawaii, USA
Date Publish Online
2019-07-17