{"type":"ScholarlyArticle","title":"Symbolic Model Checking for One-Resource RB+-ATL","authors":[{"name":"Natasha Alechina","slug":"natasha-alechina","given":"Natasha","family":"Alechina","affiliation":""},{"name":"Brian Logan","slug":"brian-logan","given":"Brian","family":"Logan","affiliation":""},{"name":"Nguyen Hoang Nga","slug":"nguyen-hoang-nga","given":"Nguyen Hoang","family":"Nga","affiliation":""},{"name":"Franco Raimondi","slug":"franco-raimondi","given":"Franco","family":"Raimondi","affiliation":""}],"abstract":"RB+-ATL is an extension of ATL where it is possible to model consumption and production of several resources by a set of agents. The model-checking problem for RB+-ATL is known to be decidable. However the only available model-checking algorithm for RB+-ATL uses a forward search of the state space, and hence does not have an efficient symbolic implementation. In this paper, we consider a fragment of RB+-ATL, 1RB+-ATL, that allows only one resource type. We give a symbolic model-checking algorithm for this fragment of RB+-ATL, and evaluate the performance of an MCMAS-based implementation of the algorithm on an example problem that can be scaled to large state spaces.","doi":null,"url":"https://ijcai.org/proceedings/2015/155","pdf":"https://ijcai.org/proceedings/2015/0155.pdf","markdown":"https://ijcai.org/proceedings/2015/155.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/155","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Model Verification / Model Checking","track":"Main Track","number":155,"pages":{"first":1069,"last":1074},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}