{"type":"ScholarlyArticle","title":"Generalized Target Assignment and Path Finding Using Answer Set Programming","authors":[{"name":"Van Nguyen","slug":"van-nguyen","given":"Van","family":"Nguyen","affiliation":"Computer Science Department New Mexico State University"},{"name":"Philipp Obermeier","slug":"philipp-obermeier","given":"Philipp","family":"Obermeier","affiliation":"Computer Science Department University of Potsdam"},{"name":"Tran Cao Son","slug":"tran-cao-son","given":"Tran Cao","family":"Son","affiliation":"Computer Science Department New Mexico State University"},{"name":"Torsten Schaub","slug":"torsten-schaub","given":"Torsten","family":"Schaub","affiliation":"Computer Science Department University of Potsdam"},{"name":"William Yeoh","slug":"william-yeoh","given":"William","family":"Yeoh","affiliation":"Computer Science Department New Mexico State University"}],"abstract":"In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications such as autonomous warehouse systems. We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based method can be efficient and can scale up to solve practical applications.","doi":"10.24963/ijcai.2017/169","url":"https://ijcai.org/proceedings/2017/169","pdf":"https://ijcai.org/proceedings/2017/0169.pdf","markdown":"https://ijcai.org/proceedings/2017/169.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/169","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":169,"pages":{"first":1216,"last":1223},"keywords":["Knowledge Representation, Reasoning, and Logic: Action, Change and Causality","Planning and Scheduling: Applications of Planning"],"videos":[],"references":[{"position":1,"raw":"[Boyarski et al., 2015] Eli Boyarski, Ariel Felner, Roni Stern, Guni Sharon, David Tolpin, Oded Betzalel, and Solomon Shimony. 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