{"type":"ScholarlyArticle","title":"ATSIS: Achieving the Ad hoc Teamwork by Sub-task Inference and Selection","authors":[{"name":"Shuo Chen","slug":"shuo-chen","given":"Shuo","family":"Chen","affiliation":"School of Computer Science and Engineering , Nanyang Technological University; ST Engineering -NTU Corporate Laboratory , Nanyang Technological University"},{"name":"Ewa Andrejczuk","slug":"ewa-andrejczuk","given":"Ewa","family":"Andrejczuk","affiliation":"ST Engineering -NTU Corporate Laboratory , Nanyang Technological University"},{"name":"Athirai A. Irissappane","slug":"athirai-a-irissappane","given":"Athirai","family":"A. Irissappane","affiliation":"School of Engineering and Technology , University of Washington"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"School of Computer Science and Engineering , Nanyang Technological University"}],"abstract":"In an ad hoc teamwork setting, the team needs to coordinate their activities to perform a task without prior agreement on how to achieve it. The ad hoc agent cannot communicate with its teammates but it can observe their behaviour and plan accordingly. To do so, the existing approaches rely on the teammates' behaviour models. However, the models may not be accurate, which can compromise teamwork. For this reason, we present Ad Hoc Teamwork by Sub-task Inference and Selection (ATSIS) algorithm that uses a sub-task inference without relying on teammates' models. First, the ad hoc agent observes its teammates to infer which sub-tasks they are handling. Based on that, it selects its own sub-task using a partially observable Markov decision process that handles the uncertainty of the sub-task inference. Last, the ad hoc agent uses the Monte Carlo tree search to find the set of actions to perform the sub-task. Our experiments show the benefits of ATSIS for robust teamwork.","doi":"10.24963/ijcai.2019/25","url":"https://ijcai.org/proceedings/2019/25","pdf":"https://ijcai.org/proceedings/2019/0025.pdf","markdown":"https://ijcai.org/proceedings/2019/25.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/25","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":25,"pages":{"first":172,"last":179},"keywords":["Agent-based and Multi-agent Systems: Coordination and Cooperation","Uncertainty in AI: Sequential Decision Making"],"videos":[],"references":[{"position":1,"raw":"[Agmon and Stone, 2012] Noa Agmon and Peter Stone. 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