{"type":"ScholarlyArticle","title":"Public Event Scheduling with Busy Agents","authors":[{"name":"Bo Li","slug":"bo-li","given":"Bo","family":"Li","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Lijun Li","slug":"lijun-li","given":"Lijun","family":"Li","affiliation":"Department of Computer Science , City University of Hong Kong"},{"name":"Minming Li","slug":"minming-li","given":"Minming","family":"Li","affiliation":"Department of Computer Science , City University of Hong Kong"},{"name":"Ruilong Zhang","slug":"ruilong-zhang","given":"Ruilong","family":"Zhang","affiliation":"Department of Computer Science and Engineering , University at Buffalo"}],"abstract":"We study a public event scheduling problem, where multiple public events are scheduled to coordinate the availability of multiple agents. The availability of each agent is determined by solving a separate flexible interval job scheduling problem, where the jobs are required to be preemptively processed. The agents want to attend as many events as possible, and their agreements are considered to be the total length of time during which they can attend these events. The goal is to find a schedule for events as well as the job schedule for each agent such that the total agreement is maximized.\r\n\r\nWe first show that the problem is NP-hard, and then prove that a simple greedy algorithm achieves 1/2-approximation when the whole timeline is polynomially bounded.\r\nOur method also implies a (1-1/e)-approximate algorithm for this case. Subsequently, for the general timeline case, we present an algorithmic framework that extends a 1/alpha-approximate algorithm for the one-event instance to the general case that achieves 1/(alpha+1)-approximation. Finally, we give a polynomial time algorithm that solves the one-event instance, and this implies a 1/2-approximate algorithm for the general case.","doi":"10.24963/ijcai.2024/319","url":"https://ijcai.org/proceedings/2024/319","pdf":"https://ijcai.org/proceedings/2024/0319.pdf","markdown":"https://ijcai.org/proceedings/2024/319.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/319","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Game Theory and Economic Paradigms","track":"Main Track","number":319,"pages":{"first":2877,"last":2885},"keywords":["Planning and Scheduling: PS: Scheduling","Agent-based and Multi-agent Systems: MAS: Resource allocation","Game Theory and Economic Paradigms: GTEP: Computational social choice"],"videos":[],"references":[{"position":1,"raw":"[Antoniadis et al., 2020] Antonios Antoniadis, Naveen Garg, Gunjan Kumar, and Nikhil Kumar. 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