{"type":"ScholarlyArticle","title":"Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling","authors":[{"name":"Chongjie Zhang","slug":"chongjie-zhang","given":"Chongjie","family":"Zhang","affiliation":""},{"name":"Julie A. Shah","slug":"julie-a-shah","given":"Julie A.","family":"Shah","affiliation":""}],"abstract":"To enable large-scale multi-agent coordination under temporal and spatial constraints, we formulate it as a multi-level optimization problem and develop a multi-abstraction search approach for co-optimizing agent placement with task assignment and scheduling. This approach begins with a highly abstract agent placement problem and the rapid computation of an initial solution, which is then improved upon using a hill climbing algorithm for a less abstract problem; finally, the solution is fine-tuned within the original problem space. Empirical results demonstrate that this multi-abstraction approach significantly outperforms a conventional hill climbing algorithm and an approximate mixed-integer linear programming approach.","doi":null,"url":"https://ijcai.org/proceedings/2016/468","pdf":"https://ijcai.org/proceedings/2016/0468.pdf","markdown":"https://ijcai.org/proceedings/2016/468.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/468","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":468,"pages":{"first":3308,"last":3313},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}