{"type":"ScholarlyArticle","title":"Efficient Object Search in Game Maps","authors":[{"name":"Jinchun Du","slug":"jinchun-du","given":"Jinchun","family":"Du","affiliation":"Faculty of Information Technology , Monash University , Melbourne , Australia"},{"name":"Bojie Shen","slug":"bojie-shen","given":"Bojie","family":"Shen","affiliation":"Faculty of Information Technology , Monash University , Melbourne , Australia"},{"name":"Shizhe Zhao","slug":"shizhe-zhao","given":"Shizhe","family":"Zhao","affiliation":"Faculty of Information Technology , Monash University , Melbourne , Australia"},{"name":"Muhammad Aamir Cheema","slug":"muhammad-aamir-cheema","given":"Muhammad Aamir","family":"Cheema","affiliation":"Faculty of Information Technology , Monash University , Melbourne , Australia"},{"name":"Adel Nadjaran Toosi","slug":"adel-nadjaran-toosi","given":"Adel Nadjaran","family":"Toosi","affiliation":"Faculty of Information Technology , Monash University , Melbourne , Australia"}],"abstract":"Video games feature a dynamic environment where locations of objects (e.g., characters, equipment, weapons, vehicles etc.) frequently change within the game world. Although searching for relevant nearby objects in such a dynamic setting is a fundamental operation, this problem has received little research attention. In this paper, we propose a simple lightweight index, called Grid Tree, to store objects and their associated textual data. Our index can be efficiently updated with the underlying updates such as object movements, and supports a variety of object search queries, including k nearest neighbors (returning the k closest objects), keyword k nearest neighbors (returning the k closest objects that satisfy query keywords), and several other variants. Our extensive experimental study, conducted on standard game maps benchmarks and real-world keywords, demonstrates that our approach has  up to 2 orders of magnitude faster update times for moving objects compared to state-of-the-art approaches such as navigation mesh and IR-tree. At the same time, query performance of our approach is similar to or better than that of IR-tree and up to two orders of magnitude faster than the other competitor.","doi":"10.24963/ijcai.2023/618","url":"https://ijcai.org/proceedings/2023/618","pdf":"https://ijcai.org/proceedings/2023/0618.pdf","markdown":"https://ijcai.org/proceedings/2023/618.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/618","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Search","track":"Main Track","number":618,"pages":{"first":5567,"last":5576},"keywords":["Robotics: ROB: Motion and path planning","Search: S: Heuristic search","Planning and Scheduling: PS: Scheduling"],"videos":[],"references":[{"position":1,"raw":"[Abraham et al., 2011] Ittai Abraham, Daniel Delling, An- drew V Goldberg, and Renato F Werneck. 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