{"type":"ScholarlyArticle","title":"Fighting Wildfires under Uncertainty - A Sequential Resource Allocation Approach","authors":[{"name":"Hau Chan","slug":"hau-chan","given":"Hau","family":"Chan","affiliation":"University of Nebraska-Lincoln , USA"},{"name":"Long Tran-Thanh","slug":"long-tran-thanh","given":"Long","family":"Tran-Thanh","affiliation":"University of Southampton , UK"},{"name":"Vignesh Viswanathan","slug":"vignesh-viswanathan","given":"Vignesh","family":"Viswanathan","affiliation":"Indian Institute of Technology , Kharagpur , India"}],"abstract":"Standard disaster response involves using drones (or helicopters) for reconnaissance and using people on the ground to mitigate the damage. In this paper, we look at the problem of wildfires and propose an efficient resource allocation strategy to cope with both dynamically changing environment and uncertainty. In particular, we propose Firefly, a new resource allocation algorithm, that can provably achieve optimal or near optimal solutions with high probability by first efficiently allocating observation drones to collect information to reduce uncertainty, and then allocate the firefighting units to extinguish fire. For the former, Firefly uses a combination of maximum set coverage formulation and a novel utility estimation technique, and it uses a knapsack formulation to calculate the allocation for the latter. We also demonstrate empirically by using a real-world dataset that Firefly achieves up to 80-90% performance of the offline optimal solution, even with a small amount of drones, in most of the cases.","doi":"10.24963/ijcai.2020/596","url":"https://ijcai.org/proceedings/2020/596","pdf":"https://ijcai.org/proceedings/2020/0596.pdf","markdown":"https://ijcai.org/proceedings/2020/596.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/596","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":596,"pages":{"first":4322,"last":4329},"keywords":["Agent-based and Multi-agent Systems: Resource Allocation","Uncertainty in AI: Other","Multidisciplinary Topics and Applications: Other","Machine Learning Applications: Environmental"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25214/file_25214.m3u8","length_sec":255,"provider":"hls"}],"references":[{"position":1,"raw":"[Andrews, 2014] Patricia Andrews. 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