{"type":"ScholarlyArticle","title":"Robust Reward Placement under Uncertainty","authors":[{"name":"Petros Petsinis","slug":"petros-petsinis","given":"Petros","family":"Petsinis","affiliation":"Department of Computer Science , Aarhus University"},{"name":"Kaichen Zhang","slug":"kaichen-zhang","given":"Kaichen","family":"Zhang","affiliation":"Artificial Intelligence Thrust , Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Andreas Pavlogiannis","slug":"andreas-pavlogiannis","given":"Andreas","family":"Pavlogiannis","affiliation":"Department of Computer Science , Aarhus University"},{"name":"Jingbo Zhou","slug":"jingbo-zhou","given":"Jingbo","family":"Zhou","affiliation":"Business Intelligence Lab , Baidu Research"},{"name":"Panagiotis Karras","slug":"panagiotis-karras","given":"Panagiotis","family":"Karras","affiliation":"Department of Computer Science , University of Copenhagen; Department of Computer Science , Aarhus University"}],"abstract":"We consider a problem of placing generators of rewards to be collected by randomly moving agents in a network. In many settings, the precise mobility pattern may be one of several possible, based on parameters outside our control, such as weather conditions. The placement should be robust to this uncertainty, to gain a competent total reward across possible networks. To study such scenarios, we introduce the Robust Reward Placement problem (RRP). Agents move randomly by a Markovian Mobility Model with a predetermined set of locations whose connectivity is chosen adversarially from a known set Π of candidates. We aim to select a set of reward states within a budget that maximizes the minimum ratio, among all candidates in Π, of the collected total reward over the optimal collectable reward under the same candidate. We prove that RRP is NP-hard and inapproximable, and develop Ψ-Saturate, a pseudo-polynomial time algorithm that achieves an ϵ-additive approximation by exceeding the budget constraint by a factor that scales as O(ln |Π|/ϵ). In addition, we present several heuristics, most prominently one inspired by a dynamic programming algorithm for the max–min 0–1 KNAPSACK problem. We corroborate our theoretical analysis with an experimental evaluation on synthetic and real data.","doi":"10.24963/ijcai.2024/748","url":"https://ijcai.org/proceedings/2024/748","pdf":"https://ijcai.org/proceedings/2024/0748.pdf","markdown":"https://ijcai.org/proceedings/2024/748.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/748","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":"Planning and Scheduling","track":"Main Track","number":748,"pages":{"first":6770,"last":6778},"keywords":["Planning and Scheduling: PS: Planning under uncertainty","Search: S: Combinatorial search and optimisation","Data Mining: DM: Networks","Planning and Scheduling: PS: Markov decisions processes"],"videos":[],"references":[{"position":1,"raw":"[Adiga et al., 2014] Abhijin Adiga, Chris J. 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