{"type":"ScholarlyArticle","title":"AsymDPOP: Complete Inference for Asymmetric Distributed Constraint Optimization Problems","authors":[{"name":"Yanchen Deng","slug":"yanchen-deng","given":"Yanchen","family":"Deng","affiliation":"College of Computer Science , Chongqing University"},{"name":"Ziyu Chen","slug":"ziyu-chen","given":"Ziyu","family":"Chen","affiliation":"College of Computer Science , Chongqing University"},{"name":"Dingding Chen","slug":"dingding-chen","given":"Dingding","family":"Chen","affiliation":"College of Computer Science , Chongqing University"},{"name":"Wenxin Zhang","slug":"wenxin-zhang","given":"Wenxin","family":"Zhang","affiliation":"College of Computer Science , Chongqing University"},{"name":"Xingqiong Jiang","slug":"xingqiong-jiang","given":"Xingqiong","family":"Jiang","affiliation":"College of Computer Science , Chongqing University"}],"abstract":"Asymmetric distributed constraint optimization problems (ADCOPs) are an emerging model for coordinating agents with personal preferences. However, the existing inference-based complete algorithms which use local eliminations cannot be applied to ADCOPs, as the parent agents are required to transfer their private functions to their children. Rather than disclosing private functions explicitly to facilitate local eliminations, we solve the problem by enforcing delayed eliminations and propose AsymDPOP, the first inference-based complete algorithm for ADCOPs. To solve the severe scalability problems incurred by delayed eliminations, we propose to reduce the memory consumption by propagating a set of smaller utility tables instead of a joint utility table, and to reduce the computation efforts by sequential optimizations instead of joint optimizations. The empirical evaluation indicates that AsymDPOP significantly outperforms the state-of-the-art, as well as the vanilla DPOP with PEAV formulation.","doi":"10.24963/ijcai.2019/32","url":"https://ijcai.org/proceedings/2019/32","pdf":"https://ijcai.org/proceedings/2019/0032.pdf","markdown":"https://ijcai.org/proceedings/2019/32.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/32","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":32,"pages":{"first":223,"last":230},"keywords":["Constraints and SAT: Distributed Constraints","Agent-based and Multi-agent Systems: Coordination and Cooperation"],"videos":[],"references":[{"position":1,"raw":"[Brito et al., 2009] Ismel Brito, Amnon Meisels, Pedro Meseguer, and Roie Zivan. 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