{"type":"ScholarlyArticle","title":"Speeding Up Incomplete GDL-based Algorithms for Multi-agent Optimization with Dense Local Utilities","authors":[{"name":"Yanchen Deng","slug":"yanchen-deng","given":"Yanchen","family":"Deng","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Bo An","slug":"bo-an","given":"Bo","family":"An","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"}],"abstract":"Incomplete GDL-based algorithms including Max-sum and its variants are important methods for multi-agent optimization. However, they face a significant scalability challenge as the computational overhead grows exponentially with respect to the arity of each utility function. Generic Domain Pruning (GDP) technique reduces the computational effort by performing a one-shot pruning to filter out suboptimal entries. Unfortunately, GDP could perform poorly when dealing with dense local utilities and ties which widely exist in many domains. In this paper, we present several novel sorting-based acceleration algorithms by alleviating the effect of densely distributed local utilities. Specifically, instead of one-shot pruning in GDP, we propose to integrate both search and pruning to iteratively reduce the search space. Besides, we cope with the utility ties by organizing the search space of tied utilities into AND/OR trees to enable branch-and-bound. Finally, we propose a discretization mechanism to offer a tradeoff between the reconstruction overhead and the pruning efficiency. We demonstrate the superiorities of our algorithms over the state-of-the-art from both theoretical and experimental perspectives.","doi":"10.24963/ijcai.2020/5","url":"https://ijcai.org/proceedings/2020/5","pdf":"https://ijcai.org/proceedings/2020/0005.pdf","markdown":"https://ijcai.org/proceedings/2020/5.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/5","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":"Agent-based and Multi-agent Systems","track":"Main track","number":5,"pages":{"first":31,"last":38},"keywords":["Constraints and SAT: Distributed Constraints","Agent-based and Multi-agent Systems: Coordination and Cooperation","Constraints and SAT: Constraint Optimization"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25295/file_25295.m3u8","length_sec":288,"provider":"hls"}],"references":[{"position":1,"raw":"[Aji and McEliece, 2000] Srinivas M Aji and Robert J McEliece. 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