{"type":"ScholarlyArticle","title":"A Multi-Valued Decision Diagram-Based Approach to Constrained Optimal Path Problems over Directed Acyclic Graphs","authors":[{"name":"Mingwei Zhang","slug":"mingwei-zhang","given":"Mingwei","family":"Zhang","affiliation":"College of Information Science and Technology , Jinan University , Guangzhou 510632 , China,"},{"name":"Liangda Fang","slug":"liangda-fang","given":"Liangda","family":"Fang","affiliation":"College of Information Science and Technology , Jinan University , Guangzhou 510632 , China,; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , Jilin University , Changchun 130012 , China"},{"name":"Zhenhao Gu","slug":"zhenhao-gu","given":"Zhenhao","family":"Gu","affiliation":"College of Information Science and Technology , Jinan University , Guangzhou 510632 , China,"},{"name":"Quanlong Guan","slug":"quanlong-guan","given":"Quanlong","family":"Guan","affiliation":"College of Information Science and Technology , Jinan University , Guangzhou 510632 , China,"},{"name":"Yong Lai","slug":"yong-lai","given":"Yong","family":"Lai","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , Jilin University , Changchun 130012 , China"}],"abstract":"Numerous combinatorial optimization problems can be reduced to the optimal path problem over directed acyclic graphs (DAGs). The constrained version of the optimal path problem requires the solution to satisfy a given logical constraint. BDD-constrained search (BCS) is an efficient algorithm for the constrained optimal path problem over DAGs. This algorithm considers edges as variables and constraints as Boolean functions and maintains constraints via binary decision diagrams (BDDs), a compact form of Boolean functions. However, BCS involves redundant operations during the search process. To reduce these redundant operations, we use vertices instead of edges as variables and hence represent constraints as multi-valued functions. Due to the multi-valued representation of constraints, we propose a novel algorithm, namely MDD-constrained search (MCS), by using multi-valued decision diagrams (MDDs) instead of BDDs, an efficient representation of multi-valued functions. In addition, we improve MCS via domain reduction in multi-valued functions. Experimental results prove that our proposed algorithm outperforms BCS.","doi":"10.24963/ijcai.2024/219","url":"https://ijcai.org/proceedings/2024/219","pdf":"https://ijcai.org/proceedings/2024/0219.pdf","markdown":"https://ijcai.org/proceedings/2024/219.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/219","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":"Constraint Satisfaction and Optimization","track":"Main Track","number":219,"pages":{"first":1979,"last":1988},"keywords":["Constraint Satisfaction and Optimization: CSO: Constraint optimization problems","Knowledge Representation and Reasoning: KRR: Knowledge compilation","Constraint Satisfaction and Optimization: CSO: Applications","Constraint Satisfaction and Optimization: CSO: Solvers and tools"],"videos":[],"references":[{"position":1,"raw":"[Andersen et al., 2007] Henrik Reif Andersen, Tarik Hadzic, John N Hooker, and Peter Tiedemann. 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