Applying Max-sum to asymmetric distributed constraint optimization problems

• Published in: Autonomous agents and multi-agent systems Vol. 34; no. 1
• Main Authors: Zivan, Roie, Parash, Tomer, Cohen-Lavi, Liel, Naveh, Yarden
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Computer Systems Organization and Communication Networks; Optimization; Search algorithms; Skewed distributions; Software Engineering/Programming and Operating Systems; User Interfaces and Human Computer Interaction; Incomplete inference distributed algorithms; Distributed constraint optimization; Max-sum
• ISSN: 1387-2532; 1573-7454
• DOI: 10.1007/s10458-019-09436-8

We study the adjustment and use of the Max-sum algorithm for solving Asymmetric Distributed Constraint Optimization Problems (ADCOPs). First, we formalize asymmetric factor-graphs and apply the different versions of Max-sum to them. Apparently, in contrast to local search algorithms, most Max-sum versions perform similarly when solving symmetric and asymmetric problems and some even perform better on asymmetric problems. Second, we prove that the convergence properties of Max-sum_ADVP (an algorithm that was previously found to outperform standard Max-sum and Bounded Max-sum) and the quality of the solutions it produces, are dependent on the order between nodes involved in each constraint, i.e., the inner constraint order (ICO). A standard ICO allows to reproduce the properties achieved for symmetric problems. Third, we demonstrate that a non-standard ICO can be used to balance exploration and exploitation. Our results indicate that Max-sum_ADVP with non-standard ICO and Damped Max-sum, when solving asymmetric problems, both outperform other versions of Max-sum, as well as local search algorithms specifically designed for solving ADCOPs.