Applying Max-sum to asymmetric distributed constraint optimization problems

Roie Zivan, Tomer Parash, Liel Cohen-Lavi, Yarden Naveh

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Article number13
JournalAutonomous Agents and Multi-Agent Systems
Issue number1
StatePublished - 1 Apr 2020


  • Distributed constraint optimization
  • Incomplete inference distributed algorithms
  • Max-sum

ASJC Scopus subject areas

  • Artificial Intelligence


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