TY - JOUR
T1 - Applying Max-sum to asymmetric distributed constraint optimization problems
AU - Zivan, Roie
AU - Parash, Tomer
AU - Cohen-Lavi, Liel
AU - Naveh, Yarden
N1 - Publisher Copyright: © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
KW - Distributed constraint optimization
KW - Incomplete inference distributed algorithms
KW - Max-sum
UR - http://www.scopus.com/inward/record.url?scp=85077208978&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/s10458-019-09436-8
DO - https://doi.org/10.1007/s10458-019-09436-8
M3 - Article
SN - 1387-2532
VL - 34
JO - Autonomous Agents and Multi-Agent Systems
JF - Autonomous Agents and Multi-Agent Systems
IS - 1
M1 - 13
ER -