Max-sum revisited; The real power of damping

Liel Cohen, Roie Zivan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Max-sum is a version of Belief Propagation, used for solving DCOPs. On tree-structured problems, Max-sum converges to the optimal solution in linear time. Unfortunately, on cyclic problems, Max-sum does not converge and explores low quality solutions. Damping is a method, often used for increasing the chances that Belief Propagation will converge. That been said, it was not mentioned in the studies that proposed Max-sum for solving DCOPs. In this paper wc advance the research on incomplete inference DCOP algorithms by investigating the effect of damping on Max-sum. We prove that Max-sum with damping is guaranteed to converge to the optimal solution in weakly polynomial lime. Our empirical results demonstrate a drastic improvement in the performance of Max-sum, when using damping. However, in contrast to the common assumption, that it performs best when converging, we demonstrate that non converging versions perform efficient exploration, and produce high quality results, when implemented within an anytime framework.

Original languageAmerican English
Title of host publication16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
EditorsEdmund Durfee, Michael Winikoff, Kate Larson, Sanmay Das
Pages1505-1507
Number of pages3
ISBN (Electronic)9781510855076
StatePublished - 1 Jan 2017
Event16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil
Duration: 8 May 201712 May 2017

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3

Conference

Conference16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
Country/TerritoryBrazil
CitySao Paulo
Period8/05/1712/05/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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