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Multi-Variable Agents Decomposition for DCOPs

AAAI 2016 · Multi-agent systems and games › Cooperative multi-agent RL · approach: Coordination graphs · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use MVA decomposition instead of Decomposition reformulationAFB-DFBnB · Radar coordination 8x8simulated time (ms): 108818 -> 213Helped
use MVA decomposition instead of Decomposition reformulationDPOP-DFBnB · Radar coordination 8x4simulated time (ms): 61978 -> 844Helped
lazy instead of eager MVA table construction (ablation)AFB-DFBnB with MVA · Radar coordination 8x8simulated time (ms): 10391 -> 213Helped
lazy instead of eager MVA table construction (ablation)DPOP-DFBnB with MVA · Radar coordination 8x8simulated time (ms): 30396 -> 30312No clear effect
lazy instead of eager MVA table construction (ablation)AFB, DPOP, D-Gibbs with MVA · Random graph instancesmessages and runtimeNo clear effect
run local Gibbs solver on GPU instead of CPUDPOP-Gibbs · Random graph, |Li|=18simulated time (ms): 447 -> 6 (74.5x speedup)Helped
run local DFBnB solver on GPU instead of CPUDPOP-DFBnB · Random graph, varying |Di|simulated time (ms): 3400 -> 450Helped
use MVA instead of Decomposition when all variables are interface variables (ratio 1)DPOP · Random graphruntimeHurt
use MVA instead of Decomposition when all variables are interface variables (ratio 1)AFB and D-Gibbs · Random graphruntimeHelped
use MVA instead of CompilationAFB · Random graphexternal messagesHelped

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