|Título||Generalizing ADOPT and BnB-ADOPT|
|Publication Type||Conference Paper|
|Year of Publication||2011|
|Authors||Gutierrez P, Meseguer P, Yeoh W|
|Conference Name||22nd International Joint Conference on Artificial Intelligence (IJCAI 2011)|
|Conference Location||Barcelona, Spain|
ADOPT and BnB-ADOPT are two optimal DCOP search algorithms that are similar except for their search strategies: the former uses best-first search and the latter uses depth-first branch-and-bound search. In this paper, we present a new algorithm, called ADOPT($k$), that generalizes them. Its behavior depends on the $k$ parameter. It behaves like ADOPT when $k=1$, like BnB-ADOPT when $k=∞$ and like a hybrid of ADOPT and BnB-ADOPT when $1 < k < ∞$. We prove that ADOPT($k$) is a correct and complete algorithm and experimentally show that ADOPT($k$) outperforms ADOPT and BnB-ADOPT on several benchmarks across several metrics.
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