Abstract
Complete search algorithms for solving Distributed constraint optimization problems (DCOPs) can be divided into two groups: algorithms that use a pseudo tree and algorithms that do not use one. The best performing algorithms that do not use a pseudo tree use some form of forward bounding. In order to try and gain from both worlds, a new algorithm, which incorporates a hybrid approach of the two groups is presented. The proposed algorithm – Pseudo-Tree Forward-bounding (PT-FB) – is shown to perform very well. PT-FB is next extended to be able to solve Asymmetric DCOPs (ADCOPs). Here again, its performance is better than its competitors. An extensive experimental evaluation of the performance of each of the proposed algorithms is presented.
Original language | English |
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Pages (from-to) | 83-99 |
Number of pages | 17 |
Journal | Artificial Intelligence |
Volume | 252 |
DOIs | |
State | Published - Nov 2017 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2017 Elsevier B.V.
Funding
This work was supported by the Lynn and William Frankel center for Computer Sciences .
Funders | Funder number |
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Research Center of the College of Computer and Information Sciences, King Saud University |
Keywords
- Asymmetric DCOPs
- Distributed constraints optimization