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Xiao H, Cui R, Xu D. A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints. IEEE Trans Cybern. 2017;48(6):1773-1785doi: 10.1109/TCYB.2017.2715228.
Xiao, H., Cui, R., & Xu, D. (2018). A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints. IEEE transactions on cybernetics, 48(6), 1773-1785. https://doi.org/10.1109/TCYB.2017.2715228
Xiao, Hu, et al. "A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints." IEEE transactions on cybernetics vol. 48,6 (2018): 1773-1785. doi: https://doi.org/10.1109/TCYB.2017.2715228
Xiao H, Cui R, Xu D. A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints. IEEE Trans Cybern. 2018 Jun;48(6):1773-1785. doi: 10.1109/TCYB.2017.2715228. Epub 2017 Jun 27. PMID: 28678726.
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IEEE Trans Cybern. 2018 Jun;48(6):1773-1785. doi: 10.1109/TCYB.2017.2715228. Epub 2017 Jun 27.

A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints.

IEEE transactions on cybernetics

Hu Xiao, Rongxin Cui, Demin Xu

PMID: 28678726 DOI: 10.1109/TCYB.2017.2715228

Abstract

This paper presents a cooperative multiagent search algorithm to solve the problem of searching for a target on a 2-D plane under multiple constraints. A Bayesian framework is used to update the local probability density functions (PDFs) of the target when the agents obtain observation information. To obtain the global PDF used for decision making, a sampling-based logarithmic opinion pool algorithm is proposed to fuse the local PDFs, and a particle sampling approach is used to represent the continuous PDF. Then the Gaussian mixture model (GMM) is applied to reconstitute the global PDF from the particles, and a weighted expectation maximization algorithm is presented to estimate the parameters of the GMM. Furthermore, we propose an optimization objective which aims to guide agents to find the target with less resource consumptions, and to keep the resource consumption of each agent balanced simultaneously. To this end, a utility function-based optimization problem is put forward, and it is solved by a gradient-based approach. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed one uses less overall resources and shows a better performance of balancing the resource consumption.

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