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

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....

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Vydáno v:	IEEE transactions on cybernetics Ročník 48; číslo 6; s. 1773 - 1785
Hlavní autoři:	Xiao, Hu, Cui, Rongxin, Xu, Demin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.06.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Bayes methods Bayesian analysis Bayesian update Computer simulation Cybernetics Decision making Mobile communication multiagent search Multiagent systems Optimization Parameter estimation particle sampling Probabilistic models Probability density function Probability density functions resource constraints (RCs) Sampling Search algorithms Search problems
ISSN:	2168-2267, 2168-2275, 2168-2275
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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.
AbstractList	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. 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.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.
Author	Xiao, Hu Xu, Demin Cui, Rongxin
Author_xml	– sequence: 1 givenname: Hu surname: Xiao fullname: Xiao, Hu email: hu.x@mail.nwpu.edu.cn organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China – sequence: 2 givenname: Rongxin orcidid: 0000-0002-8006-3620 surname: Cui fullname: Cui, Rongxin email: r.cui@nwpu.edu.cn organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China – sequence: 3 givenname: Demin surname: Xu fullname: Xu, Demin email: xudm@nwpu.edu.cn organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China
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SubjectTerms	Algorithms Bayes methods Bayesian analysis Bayesian update Computer simulation Cybernetics Decision making Mobile communication multiagent search Multiagent systems Optimization Parameter estimation particle sampling Probabilistic models Probability density function Probability density functions resource constraints (RCs) Sampling Search algorithms Search problems
Title	A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints
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