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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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 48; no. 6; pp. 1773 - 1785
Main Authors: Xiao, Hu, Cui, Rongxin, Xu, Demin
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2017.2715228