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Topology-preserving flocking of nonlinear agents using optimistic planning
L.Busoniu,I.C.Morarescu
0
(Department of Automation, Technical University of Cluj-Napoca)
摘要:
We consider the generalized flocking problem in multiagent systems, where the agents must drive a subset of their state variables to common values, while communication is constrained by a proximity relationship in terms of another subset of variables. We build a flocking method for general nonlinear agent dynamics, by using at each agent a near-optimal control technique from artificial intelligence called optimistic planning. By defining the rewards to be optimized in a well-chosen way, the preservation of the interconnection topology is guaranteed, under a controllability assumption. We also give a practical variant of the algorithm that does not require to know the details of this assumption, and show that it works well in experiments on nonlinear agents.
关键词:  Multiagent systems  Flocking  Optimistic planning  Topology preservation
DOI:
Received:July 22, 2014Revised:January 20, 2015
基金项目:
Topology-preserving flocking of nonlinear agents using optimistic planning
L. Busoniu,I. C. Morarescu
(Department of Automation, Technical University of Cluj-Napoca;Universit′e de Lorraine, CRAN, UMR 7039 and CNRS, CRAN, UMR 7039, 2 Avenue de la Fore?t de Haye)
Abstract:
We consider the generalized flocking problem in multiagent systems, where the agents must drive a subset of their state variables to common values, while communication is constrained by a proximity relationship in terms of another subset of variables. We build a flocking method for general nonlinear agent dynamics, by using at each agent a near-optimal control technique from artificial intelligence called optimistic planning. By defining the rewards to be optimized in a well-chosen way, the preservation of the interconnection topology is guaranteed, under a controllability assumption. We also give a practical variant of the algorithm that does not require to know the details of this assumption, and show that it works well in experiments on nonlinear agents.
Key words:  Multiagent systems  Flocking  Optimistic planning  Topology preservation