Title :
Swarm intelligence for achieving the global maximum using spatio-temporal Gaussian processes
Author :
Choi, Jongeun ; Lee, Joonho ; Oh, Songhwai
Author_Institution :
Dept. of Mech. Eng., Michigan State Univ., East Lansing, MI
Abstract :
This paper presents a novel class of self-organizing multi-agent systems that form a swarm and learn a spatio- temporal process through noisy measurements from neighbors for various global goals. The physical spatio-temporal process of interest is modeled by a spatio-temporal Gaussian process. Each agent maintains its own posterior predictive statistics of the Gaussian process based on measurements from neighbors. A set of biologically inspired navigation strategies are identified from the posterior predictive statistics. A unified way to prescribe a global goal for the group of agents is presented. A reference trajectory state that guides agents to achieve the maximum of the objective function is proposed. A switching protocol is proposed for achieving the global maximum of a spatio- temporal Gaussian process over the surveillance region. The usefulness of the proposed multi-agent system with respect to various global goals is demonstrated by several numerical examples.
Keywords :
Gaussian processes; multi-agent systems; statistical analysis; biologically inspired navigation strategy; global maximum; posterior predictive statistics; reference trajectory state; self-organizing multiagent system; spatio-temporal Gaussian process; swarm intelligence; switching protocol; Gaussian processes; Intelligent robots; Mobile agents; Multiagent systems; Navigation; Particle swarm optimization; Protocols; Spatiotemporal phenomena; Statistics; Surveillance;
Conference_Titel :
American Control Conference, 2008
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-2078-0
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2008.4586480