Swarm intelligence for achieving the global maximum using spatio-temporal Gaussian processes

Jongeun Choi, Joonho Lee, Songhwai Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Citations (Scopus)


This paper presents a novel class of self-organizing multi-agent systems that form a swarm and learn a spatiotemporal 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 spatiotemporal 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.

Original languageEnglish
Title of host publication2008 American Control Conference, ACC
Number of pages6
Publication statusPublished - 2008
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: 2008 Jun 112008 Jun 13

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2008 American Control Conference, ACC
Country/TerritoryUnited States
CitySeattle, WA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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