Supervised self-organization of homogeneous swarms using ergodic projections of markov chains

I. Chattopadhyay, Asok Ray

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.

Original languageEnglish (US)
Pages (from-to)1505-1515
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume39
Issue number6
DOIs
StatePublished - Oct 8 2009

Fingerprint

Markov Chains
Markov processes
Population Density
Specifications

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

@article{2e8b59f1e5474858b06e7cb8520486ce,
title = "Supervised self-organization of homogeneous swarms using ergodic projections of markov chains",
abstract = "This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.",
author = "I. Chattopadhyay and Asok Ray",
year = "2009",
month = "10",
day = "8",
doi = "10.1109/TSMCB.2009.2020173",
language = "English (US)",
volume = "39",
pages = "1505--1515",
journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",
issn = "1083-4419",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",

}

Supervised self-organization of homogeneous swarms using ergodic projections of markov chains. / Chattopadhyay, I.; Ray, Asok.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 39, No. 6, 08.10.2009, p. 1505-1515.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Supervised self-organization of homogeneous swarms using ergodic projections of markov chains

AU - Chattopadhyay, I.

AU - Ray, Asok

PY - 2009/10/8

Y1 - 2009/10/8

N2 - This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.

AB - This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.

UR - http://www.scopus.com/inward/record.url?scp=70349615413&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349615413&partnerID=8YFLogxK

U2 - 10.1109/TSMCB.2009.2020173

DO - 10.1109/TSMCB.2009.2020173

M3 - Article

C2 - 19447731

AN - SCOPUS:70349615413

VL - 39

SP - 1505

EP - 1515

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

SN - 1083-4419

IS - 6

ER -