Four classes of morphogenetic collective systems

Hiroki Sayama

doi:10.1162/978-0-262-32621-6-ch052

Four classes of morphogenetic collective systems

Hiroki Sayama

Watson Dean's Office

Binghamton University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

We studied the roles of morphogenetic principles - heterogeneity of components, dynamic differentiation/redifferentiation of components, and local information sharing among components - in the self-organization of morphogenetic collective systems. By incrementally introducing these principles to collectives, we defined four distinct classes of morphogenetic collective systems. Monte Carlo simulations were conducted using an extended version of the Swarm Chemistry model that was equipped with dynamic differentiation/re-differentiation and local information sharing capabilities. Self-organization of swarms was characterized by several kinetic and topological measurements, the latter of which were facilitated by a newly developed network-based method. Results of simulations revealed that, while heterogeneity of components had a strong impact on the structure and behavior of the swarms, dynamic differentiation/re-differentiation of components and local information sharing helped the swarms maintain spatially adjacent, coherent organization.

Original language	English
Title of host publication	Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
Editors	Hiroki Sayama, John Rieffel, Sebastian Risi, Rene Doursat, Hod Lipson
Publisher	MIT Press Journals
Pages	320-327
Number of pages	8
ISBN (Electronic)	9780262326216
DOIs	https://doi.org/10.1162/978-0-262-32621-6-ch052
State	Published - 2014
Event	14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014 - Manhattan, United States Duration: Jul 30 2014 → Aug 2 2014

Publication series

Name	Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014

Conference

Conference	14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
Country/Territory	United States
City	Manhattan
Period	07/30/14 → 08/2/14

Access to Document

10.1162/978-0-262-32621-6-ch052

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Sayama, H. (2014). Four classes of morphogenetic collective systems. In H. Sayama, J. Rieffel, S. Risi, R. Doursat, & H. Lipson (Eds.), Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014 (pp. 320-327). (Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014). MIT Press Journals. https://doi.org/10.1162/978-0-262-32621-6-ch052

Sayama, Hiroki. / Four classes of morphogenetic collective systems. Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. editor / Hiroki Sayama ; John Rieffel ; Sebastian Risi ; Rene Doursat ; Hod Lipson. MIT Press Journals, 2014. pp. 320-327 (Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014).

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abstract = "We studied the roles of morphogenetic principles - heterogeneity of components, dynamic differentiation/redifferentiation of components, and local information sharing among components - in the self-organization of morphogenetic collective systems. By incrementally introducing these principles to collectives, we defined four distinct classes of morphogenetic collective systems. Monte Carlo simulations were conducted using an extended version of the Swarm Chemistry model that was equipped with dynamic differentiation/re-differentiation and local information sharing capabilities. Self-organization of swarms was characterized by several kinetic and topological measurements, the latter of which were facilitated by a newly developed network-based method. Results of simulations revealed that, while heterogeneity of components had a strong impact on the structure and behavior of the swarms, dynamic differentiation/re-differentiation of components and local information sharing helped the swarms maintain spatially adjacent, coherent organization.",

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note = "Publisher Copyright: {\textcopyright} Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. All rights reserved.; 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014 ; Conference date: 30-07-2014 Through 02-08-2014",

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Sayama, H 2014, Four classes of morphogenetic collective systems. in H Sayama, J Rieffel, S Risi, R Doursat & H Lipson (eds), Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, MIT Press Journals, pp. 320-327, 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, Manhattan, United States, 07/30/14. https://doi.org/10.1162/978-0-262-32621-6-ch052

Four classes of morphogenetic collective systems. / Sayama, Hiroki.
Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. ed. / Hiroki Sayama; John Rieffel; Sebastian Risi; Rene Doursat; Hod Lipson. MIT Press Journals, 2014. p. 320-327 (Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We studied the roles of morphogenetic principles - heterogeneity of components, dynamic differentiation/redifferentiation of components, and local information sharing among components - in the self-organization of morphogenetic collective systems. By incrementally introducing these principles to collectives, we defined four distinct classes of morphogenetic collective systems. Monte Carlo simulations were conducted using an extended version of the Swarm Chemistry model that was equipped with dynamic differentiation/re-differentiation and local information sharing capabilities. Self-organization of swarms was characterized by several kinetic and topological measurements, the latter of which were facilitated by a newly developed network-based method. Results of simulations revealed that, while heterogeneity of components had a strong impact on the structure and behavior of the swarms, dynamic differentiation/re-differentiation of components and local information sharing helped the swarms maintain spatially adjacent, coherent organization.

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Sayama H. Four classes of morphogenetic collective systems. In Sayama H, Rieffel J, Risi S, Doursat R, Lipson H, editors, Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. MIT Press Journals. 2014. p. 320-327. (Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014). doi: 10.1162/978-0-262-32621-6-ch052

Four classes of morphogenetic collective systems

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