TY - GEN
T1 - Four classes of morphogenetic collective systems
AU - Sayama, Hiroki
N1 - Funding Information:
This material is based upon work supported by the US National Science Foundation under Grant No. 1319152.
Publisher Copyright:
© Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
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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M3 - Conference contribution
AN - SCOPUS:85086262740
T3 - Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
SP - 320
EP - 327
BT - Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
A2 - Sayama, Hiroki
A2 - Rieffel, John
A2 - Risi, Sebastian
A2 - Doursat, Rene
A2 - Lipson, Hod
PB - MIT Press Journals
T2 - 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
Y2 - 30 July 2014 through 2 August 2014
ER -