Self-Organization and Artificial Life


Por: Gershenson, Carlos, Trianni, Vito, Werfel, Justin, Sayama, Hiroki

Publicada: 1 sep 2020
Resumen:
Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely ``soft'' (mathematical/computational modeling), ``hard'' (physical robots), and ``wet'' (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

Filiaciones:
Gershenson, Carlos:
 Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Ctr Ciencias Complejidad, Mexico City, DF, Mexico

Trianni, Vito:
 Italian Natl Res Council, Inst Cognit Sci & Technol, Rome, Italy

Werfel, Justin:
 Harvard Univ, Wyss Inst Biologically Inspired Engn, Cambridge, MA 02138 USA

Sayama, Hiroki:
 SUNY Binghamton, Ctr Collect Dynam Complex Syst, Binghamton, NY 13902 USA

 Waseda Univ, Waseda Innovat Lab, Tokyo, Japan
ISSN: 10645462
Editorial
MIT PRESS, 55 HAYWARD STREET, CAMBRIDGE, MA 02142 USA, Estados Unidos America
Tipo de documento: Article
Volumen: 26 Número: 3
Páginas: 391-408
WOS Id: 000571841900005
ID de PubMed: 32697161