DeepAI AI Chat
Log In Sign Up

Complexity, Development, and Evolution in Morphogenetic Collective Systems

by   Hiroki Sayama, et al.
Binghamton University

Many living and non-living complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system's structure and behavior, (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization, (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors, and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.


page 1

page 2

page 3

page 4


Four Classes of Morphogenetic Collective Systems

We studied the roles of morphogenetic principles---heterogeneity of comp...

Guiding Designs of Self-Organizing Swarms: Interactive and Automated Approaches

Self-organization of heterogeneous particle swarms is rich in its dynami...

Engineered Self-Organization for Resilient Robot Self-Assembly with Minimal Surprise

In collective robotic systems, the automatic generation of controllers f...

Evolution of differentiated expression patterns in digital organisms

We investigate the evolutionary processes behind the development and opt...

Local Sharing and Sociality Effects on Wealth Inequality in a Simple Artificial Society

Redistribution of resources within a group as a method to reduce wealth ...

Forest structure in epigenetic landscapes

Morphogenesis is the biological process that causes the emergence and ch...

An artifcial life approach to studying niche differentiation in soundscape ecology

Artificial life simulations are an important tool in the study of ecolog...