Conference Paper

Decentralized Control and Interactive Design Methods for Large-Scale Heterogeneous Self-Organizing Swarms.

DOI: 10.1007/978-3-540-74913-4_68 Conference: Advances in Artificial Life, 9th European Conference, ECAL 2007, Lisbon, Portugal, September 10-14, 2007, Proceedings
Source: DBLP

ABSTRACT We present new methods of decentralized control and interactive design for artificial swarms of a large number of agents that
can spontaneously organize and maintain non-trivial heterogeneous formations. Our model assumes no elaborate sensing, computation,
or communication capabilities for each agent; the self-organization is achieved solely by simple kinetic interactions among
agents. Specifications of the final formations are indirectly and implicitly woven into a list of different kinetic parameter
settings and their proportions, which would be hard to obtain with a conventional top-down design method but may be designed
heuristically through interactive design processes.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Self-organization of heterogeneous particle swarms is rich in its dynamics but hard to design in a traditional top-down manner, especially when many types of kinetically distinct particles are involved. In this chapter, we discuss how we have been addressing this problem by (1) utilizing and enhancing interactive evolutionary design methods and (2) realizing spontaneous evolution of self organizing swarms within an artificial ecosystem.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: On the one hand, research in self-assembling systems, whether natural or artificial, has traditionally focused on pre-existing components endowed with fixed shapes. Biological development, by contrast, dynamically creates new cells that acquire selective adhesion properties through differentiation induced by their neighborhood. On the other hand, pattern formation phenomena are generally construed as orderly states of activity on top of a continuous 2-D or 3-D substrate. Yet, again, the spontaneous patterning of an organism into domains of gene expression arises within a multicellular medium in perpetual expansion and reshaping. Finally, both phenomena are often thought in terms of stochastic events, whether mixed components that randomly collide in self-assembly, or spots and stripes that occur unpredictably from instabilities in pattern formation. Here too, these notions need significant revision if they are to be extended and applied to embryogenesis. Cells are not randomly mixed but pre-positioned where cell division occurs. Genetic identity domains are not randomly distributed but highly regulated in number and position. In this work, I present a computational model of program-mable and reproducible artificial morphogenesis that integrates self-assembly and pattern formation under the control of a nonrandom gene regulatory network. The specialized properties of cells (division, adhesion, migration) are determined by the gene expression domains to which they belong, while at the same time these domains further expand and segment into subdomains due to the self-assembly of specialized cells. Through this model, I also promote a new discipline, embryomorphic engineering to solve the paradox of "meta-designing" decentralized, autonomous systems.
  • [Show abstract] [Hide abstract]
    ABSTRACT: On the one hand, phenomena of spontaneous pattern formation are generally random and repetitive, whereas, on the other hand, complicated heterogeneous architectures are the product of human design. Biological organisms are rather unique examples of natural systems that are both self-organized and architectured. Can we export their precise self-formation capabilities to technological systems? To address this issue, I have proposed a new field of research called “Morphogenetic Engineering” [9], which explores the artificial design and implementation of autonomous systems capable of developing complex, heterogeneous morphologies. Particular emphasis is set on the programmability and controllability of self-organization, properties that are often underappreciated in complex systems science-while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies.