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
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.
Available from: Nicola Capodieci
- "As described in Section II, evolution in swarm chemistry has already been introduced in previous literature, but it still remains a topic that deserves deeper discussion, especially with respect to the evolutionary mechanisms involved and with regard to how to design systems are able to self-evaluate whether the chemical swarm manages to create new shapes or to show new behaviours. The element of novelty of this paper is to present an Artificial Immune System (AIS) inspired algorithm that exploits an ad-hoc idiotypic network  for controlling the evolution of single particles composing the swarm in an artificial chemistry environment: the emergent property of immune networks of exhibiting cognition  proved to be extremely useful for implementing a Self- Organising system that was able to discover new shapes and new behaviours using the same tool described in . Starting from a restricted initial set of known recipes, our algorithm combines and modifies recipes to create shapes that were not present in the initial sets. "
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ABSTRACT: Morphogenetic engineering represents an interesting field in which models, frameworks and algorithms can be tested in order to study how self-properties and emergent behaviours can arise in potentially complex and distributed systems. In this field, the morphogenetic model we will refer to is swarm chemistry, since a well known challenge in this dynamical process concerns discovering mechanisms for providing evolution within coalescing systems of particles. These systems consist in sets of moving particles able to self-organise in order to create shapes or geometrical formations that provide robustness towards external perturbations. We present a novel mechanism for providing evolutionary features in swarm chemistry that takes inspiration from artificial immune system literature, more specifically regarding idiotypic networks. Starting from a restricted set of chemical recipes, we show that the system evolves to new states, using an autonomous method of detecting new shapes and behaviours free from any human interaction.
Available from: Hiroki Sayama
- "those old versions used a fixed number of swarms in each generation (though it was changeable manually in version 1.1). Moreover, the mutation operator became available only in version 1.1 (which was optional by default), and all the design results reported so far in ,  were produced using the mixing operator only. To address the problems mentioned above, we developed a new version of the Swarm Chemistry simulator, version 1.2. "
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ABSTRACT: We developed Swarm Chemistry 1.2, a new version of the Swarm Chemistry simulator with an enhanced architecture of interactive evolutionary design for exploring heterogeneous self-propelled particle swarm dynamics. In the new version, each evolutionary operator acts locally and visually to part of the population of swarms on a screen, without causing entire generation changes that were used in earlier versions. This new architecture is intended to represent cognitive actions in human thinking and decision making processes more naturally. We tested the effectiveness of the new architecture through an in-class experiment with college students participating as designers as well as evaluators of swarms. We also measured the effects of mixing and mutation operators to the performance improvement of the design processes. The students' responses showed that the designs produced using the new version received significantly higher ratings from students than those produced using the old one, and also that each of the mixing and mutation operators contributed nearly independently to the improvement of the design quality. These results demonstrate the effectiveness of the new architecture of interactive evolutionary design, as well as the importance of having diverse options of action (i.e., multiple evolutionary operators in this context) in iterative design and decision making processes. This work also presents one of the few examples of human-involved experiments on the statistical evaluation of artificial lifeforms, whose quality (such as ldquolivingnessldquo) would be hard to assess without using human cognition at this point.
Available from: mit.edu
- "The evo-devo works of , , or with lesser morphogenetic abilities , , are among these few notable achievements. Other interesting studies have explored pairs of mechanisms: SA and PF, no GR—selfassembly based on cell adhesion and signaling pattern formation , but using only predefined cell types without internal genetic variables, e.g., ; PF and GR, no SA—non-trivial pattern formation from instruction-driven intercellular signaling , but on a fixed lattice without self-assembling motion, e.g., , ; SA and GR, no PF—heterogeneous swarms of genetically programmed, self-assembling particles, but in empty space without mutual differentiation signals, e.g., . Naturally, beyond the proof-of-concept simulations presented here, a more systematic exploration is needed. "
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ABSTRACT: On the one hand, natural phenomena of spontane- ous pattern formation are generally random and repetitive, whereas, on the other hand, complicated heterogeneous architec- tures are the product of human design. The only examples of self- organized and structured systems are biological organisms pro- duced by development. Can we export their precise self-formation capabilities to computing systems? This work proposes an "em- bryomorphic engineering" approach inspired by evo-devo to solve the paradoxical challenge of planning autonomous systems. Its goal is to artificially reconstruct complex morphogenesis by integrating three fundamental ingredients: self-assembly and pattern formation under genetic regulation. It presents a spatial computational agent-based model that can be equivalently con- strued as (a) moving cellular automata, in which cell rearrange- ment is influenced by the pattern they form, or (b) heterogeneous swarm motion, in which agents differentiate into patterns accord- ing to their location. It offers a new abstract framework to ex- plore the causal and programmable link from genotype to pheno- type that is needed in many emerging computational domains, such as amorphous computing or artificial embryogeny.
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