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The distributed control of self-assembly processes requires local behaviors that will cause initially unorganized components to form a desired goal structure. While important strides have been made in designing methods for self-assembling various geometric structures under idealized simulated conditions, many unaddressed issues remain in extending these methods to more complex environments. In this work, we discuss the self-assembly of prespecified 3D structures from blocks of different sizes. Block movements through a continuous environment are constrained by each other and simulated gravity, adding to the problem's difficulty. We present a solution that integrates three distinct techniques from the field of swarm intelligence: stigmergic pattern recognition, force-based movement control, and coordination via local message passing and state changes. Further, we empirically demonstrate that a stochastic component in the blocks' acceleration can aid in preventing persistent interference, and that the use of collective, flock-like movements can be beneficial in situations of low block availability. This work provides insight into the dynamics of continuous-space self-assembly, and is a step towards the design of methods for the automated "growth" of useful structures in real-world environments.

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Most construction of artificial, multicomponent structures is based upon an external entity that directs the assembly process, usually following a script/blueprint under centralized control. In contrast, recent research has focused increasingly on an alternative paradigm, inspired largely by the nest building behavior of social insects, in which components "self-assemble" into a given target structure. Adapting such a nature-inspired approach to precisely self-assemble artificial structures (bridge, building, etc.) presents a formidable challenge: one must create a set of local control rules to direct the behavior of the individual components/agents during the self-assembly process. In recent work, we developed a fully automated procedure that generates such rules, allowing a given structure to successfully self-assemble in a simulated environment having constrained, continuous motion; however, the resulting rule sets were typically quite large. In this article, we present a more effective methodology for automatic rule generation, which makes an attempt to parsimoniously capture both the repeating patterns that exist within a structure, and the behaviors necessary for appropriate coordination. We then empirically show that the procedure developed here generates sets of rules that are not only correct, but significantly reduced in size, relative to our earlier approach. Such rule sets allow for simpler agents that are nonetheless still capable of performing complex tasks, and therefore demonstrate the problem-solving potential of self-organized systems. ACM Reference Format:Grushin, A. and Reggia, J. A. 2010. Parsimonious rule generation for a nature-inspired approach to self-assembly.