Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Journal of Algorithms
Multiagent systems
Artificial Life
Swarm intelligence
Programmable self-assembly using biologically-inspired multiagent control
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
From Natural to Artificial Swarm Intelligence
From Natural to Artificial Swarm Intelligence
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Self-Organizing Formation Algorithm for Active Elements
SRDS '02 Proceedings of the 21st IEEE Symposium on Reliable Distributed Systems
Covering Rectilinear Polygons with Axis-Parallel Rectangles
SIAM Journal on Computing
Extending self-organizing particle systems to problem solving
Artificial Life
Extended Stigmergy in Collective Construction
IEEE Intelligent Systems
Collective-movement teams for cooperative problem solving
Integrated Computer-Aided Engineering - Performance Metrics for Intelligent Systems
Robot Search in 3D Swarm Construction
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
Adapting swarm intelligence for the self-assembly of prespecified artificial structures
Adapting swarm intelligence for the self-assembly of prespecified artificial structures
Robotics and Autonomous Systems
Editorial: Special issue on organic computing
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
L-system-driven self-assembly for swarm robotics
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
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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.