Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Artificial fishes: physics, locomotion, perception, behavior
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Computational models for the formation of protocell structures
Artificial Life
Swarm intelligence
Control and Coordination of Multiple Mobile Robots in Manipulation and Material Handling Tasks
The Sixth International Symposium on Experimental Robotics VI
Extending self-organizing particle systems to problem solving
Artificial Life
Ants and reinforcement learning: a case study in routing in dynamic networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Optimization based on bacterial chemotaxis
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robotics and Autonomous Systems
A distributed learning algorithm for particle systems
Integrated Computer-Aided Engineering
Parsimonious rule generation for a nature-inspired approach to self-assembly
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Self-organizing particle systems ("swarms") consist of numerous autonomous, reflexive agents ("particles") whose collective movements through space are determined primarily by local influences. Such systems have been traditionally used to simulate groups of animals and other biological phenomena. The simple nature of these systems limits their applications in other areas. We believe that by extending these systems and combining them with a top-down approach, they can be transformed into a general problem-solving technique. In this work we present an agent architecture derived by adding goal-directed control mechanisms to particles that allow them to switch between multiple dynamics according to different situations and to keep and propagate information relevant to solving a problem. Simulations of two different tasks related to search for and transportation of objects show that agents are able to not only effectively solve the assigned problems, but that they do so in a more efficient manner than similar but independently moving agents. Further, collectively moving agents display coordination emerging purely from their local interactions and this cooperation provides a clear advantage while solving a problem as a team. These results show that the self-organizing behavior of particle systems can be extended to support problem solving in various areas such as coordinated robotic teams.