BACKGROUND
Emergence continues to lack a systemic mathematical definition that allows for rigorous emergence behavior testing, despite the decades of work that has been done. Physical flocks and swarms that contain both deterministic elements and stochastic perturbations, such as measurement noise, would benefit from such precise emergence detection. This would include testing for behaviors such as flock formation, combined cohesion, velocity alignment, and collision avoidance. For emergence to occur, there must be spatial or temporal extent, independent componence, and non-linear behavior. After defining emergence, it is important to develop swarm measurement and analysis in fields that need emergent behavior detection.
SUMMARY OF TECHNOLOGY
Researchers at Oklahoma State University have developed a systematic test for emergent behavior detection from experimental data. In this approach, an emergence definition grounded in complex theory is applied, and an embodiment-agnostic test inspired by an information theoretic construction is developed. It is then implemented through digital compression techniques on both robotic and biological examples of physical swarming. The results of extensive testing have indicated that the emergence test smoothly detects levels of structure and noise and has a computational footprint amenable to handheld hardware implementations. Within this test, a macrostate, or knee singular value, of a spatio-temporal data matrix to analyze emergence is introduced. The macrostate is computed in various simulations and experimental criteria of emergence is deduced. The development of a core mathematical routine needed to provide tools that detect structure in coordinated motion across various measurements of trajectories could be very beneficial in applications that need emergent behavior detection.
POTENTIAL AREAS OF APPLICATION
MAIN ADVANTAGES
STAGE OF DEVELOPMENT
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