Indirect Swarm Control: Characterization and Analysis of Emergent Swarm Behaviors
arxiv(2023)
Abstract
Emergence and emergent behaviors are often defined as cases where changes in
local interactions between agents at a lower level effectively changes what
occurs in the higher level of the system (i.e., the whole swarm) and its
properties. However, the manner in which these collective emergent behaviors
self-organize is less understood. The focus of this paper is in presenting a
new framework for characterizing the conditions that lead to different
macrostates and how to predict/analyze their macroscopic properties, allowing
us to indirectly engineer the same behaviors from the bottom up by tuning their
environmental conditions rather than local interaction rules. We then apply
this framework to a simple system of binary sensing and acting agents as an
example to see if a re-framing of this swarms problem can help us push the
state of the art forward. By first creating some working definitions of
macrostates in a particular swarm system, we show how agent-based modeling may
be combined with control theory to enable a generalized understanding of
controllable emergent processes without needing to simulate everything. Whereas
phase diagrams can generally only be created through Monte Carlo simulations or
sweeping through ranges of parameters in a simulator, we develop closed-form
functions that can immediately produce them revealing an infinite set of swarm
parameter combinations that can lead to a specifically chosen self-organized
behavior. While the exact methods are still under development, we believe
simply laying out a potential path towards solutions that have evaded our
traditional methods using a novel method is worth considering. Our results are
characterized through both simulations and real experiments on ground robots.
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