Emergence of Collective Behavior in Evolving Populations of Flying Agents

Spector, Lee; Klein, Jon; Perry, Chris; Feinstein, Mark

doi:10.1007/s10710-005-7620-3

Cited by 73 publications

(22 citation statements)

References 7 publications

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“…This process was repeated regardless of whether the predator successfully captured the prey. The simplicity of the predator algorithm and relative simplicity of the prey algorithms supports the findings of earlier digital swarm studies that complex swarm behaviors can be described by simple rules applied over a group of locally-interacting agents [48,49].…”

Section: Evolved Predator and Prey Behaviorsupporting

confidence: 74%

Predator confusion is sufficient to evolve swarming behaviour

Olson

Hintze

Dyer

et al. 2013

J. R. Soc. Interface.

153

133

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Swarming behaviours in animals have been extensively studied owing to their implications for the evolution of cooperation, social cognition and predatorprey dynamics. An important goal of these studies is discerning which evolutionary pressures favour the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behaviour in prey. Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator's visual field, favouring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals. Finally, we provide evidence that when prey evolve swarming in response to predator confusion, there is a change in the shape of the functional response curve describing the predator's consumption rate as prey density increases. Thus, we show that a relatively simple perceptual constraint-predator confusion-could have pervasive evolutionary effects on prey behaviour, predator sensory mechanisms and the ecological interactions between predators and prey.

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Section: Evolved Predator and Prey Behaviorsupporting

confidence: 74%

Predator confusion is sufficient to evolve swarming behaviour

Olson

Hintze

Dyer

et al. 2013

J. R. Soc. Interface.

153

133

View full text Add to dashboard Cite

show abstract

“…That said, there are many other potential causes of swarming behavior that we do not address in this study. For example, some studies have investigated the evolution of predator behavior in response to prey density [56], the role of relative predator and prey speeds on the evolution of grouping behavior [66], elaborated upon the interaction between ecology and the evolution of grouping behavior [55,64], and explored the role of group vigilance (i.e., the "many eyes" hypothesis) in the evolution of grouping behavior [16,43]. Two recent studies have explored the coevolution of predator and prey behavior in the presence of the predator confusion effect [40,32], and found that the predator confusion effect is sufficient to select for the evolution of swarming behavior in the absence of any other group benefits.…”

Section: Related Workmentioning

confidence: 99%

Evolution of Swarming Behavior Is Shaped by How Predators Attack

Olson¹,

Knoester²,

Adami

2016

Artificial Life

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Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of "domains of danger." Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to densitydependent predation.

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“…For the second stage, we use a modified bird flocking algorithm [30,31]. Each growth cone is subject to a number of weighted 'urges', including 1. a destination urge (w d u u u d ), which is directed up the attractant gradient 2. an avoidance urge (w a u u u a ) to avoid already generated bundles 3. a velocity urge (w v u u u v ) to match the average velocity of nearby agents 4. a spacing urge (w s u u u s ) to avoid collisions with nearby agents 5. a center urge (w c u u u c ) to steer toward the centroid of nearby agents and keep the group cohesive Figure 1.1 Simulation of nerve fiber growth by single agents.…”

Section: Modified Flocking Algorithmmentioning

confidence: 99%

Coordinating swarms of microscopic agents to assemble complex structures

MacLennan

2018

Swarm Intelligence - Volume 1: Principles, Current Algorithms and Methods

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This chapter addresses the problem of coordinating very large swarms of microscopic agents to assemble complex, hierarchically structured physical systems. The agents might be microscopic robots or genetically engineered microorganisms. The approach we use is a form of artificial morphogenesis, which studies the self-organized morphogenetic processes in the developing embryo, by which billions of cells cooperate to create physical form, and abstracts these processes to control microscopic agents to assemble desired physical structures. We use an approach based on the description of morphogenetic processes by partial differential equations, which ensures that our morphogenetic algorithms will scale up to arbitrarily large swarms (millions or more). We present several simulation experiments demonstrating the coordination of massive swarms to construct complex objects.

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Emergence of Collective Behavior in Evolving Populations of Flying Agents

Cited by 73 publications

References 7 publications

Predator confusion is sufficient to evolve swarming behaviour

Predator confusion is sufficient to evolve swarming behaviour

Evolution of Swarming Behavior Is Shaped by How Predators Attack

Coordinating swarms of microscopic agents to assemble complex structures

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