Key Behavioural Factors in a Self-Organised Fish School Model

Gautrais, Jacques; Jost, Christian; Théraulaz, Guy

doi:10.5735/086.045.0505

Cited by 64 publications

(66 citation statements)

References 43 publications

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“…A major improvement of our understanding of collective motion in fish has thus been reached through the effort on modelling fish school behaviour using agent-based models, which has permitted researchers to set hypothetical individual rules and test through numerical simulations the resulting behaviour at the school level. The first successful attempts of such models [44][45][46][47][48], sometimes coming from unexpected fields such as computer animation [49], demonstrated that despite some methodological flaws (few fishes leading to a dominant border effect [43]; poor exploration of initial conditions, as pointed out later [50]), simple individual behavioural rules often referred to as 'traffic rules', with appropriately tuned parameters, were able to reproduce the collective behaviour of a fish school with its main features (cohesion, polarity, shape and structure).…”

Section: From Individual To Collective Behaviourmentioning

confidence: 99%

“…It may be expressed in a modelling framework as a combination of pair interactions, e.g. by summing [69] or averaging [50]. The limited cognitive abilities at work nonetheless make suspect some utterly complex calculations over a large number of influential individuals.…”

Section: Determining Which Ways Are Used By the Individual To Integramentioning

confidence: 99%

“…manoeuvrability constraints, has a dramatic impact on individual trajectories, in particular the path curvature, and also on poupulationlevel properties, as it leads to increasing group sizes. Similarly, in a self-propelled particles model [50], varying the maximum turning rate is a means of selecting the accessible states of the system. The spontaneous motion, i.e.…”

Section: Characterizing the Spontaneous Motion Of Fishmentioning

confidence: 99%

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From behavioural analyses to models of collective motion in fish schools

et al. 2012

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Fish schooling is a phenomenon of long-lasting interest in ethology and ecology, widely spread across taxa and ecological contexts, and has attracted much interest from statistical physics and theoretical biology as a case of self-organized behaviour. One topic of intense interest is the search of specific behavioural mechanisms at stake at the individual level and from which the school properties emerges. This is fundamental for understanding how selective pressure acting at the individual level promotes adaptive properties of schools and in trying to disambiguate functional properties from non-adaptive epiphenomena. Decades of studies on collective motion by means of individual-based modelling have allowed a qualitative understanding of the self-organization processes leading to collective properties at school level, and provided an insight into the behavioural mechanisms that result in coordinated motion. Here, we emphasize a set of paradigmatic modelling assumptions whose validity remains unclear, both from a behavioural point of view and in terms of quantitative agreement between model outcome and empirical data. We advocate for a specific and biologically oriented re-examination of these assumptions through experimental-based behavioural analysis and modelling.

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Section: From Individual To Collective Behaviourmentioning

confidence: 99%

Section: Determining Which Ways Are Used By the Individual To Integramentioning

confidence: 99%

Section: Characterizing the Spontaneous Motion Of Fishmentioning

confidence: 99%

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From behavioural analyses to models of collective motion in fish schools

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“…Important elements of behavioral rules are the nature of the perceived information and the affected behavioral traits. [24,25,26]. A frequent assumption in models is that the information, perceived by the particles, is restricted to the velocity of their neighbors.…”

Section: Inroductionmentioning

confidence: 99%

Transitions in a self-propelled-particles model with coupling of accelerations

2009

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We consider a three dimensional, generalized version of the original SPP model for collective motion. By extending the factors influencing the ordering, we investigate the case when the movement of the self-propelled particles (SPP-s) depends on both the velocity and the acceleration of the neighboring particles, instead of being determined solely by the former one. By changing the value of a weight parameter s determining the relative influence of the velocity and the acceleration terms, the system undergoes a kinetic phase transition as a function of a behavioral pattern. Below a critical value of s the system exhibits disordered motion, while above it the dynamics resembles that of the SPP model. We argue that in nature evolutionary processes can drive the strategy variable s towards the critical point, where information exchange between the units of a system is maximal. InroductionCollective motion of organisms (e.g. fish schools, bird flocks, bacterial colonies) exhibits a large variety of emergent phenomena [1,2,3,4,5,6,7,8]. Synchronized motion, symmetrical group formations (e.g., V shaped) or swirling patterns emerge in spite of the apparently simple behavioral rules of the individual flock members [9,10]. The self propelled particles (SPP) model was proposed by Vicsek et al.[11] to describe the onset of ordered motion within a group of self-propelled particles in the presence of perturbations. Taking into the effects of fluctuations inevitably present in biological systems was an essential generalization of the prior deterministic flocking models such as that of Reynolds [12]. The original model considers point-like particles moving at constant velocity on a two dimensional surface with periodic boundary conditions. The only rule is that, at each time step, every particle approximates, with some uncertainty, the average direction of motion of the particles within its neighborhood of radius R. This model exhibits spontaneous self-organization; by decreasing the noise parameter, the system undergoes a kinetic phase transition from a disordered state to an ordered one where all the particles move approximately in the same direction. Due its simplicity and analogy with biological systems comprised of many, locally interacting particles, the SPP model soon became a reference model for the flocking behavior of organisms [13,14,15,16,17,18,19,20,21,22,23].The individual based behavioral rules, determining collective motion, are of particular interest. Important elements of behavioral rules are the nature of the perceived information and the affected behavioral traits. [24,25,26]. A frequent assumption in models is that the information, perceived by the particles, is restricted to the velocity of their neighbors. The interaction range is usually defined by metric distances, but Ballerini et al. [24] recently showed that topological distance is the one determining the flocking of starlings. The assumption of reflecting on the momentary velocity only may not be enough for adequately describing a number o...

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“…If both criteria score high, however, then the controller does demonstrate its ability to detect and follow a fast moving target in an autonomous way. Note that similar proposals exist in collective robotics [15] and social animals [16], though our criteria are specific to the column formation task and cannot be derived from those for more general settings [15], [16].…”

Section: Task Definition and Assessment Criteriamentioning

confidence: 99%

On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics

Kouno

Montanier

Takano

et al. 2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-This paper aims at building autonomous controllers for swarm robots, specifically aimed at enforcing a given shape formation, here a column formation. The proposed approach features two main characteristics. Firstly, a state-of-the-art evolutionary setting is used to achieve the on-board optimization of the controller, avoiding any simulator-based approach. Secondly, as the cost of physical experiments might be prohibitively high for plain evolutionary approaches, a data mining approach is achieved on the top of evolution; rule discovery is used to discover the most promising regions in the controller search space. The merits of the approach are experimentally validated using a 5 robot formation, showing that the hybrid evolutionary learning process outperforms evolution alone in terms of swarm speed and shape quality.

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Key Behavioural Factors in a Self-Organised Fish School Model

Cited by 64 publications

References 43 publications

From behavioural analyses to models of collective motion in fish schools

From behavioural analyses to models of collective motion in fish schools

Transitions in a self-propelled-particles model with coupling of accelerations

On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics

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