The Ecology of Collective Behavior in Ants

2019 ◽  
Vol 64 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Deborah M. Gordon
Keyword(s):  
Collective Behavior ◽  
Task Allocation ◽  
Pogonomyrmex Barbatus ◽  
Monomorium Pharaonis ◽  
Nest Choice ◽  
Distribution Of Resources ◽  
Regulation Of Activity ◽  
Pheidole Dentata

Nest choice in Temnothorax spp.; task allocation and the regulation of activity in Pheidole dentata, Pogonomyrmex barbatus, and Atta spp.; and trail networks in Monomorium pharaonis and Cephalotes goniodontus all provide examples of correspondences between the dynamics of the environment and the dynamics of collective behavior. Some important aspects of the dynamics of the environment include stability, the threat of rupture or disturbance, the ratio of inflow and outflow of resources or energy, and the distribution of resources. These correspond to the dynamics of collective behavior, including the extent of amplification, how feedback instigates and inhibits activity, and the extent to which the interactions that provide the information to regulate behavior are local or spatially centralized.

scholarly journals Movement, Encounter Rate, and Collective Behavior in Ant Colonies

2020 ◽  
Author(s):  
Deborah M Gordon
Keyword(s):  
Collective Behavior ◽  
Task Allocation ◽  
Social Insect ◽  
Local Density ◽  
Movement Patterns ◽  
Insect Behavior ◽  
Nest Size ◽  
Encounter Rate ◽  
Encounter Rates ◽  
Nest Choice

Abstract Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.

scholarly journals A Genetic Algorithm for Task Allocation Problem in the Internet of Things

2021 ◽  
pp. 1376-1385
Author(s):  
Hussein M. Burhan ◽  
Mustafa N. Abbas ◽  
Bara'a A. Attea
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Task Allocation ◽  
Memory Storage ◽  
The Internet ◽  
Everyday Objects ◽  
Distribution Of Resources ◽  
Energy Processing ◽  
The Individual ◽  
The Internet Of Things

In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonical definition for network lifetime in the IoT is to increase the period of cooperation between objects to carry out all the assigned tasks. The main contribution in this paper is to address the problem of task allocation in the IoT as an optimization problem with a lifetime-aware model. A genetic algorithm is proposed as a task allocation protocol. For the proposed algorithm, a problem-tailored individual representation and a modified uniform crossover are designed. Further, the individual initialization and perturbation operators (crossover and mutation) are designed so as to remedy the infeasibility of any solution located or reached by the proposed genetic algorithm. The results showed reasonable performance for the proposed genetic-based task allocation protocol. Further, the results prove the necessity for designing problem-specific operators instead of adopting the canonical counterparts.

CHEMOTAXONOMIC STUDIES OF ANTS: VOLATILE COMPOUNDS

1969 ◽  
Vol 101 (8) ◽  
pp. 879-883
Author(s):  
K. Vick ◽  
W. A. Drew ◽  
J. Young ◽  
D. J. McGurk ◽  
E. J. Eisenbraun
Keyword(s):  
Volatile Compounds ◽  
Pogonomyrmex Barbatus ◽  
Volatile Chemicals ◽  
Potential Value ◽  
Tapinoma Sessile ◽  
Pheidole Dentata ◽  
Acromyrmex Versicolor

AbstractThe gas chromatograms of some of the volatile chemicals found in the following ants are presented: Pogonomyrmex barbatus (F. Smith), Forelius foetida (Buckley), Conomyrma pyramica (Roger), Iridomyrmex pruinosus analis (E. André), Tapinoma sessile (Say), Trachymyrmex septentrionalis obscura (Wheeler), Pheidole dentata Mayr, Acromyrmex versicolor (Pergunda), and Novomessor cockerelli (E. André). The data and methods are presented to demonstrate their potential value as taxonomic characters in the study of ant systematics.

Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm

2018 ◽  
Vol 22 (5) ◽  
pp. 621-628 ◽  
Author(s):  
Motoaki Hiraga ◽  
Toshiyuki Yasuda ◽  
Kazuhiro Ohkura ◽  
◽  
Keyword(s):  
Collective Behavior ◽  
Task Allocation ◽  
Swarm Robotics ◽  
Local Environment ◽  
Coarse Grained ◽  
Neural Network Controller ◽  
Network Controller ◽  
Robotic Swarm ◽  
Limited Sensing

Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

Random isn't real: How the patchy distribution of ecological rewards may generate “incentive hope”

2019 ◽  
Vol 42 ◽  
Author(s):  
Laurel Symes ◽  
Thalia Wheatley
Keyword(s):  
Natural Environment ◽  
Random Distribution ◽  
Spatiotemporal Distribution ◽  
Patchy Distribution ◽  
Distribution Of Resources

AbstractAnselme & Güntürkün generate exciting new insights by integrating two disparate fields to explain why uncertain rewards produce strong motivational effects. Their conclusions are developed in a framework that assumes a random distribution of resources, uncommon in the natural environment. We argue that, by considering a realistically clumped spatiotemporal distribution of resources, their conclusions will be stronger and more complete.

Voronoi Analysis of a Soccer Game

2004 ◽  
Vol 9 (3) ◽  
pp. 233-240 ◽  
Author(s):  
S. Kim
Keyword(s):  
Collective Behavior ◽  
Quantitative Assessment ◽  
The Other ◽  
Analysis Method ◽  
Random System ◽  
Soccer Game ◽  
Voronoi Polygons ◽  
The Mean

This paper describes a Voronoi analysis method to analyze a soccer game. It is important for us to know the quantitative assessment of contribution done by a player or a team in the game as an individual or collective behavior. The mean numbers of vertices are reported to be 5–6, which is a little less than those of a perfect random system. Voronoi polygons areas can be used in evaluating the dominance of a team over the other. By introducing an excess Voronoi area, we can draw some fruitful results to appraise a player or a team rather quantitatively.

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