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Evolving flocking in embodied agents based on local and global application of Reynolds’ rules
- Source :
- PLoS ONE, PLoS ONE, Vol 14, Iss 10, p e0224376 (2019), Ramos, R P, Oliveira, S M, Vieira, S M & Christensen, A L 2019, ' Evolving flocking in embodied agents based on local and global application of Reynolds' rules ', PLOS ONE, vol. 14, no. 10 . https://doi.org/10.1371/journal.pone.0224376, Repositório Científico de Acesso Aberto de Portugal, Repositório Científico de Acesso Aberto de Portugal (RCAAP), instacron:RCAAP
- Publication Year :
- 2019
- Publisher :
- Public Library of Science (PLoS), 2019.
-
Abstract
- In large scale systems of embodied agents, such as robot swarms, the ability to flock is essential in many tasks. However, the conditions necessary to artificially evolve self-organised flocking behaviours remain unknown. In this paper, we study and demonstrate how evolutionary techniques can be used to synthesise flocking behaviours, in particular, how fitness functions should be designed to evolve high-performing controllers. We start by considering Reynolds' seminal work on flocking, the boids model, and design three components of a fitness function that are directly based on his three local rules to enforce local separation, cohesion and alignment. Results show that embedding Reynolds' rules in the fitness function can lead to the successful evolution of flocking behaviours. However, only local, fragmented flocking behaviours tend to evolve when fitness scores are based on the individuals' conformity to Reynolds' rules. We therefore modify the components of the fitness function so that they consider the entire group of agents simultaneously, and find that the resulting behaviours lead to global flocking. Furthermore, the results show that alignment need not be explicitly rewarded to successfully evolve flocking. Our study thus represents a significant step towards the use of evolutionary techniques to synthesise collective behaviours for tasks in which embodied agents need to move as a single, cohesive group. info:eu-repo/semantics/publishedVersion
- Subjects :
- 0209 industrial biotechnology
Inertia
Computer science
Statistics as Topic
Social Sciences
Predation
02 engineering and technology
Conformity
020901 industrial engineering & automation
0202 electrical engineering, electronic engineering, information engineering
Psychology
Engenharia e Tecnologia::Outras Engenharias e Tecnologias [Domínio/Área Científica]
Foraging
Flocking (texture)
media_common
Multidisciplinary
Fitness function
Behavior, Animal
Animal Behavior
Ecology
Physics
Classical Mechanics
Robotics
Trophic Interactions
Community Ecology
Physical Sciences
Medicine
Engineering and Technology
020201 artificial intelligence & image processing
Robots
Algorithms
Research Article
Computer and Information Sciences
Science
media_common.quotation_subject
Models, Biological
Motion
Artificial Intelligence
Animals
Computer Simulation
Social Behavior
Artificial Neural Networks
Computational Neuroscience
Behavior
Robotic Behavior
Flocking (behavior)
business.industry
Mechanical Engineering
Ecology and Environmental Sciences
Ciências Naturais::Ciências da Computação e da Informação [Domínio/Área Científica]
Biology and Life Sciences
Computational Biology
Collective Animal Behavior
Embodied cognition
Boids
Robot
Artificial intelligence
Flock
Collective animal behavior
business
Zoology
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....d920806991ecd1b26be53ae6ac8d5cba