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Behaviourism in Studying Swarms: Logical Models of Sensing and MotoringIntroduction

Behaviourism in Studying Swarms: Logical Models of Sensing and Motoring: Introduction [The notion of swarm intelligenceSwarm intelligence was first introduced in to denote the collective behaviour of decentralized and self-organized systems. Now, this notion is used in robotics to design a population of robots interacting locally among themselves and reacting locally to their environment with an emergent effect when all the local reactions of them are being cumulated into the one collective reaction. There are many natural examples of swarm intelligence: ant colonies, bee colonies, fish schooling, bird flocking and horse herding, bacterial colonies with a kind of social behaviour, multinucleated giant amoebae Physarum polycephalum, etc. The main feature of all these systems is that their individual agents behave locally without any centralized control, but their interactions lead to the emergence of global behaviour of the whole group that cannot be reduced to subsystems additively. By placing attractants and repellents at different sites we can manage and program the swarm behaviour. This opportunity allows us to design a biological computer on swarms.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Behaviourism in Studying Swarms: Logical Models of Sensing and MotoringIntroduction

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG, part of Springer Nature 2019
ISBN
978-3-319-91541-8
Pages
1 –26
DOI
10.1007/978-3-319-91542-5_1
Publisher site
See Chapter on Publisher Site

Abstract

[The notion of swarm intelligenceSwarm intelligence was first introduced in to denote the collective behaviour of decentralized and self-organized systems. Now, this notion is used in robotics to design a population of robots interacting locally among themselves and reacting locally to their environment with an emergent effect when all the local reactions of them are being cumulated into the one collective reaction. There are many natural examples of swarm intelligence: ant colonies, bee colonies, fish schooling, bird flocking and horse herding, bacterial colonies with a kind of social behaviour, multinucleated giant amoebae Physarum polycephalum, etc. The main feature of all these systems is that their individual agents behave locally without any centralized control, but their interactions lead to the emergence of global behaviour of the whole group that cannot be reduced to subsystems additively. By placing attractants and repellents at different sites we can manage and program the swarm behaviour. This opportunity allows us to design a biological computer on swarms.]

Published: May 26, 2018

Keywords: Physarum Polycephalum; Swarm Behavior; Biological Computer; Badhamia Utricularis; Performative Propositions

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