Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments-Reference-Cited by-同舟云学术

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Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

Published:2016-08-08 Issue:3 Volume:21 Page:317-323
ISSN:1433-5298
Container-title:Artificial Life and Robotics
language:en
Short-container-title:Artif Life Robotics

Author:

Drozd Aleksandr,Witkowski Olaf,Matsuoka Satoshi,Ikegami Takashi

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,General Biochemistry, Genetics and Molecular Biology

Link

http://link.springer.com/content/pdf/10.1007/s10015-016-0303-8.pdf

Reference27 articles.

1. Ballerini M, Cabibbo N, Candelier R, Cavagna A, Cisbani E, Giardina I, Lecomte V, Orlandi A, Parisi G, Procaccini A, Viale M, Zdravkovic V (2008) Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proc Natl Acad Sci 105(4):1232–1237

2. Barnes J, Hut P (1986) A hierarchical o(n log n) force-calculation algorithm. Nature 324:446–449

3. Blackston D, Suel T (1997) Highly portable and efficient implementations of parallel adaptive n-body methods. In: In SC’97 :1–20

4. Board OAR (2013) OpenMP Application Program Interface, Version 4.0. http://www.openmp.org/mp-documents/OpenMP4.0.0.pdf

5. Budrene EO, Berg HC et al (1991) Complex patterns formed by motile cells of Escherichia coli. Nature 349(6310):630–633

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1. How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles;Artificial Life;2019-05

2. Life as an emergent phenomenon: studies from a large-scale boid simulation and web data;Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences;2017-11-13

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