Swarm Intelligence &
Artificial Life

This website gives details on a fish tank demonstration that uses swarm intelligence techniques to simulate fish swimming. The demonstration allows users to add food for the fish as well as introduce a predator to the environment.

Why does this matter?

Both artificial life and swarm intelligence were initially inspired by nature which has shown evidence of being able to find solutions to highly complex problems. By using simplistic agents that do not require highly complex processing, these techniques can be used to great effect to solve a multitude of problems. By drawing on nature to inspire movement and using partical swarm techniques to search a search space, systems that are able to search extremely hostile environments can be developed cheaply with low cost parts.

References

[1] Reynolds, C. W. (1987) "Flocks, Herds, and Schools: A Distributed Behavioral Model", in Computer Graphics, 21(4) (SIGGRAPH '87 Conference Proceedings) pages 25-34.

[2] D. Xydas, D. Norcott, K. Warwick, B. Whalley, S. Nasuto, V. Becerra, M. Hammond, J. Downes, and S. Marshall, “Architecture for neuronal cell control of a mobile robot,” in European Robotics Symposium 2008 (H. Bruyninckx, L. Pˇreuˇcil, and M. Kulich, eds.), vol. 44 of Springer Tracts in Advanced Robotics, pp. 23–31, Springer Berlin Heidelberg, 2008.

[3] S. Nasuto and J. Bishop, “Of (zombie) mice and animats,” in Philosophy and Theory of Artificial Intel- ligence (V. C. Mu ̈ller, ed.), vol. 5 of Studies in Applied Philosophy, Epistemology and Rational Ethics, pp. 85–106, Springer Berlin Heidelberg, 2013.

[4] Kennedy, J. "Particle swarm optimization." Encyclopedia of Machine Learning (2010): 760-766. [View Online]

[5] Sina & Marie (2013) "Fish", image via The Noun Project Accessed: 2015-04-14 [View Online]

[6] Benn, O. (2012) "Shark", image via The Noun Project Accessed: 2015-04-14 [View Online]