Abstract
Rules determine the functionality of a given system, in either natural or man-made systems. Man-made systems, such as computer applications, use a set of known rules to control the behaviours applied in a strict manner. Biological or natural systems employ unknown rules, these being undiscovered rules which are more complex. These rules are unknown due to the inability to determine how they are applied, unless observed by a third party. The swarm is one of the largest naturally observed systems, with bird flocks and ant colonies being the most notable. It is a collection or group of individuals who use behaviours to complete a given goal or objective. It is the aim of this paper to present rule discovery methods for the mining of these unknown rules within a swarm system, employing a bird flock simulation environment to gather data.
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References
Stoops, D., Wang, H., Moore, G., Bi, Y.: Rule Discovery from Swarm Systems. In: Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, pp. 3544–3549 (2009)
Flake, G.W.: The computational beauty of nature, 1st edn. MIT Press, Cambridge (1998)
Kennedy, J., Eberhart, R.: Swarm Intelligence, 1st edn. Morgan Kaufmann, San Francisco (2001)
Reynolds, C.W.: Flocks, Herds, and Schools: A Distributed Behavioral Model. Computer Graphics 21(4), 25–34 (1987)
Adami, C.: Introduction to Artificial Life, 1st edn. Springer, Berlin (1998)
Dorigo, M.: Swarms. Swarm Intelligence 1(1), 1–2 (2007)
de Almeida Prado, G., Toracio, A., Pozo, A.T.R.: Multiple objective particle swarm forclassification-rule discovery (2007)
Zhang, M., Shao, C., Li, M., Sun, J.: Mining Classification Rule with ArtificialFish Swarm (2006)
Bo, L., Abbas, H.A., McKay, B.: Classification rule discovery with ant colony optimization (2003)
NASA, Autonomous Nano Technology Swarm, NASA ANTS Homepage, http://ants.gsfc.nasa.gov/
Hall, M., Holmes, G., Frank, E.: Generating Rule Sets from Model Trees. In: Foo, N.Y. (ed.) AI 1999. LNCS, vol. 1747, pp. 1–12. Springer, Heidelberg (1999)
Witten, I.H.: Data Mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, London (2005)
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Stoops, D., Wang, H., Moore, G., Bi, Y. (2010). Behavioural Rule Discovery from Swarm Systems. In: Bi, Y., Williams, MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science(), vol 6291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15280-1_46
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DOI: https://doi.org/10.1007/978-3-642-15280-1_46
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