Abstract
This paper describes the development of a hazard prediction system for Weapons of Mass Destruction (WMD) and discusses how three distinct Artificial Intelligence (AI) techniques were found to be necessary to enable operational use of such a system. The three techniques: Bayesian data fusion, Blackboard Architecture and Genetic Algorithm optimisation, are described, along with the novel modifications found necessary for their use in this domain. Furthermore, issues encountered and practical aspects of the development phase are discussed.
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© 2004 Springer-Verlag London Limited
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Thomas, P.A., Bull, M., Ravenscroft, G.R. (2004). WMD Hazard Prediction — Blending three AI techniques to produce a superior defence capability. In: Bramer, M., Ellis, R., Macintosh, A. (eds) Applications and Innovations in Intelligent Systems XI. SGAI 2003. Springer, London. https://doi.org/10.1007/978-1-4471-0643-2_9
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DOI: https://doi.org/10.1007/978-1-4471-0643-2_9
Publisher Name: Springer, London
Print ISBN: 978-1-85233-779-7
Online ISBN: 978-1-4471-0643-2
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