Knight, Brian, Woon, Fei Ling, Coenen, Frans and Allen, Tony (2005) Case based adaptation using interpolation over nominal values. In: Research and Development in Intelligent Systems XXI: Proceedings of AI-2004, the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer, London, UK, pp. 73-86. ISBN 9781852339074 (doi:https://doi.org/10.1007/1-84628-102-4_6)

Full text not available from this repository.

Abstract

In this paper we propose a method for interpolation over a set of retrieved cases in the adaptation phase of the case-based reasoning cycle. The method has two advantages over traditional systems: the first is that it can predict “new” instances, not yet present in the case base; the second is that it can predict solutions not present in the retrieval set. The method is a generalisation of Shepard’s Interpolation method, formulated as the minimisation of an error function defined in terms of distance metrics in the solution and problem spaces. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The method is illustrated in the paper with reference to the Irises classification problem. It is evaluated with reference to a simulated nominal value test problem, and to a benchmark case base from the travel domain. The algorithm is shown to out-perform conventional nearest neighbour methods on these problems. Finally, GSNN is shown to improve in efficiency when used in conjunction with a diverse retrieval algorithm.

Item Type:	Conference Proceedings
Title of Proceedings:	Research and Development in Intelligent Systems XXI: Proceedings of AI-2004, the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
Additional Information:	[1] This paper was first presented within Session 1b at AI-2004, the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence held at Queens' College, Cambridge, UK from 13th-15th December 2004. [2] ISBN: 978-1-85233-907-4 (Print); 978-1-84628-102-0 (Online)
Uncontrolled Keywords:	artificial intelligence, robotics, nominal values
Subjects:	Q Science > QA Mathematics
Pre-2014 Departments:	School of Computing & Mathematical Sciences School of Computing & Mathematical Sciences > Computer & Computational Science Research Group School of Computing & Mathematical Sciences > Department of Computer Science
Related URLs:	Publisher Publisher Organisation Organisation
Last Modified:	14 Oct 2016 09:02
URI:	http://gala.gre.ac.uk/id/eprint/935

Actions (login required)

View Item