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Experience with Rule Induction and k-Nearest Neighbour Methods for Interface Agents that Learn

Payne, Terry R., Edwards, Peter and Green, Claire L., Rammamoorthy, C.V., Wah, Benjamin, Bastani, Farokh B. and Spooner, David(eds.) (1997) Experience with Rule Induction and k-Nearest Neighbour Methods for Interface Agents that Learn IEEE Transactions on Knowledge and Data Engineering, 9, (2), pp. 329-335.

Record type: Article


Interface Agents are being developed to assist users with a variety of tasks. To perform effectively, such agents need knowledge of user preferences. An agent architecture has been developed which observes a user performing tasks, and identifies features which can be used as training data by a learning algorithm. Using the learned profile, an agent can give advice to the user on dealing with new situations. The architecture has been applied to two different information filtering domains: classifying incoming mail messages (Magi) and identifying interesting USENET news articles (UNA). This paper describes the architecture and examines the results of experimentation with different learning algorithms and different feature extraction strategies within these domains.

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Published date: 1997
Organisations: Electronics & Computer Science


Local EPrints ID: 257258
ISSN: 1041-4347
PURE UUID: e84a4ad5-19ff-4efd-bac4-f34799d77110

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Date deposited: 29 Jan 2003
Last modified: 18 Jul 2017 09:39

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Author: Terry R. Payne
Author: Peter Edwards
Author: Claire L. Green
Editor: C.V. Rammamoorthy
Editor: Benjamin Wah
Editor: Farokh B. Bastani
Editor: David Spooner

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