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Learning to use operational advice

Abstract
We address the problem of advice-taking in a given domain, in particular for building a game-playing program. Our approach to solving it strives for the application of machine learning techniques throughout, i.e. for avoiding knowledge elicitation by any other means as much as possible. In particular, we build upon existing work on the operationalization of advice by machine and assume that advice is already available in operational form. The relative importance of this advice is, however, not yet known can therefore not be utilized well by a program. This paper presents an approach to determine the relative importance for a given situation through reinforcement learning. We implemented this approach for the game of Hearts and gathered some empirical evidence on its usefulness through experiments. The results show that the programs built according to our approach learned to make good use of the given operational advice.
Type
Conference Contribution
Type of thesis
Series
Citation
Fürnkranz, J., Pfahringer, B., Kaindl, H. & Kramer, S.(2000). Learning to use operational advice. In W. Hom (Ed), ECAI 2000. Proceedings of the 14th European Conference on Artificial Intelligence, April 7-8, 2000(pp. 291-295). Amsterdam, The Netherlands: IOS press.
Date
2000
Publisher
IOS press
Degree
Supervisors
Rights
This is an author’s version of an article published in the proceedings 14th European Conference on Artificial Intelligence, April 7-8, 2000. Copyright © IOS press.