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Learning to Use Operational Advice

Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer

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 and 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.

Keywords: Machine Learning, Reinforcement Learning

Citation: Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer: Learning to Use Operational Advice. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.291-295.

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ECAI-2000 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Humboldt University on behalf of Gesellschaft für Informatik.