Authors
Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer
Description
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, ie 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 …