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Extending neuro-evolutionary preference learning through player modeling | IEEE Conference Publication | IEEE Xplore

Extending neuro-evolutionary preference learning through player modeling


Abstract:

In this paper we propose a methodology for improving the accuracy of models that predict self-reported player pairwise preferences. Our approach extends neuro-evolutionar...Show More

Abstract:

In this paper we propose a methodology for improving the accuracy of models that predict self-reported player pairwise preferences. Our approach extends neuro-evolutionary preference learning by embedding a player modeling module for the prediction of player preferences. Player types are identified using self-organization and feed the preference learner. Our experiments on a dataset derived from a game survey of subjects playing a 3D prey/predator game demonstrate that the player model-driven preference learning approach proposed improves the performance of preference learning significantly and shows promise for the construction of more accurate cognitive and affective models.
Date of Conference: 18-21 August 2010
Date Added to IEEE Xplore: 30 September 2010
ISBN Information:

ISSN Information:

Conference Location: Copenhagen, Denmark

References

References is not available for this document.