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Preference Learning from Annotated Game …
Preference Learning from Annotated GameDatabasesChristian Wirth and Johannes F urnkranzKnowledge Engineering, Technische Universit at Darmstadt, chess, as well as many other domains, expert feedback isamply available in the form of Annotated games . This feedback usuallycomes in the form of qualitative information because human annotatorsfind it hard to determine precise utility values for game states. There-fore, it is more reasonable to use those annotations for a Preference basedlearning setup, where it is not required to determine values for the quali-tative symbols. We show how game annotations can be used for learninga utility function by translating them to evaluate the resulting function by creating multiple heuristics basedupon different sized subsets of the training data and compare them ina tournament scenario.
Preference Learning from Annotated Game Databases Christian Wirth and Johannes Furnkranz Knowledge Engineering, Technische Universit at Darmstadt, Germany
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