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Competing with humans at fantasy football: Team formation in large partially-observable domains

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Competing with humans at fantasy football: Team formation in large partially-observable domains (EN)
Χαλκιαδακης Γεωργιος (EL)
Chalkiadakis Georgios (EN)
Tim Matthews (EN)
Sarvapali D. Ramchurn (EN)
full paper
conferenceItem
2012
We present the first real-world benchmark for sequentially- optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforce- ment learning one, where the action space is exponential in the number of players and where the decision maker’s be- liefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to es- tablish the baseline performance in this domain, even without complete information on footballers’ performances (accessi- ble to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players. (EN)
BASICS OF Q-LEARNING (EN)
BACKGROUND ON FANTASY FOOTBALL (EN)
EVALUATION (EN)
MODELLING FPL (EN)
English
Association for the Advancement of Artificial Intelligence (EN)
Πολυτεχνείο Κρήτης (EL)
Technical University of Crete (EN)
Technical University of Crete
Institutional Repository Technical University of Crete
School of Production Engineering and Management - Journal Publications



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