@inproceedings{
 title = {Predicting performance in team games: The automatic coach},
 type = {inproceedings},
 year = {2011},
 keywords = {Clustering,Videogames},
 pages = {401-406},
 volume = {1},
 id = {c5695fd9-59da-36ce-b7bb-83c24b717540},
 created = {2016-03-23T11:20:11.000Z},
 file_attached = {false},
 profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},
 last_modified = {2017-03-28T19:10:31.050Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {A wide range of modern videogames involves a number of players collaborating to obtain a common goal. The way the players are teamed up is usually based on a measure of performance that makes players with a similar level of performance play together. We propose a novel technique based on clustering over observed behaviour in the game that seeks to exploit the particular way of playing of every player to find other players with a gameplay such that in combination will constitute a good team, in a similar way to a human coach. This paper describes the preliminary results using these techniques for the characterization of player and team behaviours. Experiments are performed in the domain of Soccerbots.},
 bibtype = {inproceedings},
 author = {Jiménez-Díaz, G. and Menéndez, H.D. and Camacho, D. and González-Calero, P.A.},
 booktitle = {ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence}
}

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