ron musick zqsmirikunicom call papers machine learning journal special issue machine learning games httpwwwaicmlcsualbertaca_mljcfphtm scope authors invited submit full papers presenting original results aspect machine learning games ideal contribution special issue would strongly motivated applications commercial classical games focused research issues relevant topics described papers specific game theory however submitted forthcoming special issue background games whether created entertainment simulation education provide great opportunities machine learning variety possible virtual worlds subsequent mlrelevant problems posed agents worlds limited imagination furthermore games industry large growing surpassed movie industry revenue years back faced tremendous demand novelty struggles provide backdrop machine learning driven successes would draw highprofile attention field surprisingly however commercial game date less impact learning made quite unlike great matches application datadriven analytics data mining olap broad familiar spectrum research relevant games applications ranging inference partially observable worlds representational issues faster robust methods speech recognition relatively new research thrusts well example online learning models constructed used fly data unavailable gameplay time rich source new interesting problems topics particular importance successful game applications include generation new practical theoretical tools help learning play game game worlds provide excellent test beds investigating potential learning improve agents capabilities enviroment constructed varying characteristics deterministic discrete classical board card games indeterministic continuous action computer games learning algorithms tasks studied quite thoroughly recent improvements interest special issue learning players opponent modeling partner modeling team modeling multiple team modeling fascinating interdependent largely unsolved challenges model selection stability online settings lead effectively unsupervised construction models supervised algorithms methods biasing proposed model space without significant loss predictive power critical learning efficiency interpretive ability enduser confidence optimizing adaptivity building opponents barely lose interesting ways important game world creating worldclass opponents requires building highly adaptive models substantively personalize adversaries partners wide range competence rapid shifts play style introducing different set update optimization criteria learners wealth new research targets created model interpretation whats next move query desired models game certainly one gets attention creating illusion intelligence requires painting picture agents thinking process ability describe current state model process inference model decision decision enables queries provide foundation host social actions game predictions contracts counterfactual assertions advice justification negotiation demagoguery much influence outcomes actual ingame actions performance resource requirements update inference always great importance ai get bulk cpu memory machines driving market always underpowered compared typical desktops point time submission reviewed according standards machine learning journal important dates titles short abstracts due january 14 2005 papers due february 11 2005 author notification april 1 2005 final versions accepted papers due june 3 2005 publication fall 2005 submission information electronic submissions accepted instructions submission found httpwwwkluweronlinecomissn08856125 text electronic submission please explicitly state paper special issue machine learning games addition submitting paper kwvwkapcom please also submit guest editors michael bowling awyhypbcsualbertaca johannes fuernkranz ctpoqirdrninformatiktudarmstadtde thore graepel tqrxfjmicrosoftcom ron musick zqsmirikunicom inquiries regarding special issue directed guest editors end forwarded message tue 05 aug 2008 175057 0800 uai mlj special issue