@Bibtex-file{Ai/lig.bib,
  title =        "Bibliography on Machine Learning in Strategic Game
                 Playing",
  author =       "Johannes F{\"u}rnkranz",
  email =        "juffi@ai.univie.ac.at",
  address =      "{\"O}sterreichisches Forschungsinstitut f{\"u}r
                 Artificial Intelligence ({\"O}FAI)\\ Schottengasse 3\\
                 A-1010 Vienna\\ Austria",
  supported =    "yes",
  abstract =     "This bibliography contains a variety of references
                 concerning Machine Learning in Strategic Game Playing,
                 i.e. on ideas how game playing programs can improve
                 their play by learning from their own or others'
                 experience. Included in the list are only references in
                 which the application of a Machine Learning algorithm
                 to a game playing problem forms a considerable part of
                 the paper. Papers in Machine Learning that might be
                 relevant for the problem, but not explicitly address
                 game playing (like Sutton's paper on Temporal
                 Difference Learning) have not been included. Likewise,
                 papers that describe a game playing application that
                 might be relevant for Machine Learning methods (like
                 Wilkins's paper on PARADISE) have been omitted.",
  keywords =     "machine learning, strategic game playing, Chess,
                 Checkers, Othello, Go, Go-Moku, Backgammon, Abalone,
                 Connect-Four",
  readme =       "I am interested in all references to on-line or
                 off-line ressources on this topic. If you happen to
                 know anything that I seem to be unaware of please let
                 me know!.",
}