@Article{Riv87c,
  author = 	 { Ronald L. Rivest },
  title = 	 { Game tree searching by min/max approximation },
  journal = 	 { Artificial Intelligence},
  issn =         { 0004-3702 },
  OPTyear = 	 { 1987 },
  OPTmonth = 	 { December },
  date =         { 1987-12 },                  
  volume = 	 { 34 },
  number = 	 { 1 },
  pages = 	 { 77--96 },
  doi =          { 10.1016/0004-3702(87)90004-X },
  url =          { http://www.sciencedirect.com/science/article/pii/000437028790004X },
  abstract =     {
        We present an iterative method for searching min/max game trees based on the
        idea of approximating the ``min'' and ``max'' operators by generalized
        mean-valued operators.  This approximation is used to guide the selection of
        the next leaf node to expand, since the approximations allow one to select
        efficiently that leaf node upon whose value the (approximate) value at the
        root most highly depends.  Experimental results from almost 1,000 games of
        Connect-Four suggest that our scheme is superior to minimax search with 
        alpha-beta pruning, for the same number of calls to the move routine.
        However, our scheme has higher overhead, so that further work is needed before
        it becomes competitive when CPU time per turn is the limiting resource.                  
                 },
}