Hi,
Interesting paper, in case you did not notice:
http://www.machinelearning.org/proceedings/icml2007/papers/394.pdf
Title: Learning to solve game trees
Authors: David Stern, Ralf Herbrich, Thore Graepel
Abstract: We apply probability theory to the task of proving whether a
goal can be achieved by a player in an adversarial game. Such problems
are solved by searching the game tree. We view this tree as a graphical
model which yields a distribution over the (Boolean) outcome of the
search before it terminates. Experiments show that a best-first search
algorithm guided by this distribution explores a similar number of nodes
as Proof-Number Search to solve Go problems. Knowledge is incorporated
into search by using domain-specific models to provide prior
distributions over the values of leaf nodes of the game tree. These are
surrogate for the unexplored parts of the tree. The parameters of these
models can be learned from previous search trees. Experiments on Go show
that the speed of problem solving can be increased by orders of
magnitude by this technique but care must be taken to avoid over-fitting.
Rémi
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