The goal of a decision tree is to investigate the possible game outcomes
given the current state of the game. In order to do this, a tree of game states
is built where the depth of the tree represents how many moves into the future
is currently being investigated. In order to evaluate the strength of one
move branch over another, we return to the general strategy of gaining irreversible
board locations.
Somewhere in the decision tree's branches, there exists the best possible
outcome as well as the worse outcome and everything in between given any particular
move. Since we are interested in gaining positions that we can't lose, it's
the worse possible outcome as the result of each of immediately possible
moves that will receive our attention. More to the point, if we choose the
move that has the best worse possible outcome each time, i.e. creating irreversible
pieces, then we are constantly making moves that improve our strategic strength.
In order to evaluate the value of each possible outcome [node] in the tree,
one should use a board location value matrix. The behavior of the decision
tree is further increased if a relative strength (player 1 vs. player 2)
is used as an indicator instead of the board value for the active player alone.
Hence, a location value ratio is a great way to evaluate a tree node's weight:
Location Value Ratio: The sum of the active player's board values
divided by the sum of the opponent's board values, where the each players
values are established using their relative board location value matrix.
Parsing
It is necessary to parse these decision trees using some hueristic(s) so
that they can be used real time.
More Fun Reading
If you want to read more fun stuff I wrote, check out my first Sci-Fi novel
The Synthetic Age.