AI/ML

A More Human Way to Play Computer Chess. (arXiv:1503.04333v5 [cs.AI] UPDATED)



This paper suggests a forward-pruning technique for computer chess that uses
‘Move Tables’, which are like Transposition Tables, but for moves not
positions. They use an efficient memory structure and has put the design into
the context of long and short-term memories. The long-term memory updates a
play path with weight reinforcement, while the short-term memory can be
immediately added or removed. With this, ‘long branches’ can play a short path,
before returning to a full search at the resulting leaf nodes. Re-using an
earlier search path allows the tree to be forward-pruned, which is known to be
dangerous, because it removes part of the search process. Additional checks are
therefore made and moves can even be re-added when the search result is
unsatisfactory. Automatic feature analysis is now central to the algorithm,
where key squares and related squares can be generated automatically and used
to guide the search process. Using this analysis, if a search result is
inferior, it can re-insert un-played moves that cover these key squares only.
On the tactical side, a type of move that the forward-pruning will fail on is
recognised and a pattern-based solution to that problem is suggested. This has
completed the theory of an earlier paper and resulted in a more human-like
approach to searching for a chess move. Tests demonstrate that the obvious
blunders associated with forward pruning are no longer present and that it can
compete at the top level with regard to playing strength.

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