"We also investigated the possibility of using probability thresholds instead of depth

to shape search trees. Depth-based searches form the backbone of virtually all chess

engines in existence today, and is an algorithm that has become well-established over

the past half century. Preliminary comparisons between a basic implementation of

probability-based search and a basic implementation of depth-based search showed that

our new probability-based approach performs moderately better than the established

approach. There are also evidences suggesting that many successful ad-hoc add-ons to

depth-based searches are generalized by switching to a probability-based search. We

believe the probability-based search to be a more fundamentally correct way to perform

minimax.

**Finally, we designed another machine learning system to shape search trees within**

the probability-based search framework.Given any position, this system estimates the

the probability-based search framework.

probability of each of the moves being the best move without looking ahead. The system

is highly effective - the actual best move is within the top 3 ranked moves 70% of the

time, out of an average of approximately 35 legal moves from each position. This also

resulted in a signficant increase in playing strength.

With the move evaluator guiding a probability-based search using the learned evaluator,

Giraffe plays at approximately the level of an FIDE International Master (top

2.2% of tournament chess players with an offcial rating)12."

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