Changes

a [https://en.wikipedia.org/wiki/Heuristic_(computer_science) heuristic function] to determine the [[Score|relative value]] of a [[Chess Position|position]], i.e. the chances of winning. If we could see to the end of the game in every line, the evaluation would only have values of -1 (loss), 0 (draw), and 1 (win), and the chess engine should search to depth 1 only to get the best move. In practice, however, we do not know the exact value of a position, so we must make an approximation with the main purpose is to compare positions, and the chess engine now must search deeply and find the highest score position within a given period.

Beginning chess players learn to do this starting with the [[Point Value|value]] of the [[Pieces|pieces]] themselves. Computer evaluation functions also use the value of the [[Material|material balance]] as the most significant aspect and then add other considerations.

: [https://groups.google.com/g/lczero/c/TLCMkkdm1hw/m/SgbGghzhBAAJ Re: Idea: use range (evalMin - evalMax) for position evaluation] by [[Álvaro Begué]], [[Computer Chess Forums|LCZero Forum]], May 28, 2021 <ref>[[Eric B. Baum]], [[Warren D. Smith]] ('''1999'''). ''[https://www.mitpressjournals.org/doi/abs/10.1162/089976699300016881?journalCode=neco Propagating Distributions Up Directed Acyclic Graphs]''. [https://en.wikipedia.org/wiki/Neural_Computation_%28journal%29 Neural Computation], Vol. 11, No. 1</ref>

: [https://groups.google.com/g/lczero/c/TLCMkkdm1hw/m/F9JjnN8FBQAJ Re: Idea: use range (evalMin - evalMax) for position evaluation] by [[Warren D. Smith]], [[Computer Chess Forums|LCZero Forum]], May 29, 2021

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