Author: KarinsDad
Date: 10:50:01 07/19/99
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On July 18, 1999 at 14:27:58, Dan Andersson wrote: [snip] >I use a alpha-betaized evaluation function (it orders the evaluation factors in >a binary search and cuts off when it is out of alpha or beta boundaries and >returns alpha or beta) and save the state of the evaluation function in the hash >table if the position was not fully evaluated, to continue evaluating later if >needed. This way I get the benefits of MTD and lazy evaluation. i.e alpa-beta >evaluation cut evaluation time by 75% and it allows bootsrapping evaluation >values. > Dan, I am not sure if I totally understood what you wrote here, but what I got out of it is that you do a partial evaluation and if you fall outside of the bounds, you stop evaluating, indicate in the node how far into the evaluation you got, place the node into the hash table, then return the alpha or the beta value. Later on, if you get back to that node and are within bounds, you can decide whether to continue the evaluation or not. This sounds good to me (regardless of whether this is what you meant). Thanks for the idea! KarinsDad :)
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