Author: Robert Hyatt
Date: 06:43:44 07/25/00
Go up one level in this thread
On July 25, 2000 at 06:48:09, Graham Laight wrote: <snip> > >It seems to me that you are selecting which knowledge to use on the basis of >"inductive reasoning", or a tree of questions. > >e.g. > >Is there plenty of material left? > | > | > |- Yes > | | > | |- Are the opponent's pieces near my king? > | | > | |- Yes > | | | > | | |- Are the pawns around my king well placed? > | | | > | | |- No > | | | | > | | | |- A king safety problem exists > | | | > | | | > |---|---| > | > | >A king safety problem does not exist You imply this is a boolean decision. It isn't. The king safety scores can range over a set of integer scores from roughly -5.00 (-500 in Crafty) to +5.00, rather than being a decision tree 0/1 as you have it above. That seems to help my evaluations quite a bit, because over time, the endgame prediction score can catch up and surpass the king safety score causing crafty to want to trade even when it is attacking, because it has seen (or thinks it has seen) a way to reach an endgame where it has an advantage that is better than the attacking chances. That is important in my evaluation... there are no "on/off" things at all. IE some turn king safety off when material drops below X. Right around X, things are flakey as on one side you get a big negative score, while on the other side you get a 0, so a single capture can whack the score significantly. I don't do that... it smoothly transitions from big to small over many captures... And endgame scores smoothly transition from small to big as material comes off. What it means is that I evaluate everything, then figure out how much of each "component" ought to be included in the current score, rather than turning some off completely. IE if your king is in trouble, you might turn pawn structure analysis off as you are trying to save your king. I am trying to save both my king _and_ pawn structure, but I try harder to save the king of course. :) > >Now, inductive reasoning is usually faster that the sorting/selection process, >but, especially when there are a lot of records to look at, it becomes less >accurate. Generally about 20% less accurate. > >A _simple_ example of how it can go wrong would be as follows: > >Suppose a car dealer has 2 cars of interest on his website - a Jaguar and a BMW >- and there are only 2 selection criteria - colour and sex-appeal. And finally, >suppose you (the buyer) want to buy a sexy car. > >If the website uses inductive reasoning, if the first question is asking you >what colour you want, you may eliminate the Jaguar before you even get to the >sex appeal question. > >If, on the other hand, selection was done by indices, you would be asked >(hopefully) to specify your requirements for colour and sex-appeal, and also for >weightings (how important these indices are to you). > >In this case, you'd specify that colour was of low importance, high sex appeal >was very important, and even though you'd asked for the wrong colour, the Jaguar >would still appear at the top of your list. > >The other benefit is that the BMW would still appear on your list - in 2nd place >with a lower match score - so you could still consider it. > >Anyway, hopefully this demonstrates that "Nearest Neighbour" reasoning is >generally better than "inductive" reasoning (except in terms of speed). >
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