Author: Andrew Dados
Date: 17:57:50 10/17/00
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On October 17, 2000 at 11:06:22, Graham Laight wrote: >The purpose of this is to build a program that can teach itself to do a good job >of evaluating chess positions, using only technology that is available today, >and can be applied on a PC which can be bought off the shelf today. > >Steps to building a self learning chess machine - 1st draft: > >* assemble a collection of evaluation components. There should be sufficient >eval components to be able to theoretically evaluate any position, if combined >correctly > >* set up a genetic algortithm to be able to combine these components into a >single evaluation function, and to be able to vary them from game to game > >* write a program that can "categorise" chess positions, and come up with a >measure of "similarity" between them > >* assemble a collection of categorisation components > >* set up a genetic algorithm to to be able to combine these components into a >single categorisation function, and to be able to vary them from game to game > >* new categories and evaluation functions can be made by combining components >from existing evaluation functions (chosen for their "similarity"), when the >"similarity" between the new position and existing categories is sufficiently >small > >* seed the system with some categories > >* seed the system with a categorisation function that works > >* seed the system with working eval functions suited to the categories > >* ensure the system is clever enough to get to check-mate from the 1st game of >the experiment > >* start the system playing against another copy of itself > >* During the game, every legal move will be evaluated (1 ply) and the best one >chosen > >* when the system loses a game, it must evolve. From the move list, the >evaluation function used prior to the eval score falling will be subjected to >the genetic algorithm, as will the categorisation > >There is a problem in computer chess that the problem may have occured before >the evaluation started to fall. In this system, the problem will be solved >because, with sufficient play, the poor evaluation will eventually make its way >back to the source of the problem (though other eval functions will temporarily >be messed up on the way!). > >It took roughly 400,000 generations to change chimpanzees into humans (based on >average generation of 15 years - a number I admit I've plucked out of the air, >but which is at least the right order of magnitude). > >Could 400,000 generations of the above system produce a great chess player? > >Comments please! > >-g Try to imagine *only* K+P vs K endgame positions. Then try to play 10000 games in order to teach your genetic algorithm to play KPK correctly. Do you *really* believe it will learn to evaluate all KPK positions as good as average 2200 player? Now try to imagine time to teach genetic alghoritm to evaluate middlegame.... -Andrew-
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