Author: Graham Laight
Date: 02:57:02 10/18/00
Go up one level in this thread
On October 17, 2000 at 20:57:50, Andrew Dados wrote: >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- If the K+P v K endgame was seeded with some basic knowledge and categories, (after all - humans didn't come from nowhere - they evolved from chimpanzees, which are very similar to humans), then it is certainly possible that after 10,000 generations, the system would play the game well. -g
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