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Re: [computer-go] An [open] question on game tree search theory



Hi Antti and others,

I think that's a very interesting problem.

If E is just a random number attached to every node in the tree, search is meaningless---any min-max value over the tree is just a min-max value of random numbers, and hence worthless.
Actually, min-max value of random numbers would not be totally worthless. It would favour lines of play where black has more options and white fewer and thus improve over making random moves (in many games).

For example, in chess E reflects usually among other things material balance. There is probably even a (conjencture level) fact that positions with material balance favoring white are statistically more probably game-theoretic wins for white than those positions where material balance favors black. This would be a mathematical link between E and G. Higher values of E would yield game-theoretic win for white (in case of chess) with higher probability in a strict statistical sense. Does min-maxifying keep and strengthen this effect? Why? Someone probably knows this. And, is there a way to measure the effect or verify it algorithmically?
Measuring the effect should be straightforward if you know the G values. Perhaps the most interesting case where G is available, is a chess endgame database. I don't know whether anyone has studied that. Machine learning with a chess endgame database is probably quite common, for instance a student project is proposed at http://www.informatik.uni-freiburg.de/~ml/jobs.html

I am interested in playout analysis, that is, playing the game stochastically to the end from the current position, and analysing the statistics. This gives another type of a heuristic which could be compared to E.
http://www.cis.hut.fi/praiko/go81/go_step.pdf

Regards,

--
Tapani Raiko, <tapani.raiko@xxxxxxxxxxxxxxxxx>, +358 50 5225750,
http://www.hut.fi/u/praiko/
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