Random playout vs evaluation

[Robert Pope]

SJE's posts got me thinking.

How strong a correlation there would be between a monte carlo playout of a position vs. a traditional search? It seems that monte carlo would perform poorly when there is a narrow forced win. But perhaps the reverse is true, and random playout could identify positions where an engine's evaluation has a blind spot.

Has anyone done anything like that?

[Dann Corbit]

Monte Carlo search is used all the time in go.
I don't know if anyone has tried it in chess.
https://chessprogramming.wikispaces.com ... ree+Search

Here is a github monte carlo tree search:
https://github.com/lteacy/mcts-cpp

[AlvaroBegue]

I don't expect Monte-Carlo simulations would work very well in chess. Certainly not when giving every move the same probability ("light playouts", in MCTS lingo).

If you write something a bit smarter, so you don't drop pieces for no reason, perhaps you get some reasonable results. I would be curious to see a MCTS chess engine that uses depth-1 search during the playouts.

[Daniel Shawul]

I don't expect Monte-Carlo simulations would work very well in chess. Certainly not when giving every move the same probability ("light playouts", in MCTS lingo).

If you write something a bit smarter, so you don't drop pieces for no reason, perhaps you get some reasonable results. I would be curious to see a MCTS chess engine that uses depth-1 search during the playouts.

I tried this a while ago for chess and it sucked. MCTS works awesomely for checkers though because there captures are forced unlike in chess. And that seems to make a big difference more than anything else to the accuracy of the random playout. Nebiyu has MCTS mode for playing many types of games if anyone is interested.

[flok]

I tried it once, in a distributed version (+/- 120 computers).
Failed