This causes what I call the "horizon effect" which prevents the tree exploration to work properly - the moment the tree finds a sequence that unbiases the simulations, it is horrified by the bad results [*] and switches to a different branch, until it finds the same; thus, the bot pushes the resolution (unbiasing) of the situation over the tree horizon for as long as possible. This shows in actual games as the bot playing random throwins and obviously pass moves before resigning.
I don't know how much hidden ongoing research is being done about dealing with the bias in the tree, the only result so far I'm aware of is Remi Coulom's criticality heuristic, and it wasn't that effective; I personally have some clear ideas I want to start experimenting with as soon as possible - I think we need a top-down counterpart for the bottom-up propagation function of RAVE. IMHO a substantial progress in this direction is the next big thing to happen in computer go.
Petr "Pasky" Baudis
I like the criticality heuristic. It certainly points to an important problem. One trick might be to identify "hot spots" with something akin to criticality and then penalize jumping between them. Because permutating moves between separate important areas is exactly what leads to the consequence evading horizon effect.
Stefan _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
