> > So I believe a better approach is a heavy playout approach with NO > tree. Instead, rules would evolve based on knowledge learned from each > playout - rules that would eventually move uniformly random moves into > highly directed ones. All-moves-as-first teaches us that in the > general case a move that is good now is good later or visa versa. But > it needs to go way farther than that. It needs to "act like a tree" > when something specific needs to be handled and generalize when this is > most appropriate. If something like this could be made to work, a > tree could probably be built on top of it if desired. This would be a > super-playout approach. > This looks very much like the way human players work (albeit with a tree): read local sequences and outcomes that can be kept in reserve for a long time, but called about any time depending on the situation. Big chunks.
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