Saying in an unqualified way that AlphaGo is brute force is wrong in the spirit of the question. Assuming AlphaGo uses a typical variant of MCTS, it is technically correct. The reason it's technically correct uninteresting is because the bias introduced by a policy net is so extreme that it might as well be a selective search.
Or put another way, imagine one were to set a threshold on the policy net output past a certain point in the tree such that moves below the threshold would be hard-pruned, and that threshold were set to a level that would prune, say, 70% of the legal moves in an average position. In technical sense, the search would no longer be full-width, and therefore it would suddenly become "not brute force" according to the definition earlier in the thread. But this distinction is not very useful, because moves in the tree that fall below such a threshold would receive zero simulations under any reasonable time controls anyways, so there would be no practical observable difference in the program's search or its play. So - spirit of the question - no AlphaGo is not brute force, its search is selective to an extreme due to the policy net, the vast majority of possibilities will never be in practice given any attention or time whatsoever. Technical answer - yes, AlphaGo is brute force, in that in the limit of having enormously vastly many more orders of magnitude of search time than we would ever devote to it and unbounded memory, it will theoretically eventually search everything (maybe, it would still depend on the actual details of its implementation). On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard via Computer-go < computer-go@computer-go.org> wrote: > I understand why most people are saying that AlphaGo is not brute force, > because it appears to be highly selective. But MCTS is a full width search. > Read the AlphaGo papers, as one of the other respondents (rather > sarcastically) suggested: AlphaGo will eventually search every move at > every node. > > > > MCTS has the appearance of a selective search because time control > terminates search while the tree is still ragged. In fact, it will search > every continuation an infinite number of times. > > > > In order to have high performance, an MCTS implementation needs to search > best moves as early as possible in each node. It is in this respect that > AlphaGo truly excels. (AlphaGo also excels at whole board evaluation, but > that is a separate topic.) > > > > > > *From:* Steven Clark [mailto:steven.p.cl...@gmail.com] > *Sent:* Sunday, August 6, 2017 1:14 PM > *To:* Brian Sheppard <sheppar...@aol.com>; computer-go < > computer-go@computer-go.org> > *Subject:* Re: [Computer-go] Alphago and solving Go > > > > Why do you say AlphaGo is brute-force? Brute force is defined as: "In > computer science, brute-force search or exhaustive search, also known as > generate and test, is a very general problem-solving technique that > consists of *systematically enumerating all possible candidates* for the > solution and checking whether each candidate satisfies the problem's > statement." > > > > The whole point of the policy network is to avoid brute-force search, by > reducing the branching factor... > > > > On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go < > computer-go@computer-go.org> wrote: > > Yes, AlphaGo is brute force. > > No it is impossible to solve Go. > > Perfect play looks a lot like AlphaGo in that you would not be able to > tell the difference. But I think that AlphaGo still has 0% win rate against > perfect play. > > > > My own best guess is that top humans make about 12 errors per game. This > is estimated based on the win rate of top pros in head-to-head games. The > calculation starts by assuming that Go is a win at 6.5 komi for either > Black (more likely) or White, so a perfect player would win 100% for Black. > Actual championship caliber players win 51% to 52% for Black. In 9-dan play > overall, I think the rate is 53% to 54% for Black. Then you can estimate > how many errors each player has to make to bring about such a result. E.g., > If players made only one error on average, then Black would win the vast > majority of games, so they must make more errors. I came up with 12 errors > per game, but you can reasonably get other numbers based on your model. > > > > Best, > > Brian > > > > *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On > Behalf Of *Cai Gengyang > *Sent:* Sunday, August 6, 2017 9:49 AM > *To:* computer-go@computer-go.org > *Subject:* [Computer-go] Alphago and solving Go > > > > Is Alphago brute force search? > > Is it possible to solve Go for 19x19 ? > > And what does perfect play in Go look like? > > How far are current top pros from perfect play? > > > > Gengyang > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go > > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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