Often there are two or more moves that are equally good. You need to handle tat case as well.
David > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of > steve uurtamo > Sent: Sunday, March 11, 2007 1:44 PM > To: computer-go > Subject: Re: [computer-go] when to stop searching > > > no problem. :) > > just run the following algorithm: > > i) get MC "estimates" for each move. > ii) partition the list into "good looking moves" > and "bad looking moves" based upon the > estimated probability of earning a win. > iii) compare the top move pairwise with the rest > of the "good looking moves" to see if it > satisfies a student t-test at some reasonable > value. if it does, play it. > iv) drop anything from the bottom move list that > satisfies a t-test as being worse than all of > the moves from the "top move" list. > > repeat i)-iv) until you play a move. > > s. > > > --- Brian Slesinsky <[EMAIL PROTECTED]> wrote: > > > On 3/11/07, [EMAIL PROTECTED] <[EMAIL PROTECTED]> > > wrote: > > > > > > The problem in Computer Go is the search of the > > best move that can be found. > > > > > > First, Since a computer prgram, or a player cannot > > consider every possible > > > moves, they usually has a move selecting function > > which select a sub set of > > > the all possible moves. The moves for > > consideration selected by a computer > > > program, by a player, or by a pro player may not > > contain the true best move. > > > > A computer can consider every possible next move at > > the first ply, so > > that would include the best move. I assume you mean > > that only a > > subset of the full move tree can be visited, and a > > sequence of moves > > that justifies the best move may not be visited, so > > it may not be > > recognized. > > > > But to pick the best move, it's "only" necessary to > > recognize the > > weaknesses in all the other moves. In many cases > > these weaknesses can > > be recognized using move sequences that are far less > > than perfect > > play. The tricky part seems to be sequences can > > only be evaluated > > with perfect play for many moves such as ladders. > > It's unclear how > > often such perfection is required to pick the best > > move. > > > > > Thus, one starts with an imperfect subset of moves > > and an imperfect > > > evaluation function and feed them to a search > > algorithm (alpha-beta, for > > > example). In general, the higher are the merit > > probabilities, the more > > > effective is the search. > > > > With UTC, if I understand correctly, it would > > eventually try every > > possible sequence, but of course not within the time > > limit, so it > > isn't clear that it starts with an "imperfect subset > > of moves" that is > > separate from the other factors. > > > > > Postulation: For a given function merit > > probability and a given evaluation > > > merit probability the move merit probability > > function approaches a constant > > > value for large enough search scope. > > > > > > That is beyond certain value of the search scope, > > the move merit > > > probabability function won't improve anymore. > > > > I think what you're saying is that it should be > > possible to derive a > > formula for when a search should stop so that the > > program can save > > time for future moves? > > > > - Brian > > _______________________________________________ > > computer-go mailing list > > [email protected] > > > http://www.computer-go.org/mailman/listinfo/computer-go/ > > > > > > > ______________________________________________________________ > ______________________ > Don't pick lemons. > See all the new 2007 cars at Yahoo! Autos. > http://autos.yahoo.com/new_cars.html > _______________________________________________ > computer-go mailing list > [email protected] > http://www.computer-go.org/mailman/listinfo/computer-go/ > _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
