On Thu, 2008-10-23 at 18:46 +0100, Claus Reinke wrote: > > Thanks again for more explanations. I think the AMAF is clear to me now. > > For what it is worth: I read the AMAF section as indicating that the bots > are to play using AMAF heuristics - random playouts, followed by playing > the AMAF-scored winning move, rinse and repeat. Which is why I thought > I shouldn't try until I get round to making a performant, lightweight playout. > Is that right, or are these bots supposed to play random playouts only, but > provide scoring informaton as if those playouts were part of an AMAF bot?
I'm not sure I understand the question. You are implementing a bot which can play real games on CGOS for instance. It will generate and play a move from the gtp genmove command based on AMAF scoring of N random playouts. Of course if you want to compare statistics, you don't have to have a full game playing bot, you can just see if random playouts match what we are getting. But when I build a more sophisticated conformance tester it will require a full game playing bot. When I ask for scoring information using ref-score I simply want to know the fraction of times that the color who is to move won those playouts. That will depend of course on how komi is set. Did that answer your question? - Don > > In either case, a working example explanation would make a useful addition. > > Btw, if there was a version of the bot specification on a wiki page, people > might be able to clarify text or add questions. The wiki version could point > to the current reference version (part of the reference implementation, I > believe? I didn't want to look at Don's code until I get round to ripping out > all the extra info and experimental code I keep playing with, which keeps > my code from matching that 20k playouts/sec figure). > > > This is still something I don't understand. Are there others who > > implemented the same thing and > > got 111 moves per game on average? I tried to look through some posts on > > this list but didn't see > > any other numbers published. > > I'm not yet implementing the reference spec, but if you're just asking > about random playouts: yes, I tend to get around 111 moves. The > most frequent game length is even shorter (81 positions+ ~39 positions > freed by capture - ~13 positions occupied by eyes => ~107 moves), > but there are more longer games than shorter ones (see histogram below). > > Claus > > $ ./Go.exe 0.5 10000 > Size: 9 > Komi: 0.5 > Games: 10000 (1.3056 average black-white score difference) > Black: 5141 games (26.9716=6.6633+20.3083 average score) > White: 4859 games (25.1660=6.1724+18.9936+0.5 average score) > Draws: 0 > > Gobble-style eye: 442830 > capture (w/o ko): 986684 > ko candidate: 87978 > positional superko: 34 > suicide: 595044 > > game lengths (average: 111.1783): > [(80,3),(81,9),(82,9),(83,19),(84,16),(85,47),(86,44),(87,57),(88, > 79),(89,82),(90,124),(91,156),(92,158),(93,159),(94,223),(95,234),(96,236),(97,312),(98,278),(99,310 > ),(100,282),(101,301),(102,305),(103,340),(104,282),(105,308),(106,311),(107,255),(108,300),(109,291 > ),(110,252),(111,253),(112,246),(113,241),(114,195),(115,199),(116,200),(117,167),(118,134),(119,144 > ),(120,130),(121,101),(122,88),(123,76),(124,79),(125,67),(126,74),(127,61),(128,58),(129,66),(130,5 > 5),(131,92),(132,44),(133,87),(134,70),(135,101),(136,79),(137,110),(138,93),(139,110),(140,73),(141 > ,81),(142,65),(143,98),(144,61),(145,60),(146,58),(147,60),(148,30),(149,30),(150,29),(151,22),(152, > 23),(153,16),(154,18),(155,14),(156,14),(157,13),(158,11),(159,3),(160,6),(161,3),(162,1),(163,6),(1 > 66,2),(184,1)] > > > > _______________________________________________ > computer-go mailing list > computer-go@computer-go.org > http://www.computer-go.org/mailman/listinfo/computer-go/
signature.asc
Description: This is a digitally signed message part
_______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
