I've been experimenting a bit in the area of humanizing :) the endgame.
The only method I've found acceptable is to do something like Don wrote. I use the average score in the tree to set at new goal for the search. First you start out with the standard goal of winning by at least 0.5 pts. But after a number of simulations you can start using the average score instead. Meaning that if the search converge to a score of say +5.5 points it will see a result < 5.5 as inferior ( well, a loss ). Using the above will change the behaviour quite nicely. However, if this is applied to all situations you'll end up with a bot not trying to win ( thus reducing strength a lot ). You must only use a floating goal when it's ahead. I recently ran 502 games against the same bot but without this feature. Having the "floating" goal makes it win about 47%, so a slight decrease in strength.. but I'm sure a bit of tweaking may actually make it stronger. The best part is that it now wins by 51pts and loses by 17pts on average. /Christian On 1/9/07, Sylvain Gelly <[EMAIL PROTECTED]> wrote:
> and if it doesn't, then there's a simple formula > for getting a lot more resignations out of your opponents It is not so simple to implement, because you have to keep two statistics (the wins and the territories), and switch between then (and switch back if the winning probability drops). So you have to change somehow the structure. At least in MoGo it would takes time to do. What we did try is to give a bonus for moves which give a bigger territory. The reward is for example for a win 1+1/1000*territory. This gave unfortunately a lower winning percentage for MoGo. Sylvain _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
_______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
