Le 25/11/2009 à 15:11, Vlad Dumitrescu a écrit : > What I am considering is a way to analyze a list of moves, each having > in turn a value that is a list of expected outcomes and their > respective estimated probabilities, and to sort the moves by the > expected outcome in the context of a given risk strategy. In practice, > this means that the strategy is an algorithm that takes an > outcome/probability list and converts it to a number, so that it can > be compared to the other values. > > The algorithm in the example above is a linear weighted sum. Normal MC > programs look only at the number of positive and negative outcomes. > These are only two possibilities. Yes its the mathematical expectation of the "aim" of the game the bot is playing: score or win respectively correspond to the Hahn game or Go game.
> If using a more generic approach, > the strategy can be parametrized and optimized (both offline and > online), hopefully resulting in a better gameplay. I don't understand how anything could be better than the expectation, exept if you have additional information. For example mogo1 does not know bulky-five-dead-shape and has lots of problem to find it is dead (before being killed). So when playing against mogo1 you want to bias the estimator in the branches were bulky five appears ? Or did i totally misunderstood you ? Best regards. Alain _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
