ACBot uses my approach, which was machine learned from millions of game results rather than from quackle's superleave. They should probably be "relearned" in light of QI.
That approach suffers from no concept of tile synergy other than the duplication penalty, so no set of parameters could ever achieve a good statistical fit to quackle's superleave. For example, racks with Q and a U will get undervalued or racks with Q and no U (and no I) will get overvalued (or more likely both in order to achieve the closest statistical fit). The most practical approximation to tile synergy for humans is intuition (possibly with a little tuning to avoid overvaluing ING, for example). Given that human intuition has to be applied on top of whatever tile values one would memorize, anything like the numbers John suggested or any of the other numbers that have been published are likely good enough to get in the ballpark. Steven Gordon On 5/11/07, Amit Chakrabarti <[EMAIL PROTECTED]> wrote:
* sapphirebrand2000 ([EMAIL PROTECTED] <sheppardco%40aol.com>) [070511 12:38]: > I must refrain from a definite opinion, since I cannot devote the > time necessary to studying this issue. But: perhaps you are better > off using Basic, or Maven's values or something, along with a V/C > balance adjustment. One might be able to improve on this somewhat. The goal is to come up with a simple model for approximating rack leave values that involves a small amount of memorization (preprocessing phase) plus a small amount of in-the-head arithmetic (query phase). I don't want to propose any particular model, but it seems that the way to go about it would be to learn the parameters of the model from quackle's data, basically treating quackle's "superleave" values as the "correct" ones. Sounds like a machine learning problem to me. Perhaps someone with solid machine learning background (not me) can suggest a suitable off-the-shelf algorithm to create a concise model. For starters, it might be worthing learning the "correct" parameters for an ACBot-style model. ACBot has values for each possible number of occurrences of each tile, plus a matrix of V/C mix values. -AC
