Restart working on my bot "Melody" after a long time

#### Comments, questions, feedback:

You pushed me (GrubbmGrb) out of the top-50!! When I can find the time to work on the movement of my new bot, I will beat you again ;-) Quite a nice entry for basic GF-targeting and basic WaveSurfing -- GrubbmGait

- 2006-07-12: 1.3.1 released. Implemented basic GuessFactorTargeting and WaveSurfing.

- Part of the movement code is borrowed from Voidious's WaveSurfing/Tutorial

- 2006-07-14: some minor fixes to the targeting algorithm, but it is still very simple. My plan is to implement a Bayesian GF targeting, something like Segmentation but in a different way. Since the GuessFactor targeting can be viewed as a classfication problem of N bins. So let S be the random variable of the bin index, and O be the observed features (e.g., velocity, distance, accelaration, etc). What we want to do is to pick bin i such that:

- i = argmax_i P( S = i | O )

- which, according to Bayesian rule, is equivalent to

- i = argmax_i P( O | S = i ) P( S = i )

- Note that P ( S = i ) can be estimated easily, and is alway done by any basic GF targeting algorithms. The first factor may be calculated in many ways. If we do VQ in the feature space, O will become a discrete random variable, and the statistics can be collected via counting. In that case, it would be equivalent to many Segmentation algorithms. It is also possible, though I'm not sure whether it will be better, that P ( O | S = i ) is modeled using continuous distributions. In the simpliest case, this conditional probability can be just ignored, which gives a basic GF gun. This flexiblity might be useful when data is not sufficient, or when we're not confident enough about the first factor.