If you honestly feel they are that valuable, you should be willing to make the resources available to teach people about them without needing encouragement. Most people won't see the need or interest until after a resource is available which clearly, concisely, and comprehensively illustrates the value of that technique.
Frankly, you shouldn't need our approval or rabid anticipation to do something that you think is worthwhile.
I am short on time or I would volunteer. Someday maybe. But he is right Decision Trees are something that are not payed enough attention to. Random Forest in my use case outperformed neural nets and SVM and are speedy to train.
For games, more than neural or bayes nets or genetic search, decision trees are the one thing out of machine learning I would argue to be most applicable to games in a splash and dash manner. They represent a probability distribution over the data, are not very far from FSM many are used to and with a weighted randomized voting method are close to behaviour trees (although built in an inverse manner - decision trees you start from a list of scenarios and desired ouptuts and it returns a tree, btrees - you start with actions to input states and build the tree - at least for what I can understand of behaviour trees, the game literature terminology is not one I am fluent in)
Here is a fairly clear but basic python example of a decision Tree from Machine Learning An Algorithmic Perspective (I highly recommend the book). http://www-ist.masse...Code/6/dtree.py