Closed xurxodiz closed 12 years ago
They are not odds for the dice now, but probabilities for the transition between states of a FSA representing a Markov process (see #16). Their function and our need for them remains the same though.
Quoting from #7:
The probabilities could be generated by either a) a neural network b) a classifier. In the second case, each category would have the probabilities "hardcoded".
The problem with the first case is that we need data for training. In the second one it's not really necessary, although it could be useful for refining the categories by clustering upon the data and checking results.
A definite direction has been chosen. We'll try to gather data from playing users (see #21 and #22), cluster that (see #23 and #24), and assign hardcoded transitions for an automaton to produce a Markov chain (see #16 and #17).
The algorithm makes "passes" over the level, filling it with different chunks. The chunks are chosen by rolling a dice and checking the odds for each element. How do we figure out those odds?
Candidates so far include neural networks, clustering, genetic algorithms and basically everything.