Richard Vida wrote:Uly wrote:Thanks Richard! I would like to ask you if you have thought about implementing learning for Critter, in the style of Shredder Learning? Currently Critter is my 3rd main engine behind the Rybkas, but I think that it could become the #1 choice for analysis if it had learning.
To be honest, I do not know how exactly Shredder Learning works. Is it like R3 Persistent Hash? While the idea sounds fairly simple, it is very difficult to implement correctly. I mean correctly form the user's perspective - to be really useful. With R3 it was a major pain to use and it was eventually removed in R4.
I will think about it, but can not promise anything yet.
Thanks for the release/update!
As for learning (an incorrect term in this case), it would require genetic programming, better known as self-modifying code. You are correct that it is more a preserve/persistent hash type feature. Search data is dumped and appended to disk for a unique positional analysis to obtain deeper search. The goal in essence is not learning, but resumption of analyses.
The book-learning feature in Fritz is closer to genetic programming where the books serve as population, and move settings evolve continuously based on statistical probability of 1, 0, and 1/2 results.