Editing Fuzzy logic (section)

=== Ecorithms ===
Computational theorist [[Leslie Valiant]] uses the term ''ecorithms'' to describe how many less exact systems and techniques like fuzzy logic (and "less robust" logic) can be applied to [[learning algorithms]]. Valiant essentially redefines machine learning as evolutionary. In general use, ecorithms are algorithms that learn from their more complex environments (hence ''eco-'') to generalize, approximate and simplify solution logic. Like fuzzy logic, they are methods used to overcome continuous variables or systems too complex to completely enumerate or understand discretely or exactly.<!-- See in particular p.&nbsp;58 of the reference comparing induction/invariance, robust, mathematical and other logical limits in computing, where techniques including fuzzy logic and natural data selection (à la "computational Darwinism") can be used to short-cut computational complexity and limits in a "practical" way (such as the brake temperature example in this article). --><ref>{{cite book |last1=Valiant, Leslie |title=Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World |date=2013 |publisher=Basic Books |location=New York |isbn=978-0465032716 }}</ref> Ecorithms and fuzzy logic also have the common property of dealing with possibilities more than probabilities, although feedback and [[feed forward (control)|feed forward]], basically stochastic weights, are a feature of both when dealing with, for example, [[dynamical system]]s.