On Sat, Feb 10, 2018 at 8:55 AM, Ben Goertzel <b...@goertzel.org> wrote:
> > You are of course aware that Yidne has been working on porting Reduct > to the URE ? There does not seem to be any activity on github that would suggest such work is underway. I strongly urge that work not be carried out in some private corner, and then revealed to the world as a fait-accompli. > > Fitness evaluation should not be tricky here, basically a fitness > function can be a GroundedSchemaNode > Umm, fitness evaluation is "trickier" than reduct. It is the the primary bottleneck in moses. Moshe created a lot of technology to make this go fast. Nil also devoted a huge amount of time and attention on this. Reduct might feel difficult, because if you just glance at the code, it obviously looks arcane. The fitness evaluation code looks like it's easy by comparison. This is highly misleading. Computer software has a kind of inversion effect, where often it is the easiest-looking things that are the hardest. Do not be fooled about where the actual complexity lies. > > (Obviously the big intended payoff here is a return to the roots of > MOSES, i.e. the use of probabilistic analysis to find patterns > regarding which program-trees in a deme are successful. Well,some caution is needed to visualize what is really happening here. You have that probabilistic analysis already available now: you can easily extract a measure from a collection of program trees, in several different ways. That measure is fractal; don't imagine that its smooth. I just now wrote, and then deleted a long email on the actual technicalities and experience of doing this by hand. The general upshot is that its a lot more subtle than what one might imagine. The notion of "returning to the roots of MOSES", is I feel, exactly the wrong direction to go in: we moved away from those roots for a reason, and got a much stronger, faster and more robust system as a result. Let me put it this way: asking "which program-trees in a deme are successful" is kind of like asking "which neurons in a neural net are successful", or "who are the John Galts of society" This is like focusing on the point dynamics in a fractal -- it identifies a set of measure zero, giving a completely faulty understanding of the fractal (dynamical system) The system dynamics is governed by measures, not by individuals. The individuals are the exception. MOSES is more accurate, the larger the population. Having 1000 exemplars will give you far more accurate answers, than what you'd get if you tried to trim this to the top-10 most successful. Put another way: ten super-accurate, highly-evolved expert trees are less accurate than the democratic vote of 1000 simple-minded average joes, when yo just want an answer for a "typical" dataset. Linas. -- cassette tapes - analog TV - film cameras - you -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to opencog+unsubscr...@googlegroups.com. To post to this group, send email to opencog@googlegroups.com. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/CAHrUA36Qu8SR55YZGKu460c2QZnJDOC3tMyYZ%3D1g%3DSPZvSBG6g%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
