Ed, Consider a probabilistic implication of the general form
Context & Procedure ==> Goal meaning (if Context C is present) & (Procedure P is executed) ==> (Goal G is satisfied) Suppose that C and G are known but P is not known Then, MOSES may be used to find P That is, if the system knows what situation it is in (C) and knows what goal it wants to achieve (G), then MOSES may be used to help find a procedure to fulfill the goal in the context Note that the goal may be derived via inferential subgoaling from other goals. Note that determining the right way to represent the current situation as a predicate (a context) is a hard inference/concept-creation problem too The fitness function may be, for instance, the strength * confidence of the implication link denoted ==> above. The fitness of a candidate procedure P1 may be evaluated either -- by direct evaluation, i.e. by executing P1 and seeing what happens -- by simulation, i.e. by executing P1 in some internal simulation world rather than in the real world, and seeing what happens -- by inference (PLN) All that is said plainly in the OpenCogPrime wikibook, but maybe the above explanation will be helpful due to its compactness? ben On Tue, Dec 16, 2008 at 7:23 PM, Ed Porter <ewpor...@msn.com> wrote: > Moshe and Ben, > > > > I feel I understand much of Novamente, even parts that I haven't heard > explained, because it is quite similar to ideas I had developed before ever > hearing about Novamente. > > > > But I have never quite understood the role of MOSES in Novamene. > > > > I do not question the power of genetic programming. Ever since I attended > a 1999 lecture by Koza on what he had managed to accomplish with genetic > programming --- and then spent about half an hour talking with him after the > lecture and at other times at the supercomputing conference at which it > occurred --- I have been very aware of GP's potential power. I am quite > certain he said that in roughly a tera-opp (which could be done in one > thousandth of a second on the current fastest computer) he claimed it > derived a band-pass filter it took humanity almost 3 decades (until the > 1940s) to develop after appearance of the earliest band-pass filters. > > > > But I haven't been able to figure out exactly how MOSES is used in the > NOVAMENTE environment. For example, Koza said a key to the success of his > genetic programming was having a task for which there was an appropriate > fitness function. He said that in his experiments using GP to design new > electronic circuitry to operate as relatively optimal band-pass filters the > fitness function to determine how well each proposed solution functioned was > the electronic simulation software called SPICE. He estimated roughly 99% > of the his network's compute time (i.e., the above mentioned 1 TeraOpps) was > spent evaluating this fitness function. > > > > From a recent re-reading all the portion of a January 2007 version of Ben's > Novamente book (an earlier version of the Open Cog documentation) that > related to MOSES, I don't remember any clear explanation of what fitness > function would be used to evaluate the presumably many thousands, or > millions, of combo programs that would be generated in an attempt to solve a > single problem. > > > > For example, in a pet brain, the pet presumably would not get a chance to > try out each of the thousands of individual combo programs on a human user, > to see which received a proper feedback from the user, without thoroughly > exhausting the human user. > > > > (?1) So what fitness function would be used to select combo programs or > direct the probabilistic distributions that are used to tune their spawning? > (If this is knowledge you intend to be in the public domain.) > > > > Also, I don't understand the relationship of combo programs to the > hypergraph. Combo uses a functional language which presumably seeks to do > away with, or greatly restrict, side effects (obvioulsly a plus, if you are > somewhat blindly cutting and pasting program fragments together) --- whereas > it seems spreading activation in a hypergraph is largely all about side > effects. (The 1000 to 10K synapses per neuron, plus electromagnetic field > effects caused by neurons in the brain, sure sound like side effects up the > wazoo to me.) > > > > I understand (a) that a combo program could be associated with individual > nodes and be computed when they are activated, (b) that hypergraph nodes or > edge values can be variables in a combo expression, (c) that some subset of > hypergraph spreading-activation inferencing ( I didn't understand exactly > which) can be used as functions in combo expressions, and (d) that the > hypergraph can be used to record and generalize information about a combo > program to appropriately enable inference in the hypergraph to appropriately > active combo programs associated with particular hypergraph nodes. > > > > (?2) Am I correct in understanding that item (d) just listed could be quite > important as a general concept in AGI learning how to automatically program, > because it would allowed the non-combo aspects of Novamente to model and use > probabilistic inference and attention focusing to reason about combo > programs and when they should be used, combined, fed what input, or perhaps > even be modified? > > > > > > (?3) Am I also correct in guessing that (b) and (c) would seem to enable > combo programs, to, in effect, create and try out (provided a proper fitness > function) novel hypergraph nodes, which would function in a manner similar > to non-combo nodes largely through spreading activation? > > > > (?4) Other than what is explained above, how are combo programs and > hypergraphs synergistically used in Novamente. > > > > Moshe and Ben, you are both very bright --- and you both place a lot of > importance on incorporating MOSES into Novamente --- so I assume there is > something important I am missing. > > > > I would appreciate it very much if you could tell me what it is that I am > missing. > > > > Ed Porter > > > > -----Original Message----- > From: Moshe Looks [mailto:madscie...@google.com] > Sent: Monday, December 15, 2008 1:33 PM > To: agi@v2.listbox.com > Subject: [agi] internship opportunity at Google (Mountain View, CA) > > > > Hi, > > > > I am seeking an intern to work on the open-source probabilistic > > learning of programs project over Summer 2009 at Google in Mountain > > View, CA. Probabilistic learning of programs (plop) is a Common Lisp > > framework for experimenting with meta-optimizing semantic evolutionary > > search (MOSES) and related approaches to learning with probability > > distributions over program spaces. Possible research topics to focus > > on include: > > > > * Learning procedural abstractions > > * Adapting estimation-of-distribution algorithms to program evolution > > * Applying plop to various interesting data sets > > * Adapting plop to do natural language processing or image processing > > * Better mechanisms for exploiting background knowledge in program > evolution > > > > This position is open to all students currently pursuing a BS, MS or > > PhD in computer science or a related technical field. It is probably > > better-suited to a grad student, but I'm open to considering an > > advanced undergrad as well. The only hard and fast requirements for > > consideration are a strong programming background (any language(s)) > > and some experience in AI and/or machine learning. Some pluses: > > > > * Functional programming experience (esp. Lisp, but ML, Haskell, or > > even the functional style of C++ count too) > > * Experience with evolutionary computation or stochastic local search > > (esp. estimation-of-distribution algorithms and/or genetic > > programming) > > * Open-source contributor > > > > More info on plop at http://code.google.com/p/plop/, more info on the > > Google internship program at: http://www.google.com/jobs/students > > > > Please contact me directly (off-list) if you are interested. > > > > Thanks! > > Moshe Looks > > > > P.S. Disclaimer: I can't promise anyone an internship, you have to go > > through the standard Google application & interview process for > > interns, yada yada ... > > > > > > ------------------------------------------- > > agi > > Archives: https://www.listbox.com/member/archive/303/=now > > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > > Modify Your Subscription: > https://www.listbox.com/member/?&; > > Powered by Listbox: http://www.listbox.com > > --~--~---------~--~----~------------~-------~--~----~ > You received this message because you are subscribed to the Google Groups > "OpenCog General & Scientific Discussion List" group. > To post to this group, send email to open...@googlegroups.com > To unsubscribe from this group, send email to > opencog+unsubscr...@googlegroups.com<opencog%2bunsubscr...@googlegroups.com> > For more options, visit this group at > http://groups.google.com/group/opencog?hl=en > -~----------~----~----~----~------~----~------~--~--- > > -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI b...@goertzel.org "I intend to live forever, or die trying." -- Groucho Marx ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=123753653-47f84b Powered by Listbox: http://www.listbox.com
