http://www.physorg.com/news82190531.html
"Rabinovich and his colleague at the Institute for Nonlinear Science at the University of California, San Diego, Ramon Huerta, along with Valentin Afraimovich at the Institute for the Investigation of Optical Communication at the University of San Luis Potosi in Mexico, present a new model for understanding decision making. Their paper, titled "Dynamics of Sequential Decision Making," has been published on Physical Review Letters." -----Original Message----- From: Ben Goertzel [mailto:[EMAIL PROTECTED] Sent: Wednesday, November 08, 2006 11:03 AM To: [email protected] Subject: Re: Re: [agi] The crux of the problem Hi, About > But a simple example is > ate a pepperoni pizza > ate a tuna pizza > ate a VEGAN SUPREME pizza > ate a Mexican pizza > ate a pineapple pizza I feel this discussion of sentence parsing and interpretation is taking a somewhat misleading direction, by focusing on examples that are in fact very easy to parse and semantically interpret. When dealing with realistic sentence parsing, things don't work out so simply as in "The cat ate the mouse." There is even some complexity of course in the above example "ate a Mexican pizza" as the adjective Mexican could mean "in the country of Mexico", "Mexican style", or "with a topping composed of pieces of a Mexican person" ;-) But the above examples, overalll, are so simplistic they cover up the real problems with commonsense knowledge and language understandng... Among other complications that arise in practice (some even worse), there are prepositions. Let's go back to our prior example of "with" ... Humans can unproblematically assign senses to "with" in the following sentences. ********** I ate the salad with a fork I ate the salad with a tweezers I ate the salad with my favorite uncle I ate the salad with my favorite pepper "I ate the salad with my favorite uncle," said the cannibal "I ate the salad with my favorite pepper," said the salt. I ate the salad with gusto I ate the salad with Ragu I ate the salad with Gusto I eat steak with red wine, and fish with white wine I eat fish with beer batter ****** Our intended approach to this problem (preposition disambiguation) within Novamente is to teach the system groundings for many sentences of this nature within the AGISim simulation world. Then, when it sees a sentence of this nature containing a concept that it hasn't seen in the simulation world, it must match the concept to ones it has seen in the simulation world, and make a guess. For instance, it may have seen and learned to understand "I ate the salad with a fork" "I ate the salad with an olive" in the sim world, so that when it sees "I ate the salad with a tweezers" it needs to realize that a tweezers is more like a fork than like an olive (since it is not edible, and is a tool), and so the sense of "with" in this latter sentence is probably like the sense in "I ate the salad with a fork." [One way for the system to realize the similarity between fork and tweezers is to use WordNet, in which both are classified as noun.artifact] What the sim world grounding gives the AI system is a full understanding of what the various senses of "with" actually mean. For instance, in "I ate the salad with a fork", what we really have is with_tool( I ate the salad, a fork) i.e. (in one among many possible notations) with_tool( A, B) Inheritance(B, fork) B := eat(I, salad) past(B) and thru interacting and learning in the sim world, the system learns various relationships related to the predicate with_tool. Once it guesses that the "I ate the salad with the tweezers" is correctly mapped into with_tool( A, B) Inheritance(B, tweezers) B := eat(I, salad) past(B) then it can use its knowledge about with_tool, gained in the sim world, to reason about the situation described. For example, it often takes an agent practice to learn to use a tool. A system that has had some experience with instances of with_tool in the sim world will know this, and will have learned that the effectiveness with which B can be used by C as a tool for A may depend on the amount of experience that C has in using B as a tool, particularly in using B as a tool in contexts similar to A. Thus, the system could respond to "I ate the salad with a tweezers" with "Is that difficult?" (knowing that, since eating salad with tweezers is unusual (since e.g. a Google search reveals few instances of it), it is likely that the speaker may not have had much practice doing it, so it may be difficult for the speaker.) This is just one among very very many examples of probabilistic/fuzzy commonsense knowledge about preposition senses. In order to interpret texts correctly an AGI system needs to have this commonsense knowledge. Otherwise, even if it correctly figures out that the intended meaning is with_tool( I eat the salad, a tweezers) it won't be able to draw the commonsensically expected implications from this. So, how to get all this probabilistic commonsense knowledge (which in humans is mostly unconscious) into the AGI system? This is where we are back to the good old alternatives, of a-- embodied learning b-- exhaustive education through NLP dialogue in very simple English c-- exhaustive education through dialogue in some artificial language like Lojban++ d-- make a big nasty database like Cyc (and try to do a better job) My bet is that a is the best foundational approach, with some augmentation by the other approaches. (Though I don't plan to embark upon d at all, I am willing to make use of DB's constructed by others. I note that there is no standard DB of preposition senses, though we have made one within Novamente for a narrow-AI NLP consulting project, a couple years ago.) Note that in my above example of interpretation of a very simple sentence I casually assumed integration of * simple language parsing * experience with tool usage in a sim world * WordNet * frequency counting based on Google searches I think this kind of integrative approach has plenty of promise. We are proceeding in this direction with Novamente, but slowly, due to having only a small amount of staff focused on the project, and having a pretty complex (necessarily, I believe) AI design. But IMO, from an AGI point of view all this is sorta "surface level" stuff. The key part in the above story is the learning engine that allows the system to learn commonsense information about tool usage from its embodied experience. The other inferences involved are not that hard and are in fact easily carried out by Novamente's current inference system. The language parsing involved in the above example is trivial and is done by our NLP parser as by many, many others. We are already using WordNet; we aren't using frequency counting based on Google searches but that's obviously "just engineering".... What is harder, and is the focus of much of our effort right now, is learning useful generalizable commonsense rules from embodied experience, using a combination of probabilistic inference, evolutionary learning and (to be integrated in 2007) economic attention allocation. So, I feel much of the present discussion on NLP interpretation is bypassing the hard problem, which is enabling an AGI system to learn the millions or billions of commonsense (probabilistic) rules relating to basic relationships like with_tool, which humans learn from experience.... Eric Baum argues that we humans have a lot of inbuilt inductive bias that helps us to learn these rules more efficiently than an AI system would be able to. This may be right.... OTOH our AGI systems have WordNet and Google, and possibly cleverer learning algorithms than the human brain.... -- Ben G ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
