Path: utzoo!attcan!uunet!zaphod.mps.ohio-state.edu!uakari.primate.wisc.edu!crdgw1!greenba From: greenba@gambia.crd.ge.com (ben a green) Newsgroups: comp.ai.philosophy Subject: Re: Machine learning Message-ID: Date: 11 Dec 90 16:26:18 GMT References: <4158@dftsrv.gsfc.nasa.gov> Sender: news@crdgw1.crd.ge.com Organization: GE Corporate Research & Development Lines: 42 In-reply-to: jones@amarna.gsfc.nasa.gov's message of 11 Dec 90 02:42:02 GMT In article <4158@dftsrv.gsfc.nasa.gov> jones@amarna.gsfc.nasa.gov (JONES, THOMAS) writes: The question has been raised as to whether or not we could put a learning or "reinforcement" algorithm, perhaps along the lines of Skinner's concepts, and make the machine *learn* all sorts of neat things without their having to be programmed in by humans. This is the oldest, worst idea in AI. Dozens of attempts have been made to carry this effort through (I myself have made a dozen or so.), essentially without success. The problem is that *all* theories of learning in psychology are *unsound.* For example, Skinner would have us believe that, if an organism is rewarded for doing a certain action in a certain situation, then he/she/it will become more likely to perform the action in question in "similar" situations. Horsefeathers! What is a similar action? What is a similar situation? All sorts of heavy machinery have been swept under the rug and labeled "similarity." An excellent point, but I haven't given up trying yet. First, we have to choose the right level of description. Subroutines (mentioned by Tom later) are too low. My choice is based on a robot with, say, 0.1 sec clock speed that emits behavior (motor control signals) at that rate. The problem of similarity is, indeed, swept under the rug by Skinner and all exponents of his ideas I have read. My robot will deal with it in the following way. First, invert the concept to that of _dissimilarity_. We need a map from a pair of environmental vectors to a scalar dissimilarity, which can be thought of as a metric in environmental space. It will have to be a plastic map, since discrimination training increases dissimilarity between previously similar environments. Dissimilarity increases with differential reinforcement. (Think of a spanish speaker learning to distinguish "b" and "v".) My suggestion: Initialize the mapping to near zero and stretch the space as a result of reinforcement. The volume of discriminable dissimilarity starts out as near zero and expands rapidly in the first hours of the robot's life. It's the big bang theory of perception. -- Ben A. Green, Jr. greenba@crd.ge.com Speaking only for myself, of course.