neuron-request@HPLABS.HP.COM ("Neuron-Digest Moderator Peter Marvit") (07/31/89)
Neuron Digest Sunday, 30 Jul 1989 Volume 5 : Issue 32 Today's Topics: Administrivia Abstracts from Journal of Experimental and Theoretical AI Book Reviews,Journal of Mathematical Psychology Report available sort of connectionist: Subcognition and the Limits of the Turing Test Technical Report Available TR: CONNECTIONISM AND COMPOSITIONAL SEMANTICS TR announcement Send submissions, questions, address maintenance and requests for old issues to "neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request" Use "ftp" to get old issues from hplpm.hpl.hp.com (15.255.176.205) ------------------------------------------------------------ Subject: Administrivia From: "Neuron-Digest Moderator -- Peter Marvit" <neuron@hplabs.hp.com> Date: Sun, 30 Jul 89 21:21:30 -0700 Greetings, My department moved last week. My systems's name remain the same, but its Internet address changed. It was, however, unavailable for most of the time in any case. Things have stabilized and you can now retrieve Digest back issues via anonymous ftp from hplpm.hpl.hp.com (15.255.176.205). I'm interested in any trip reports or reactions from IJCNN last month. What did people see, hear, or think? -Peter Marvit Your Immoderator ------------------------------ Subject: Abstracts from Journal of Experimental and Theoretical AI From: cfields@NMSU.Edu Date: Sun, 09 Apr 89 15:56:55 -0600 _________________________________________________________________________ The following are abstracts of papers appearing in the second issue of the Journal of Experimental and Theoretical Artificial Intelligence, to appear in April, 1989. For submission information, please contact either of the editors: Eric Dietrich Chris Fields PACSS - Department of Philosophy Box 30001/3CRL SUNY Binghamton New Mexico State University Binghamton, NY 13901 Las Cruces, NM 88003-0001 dietrich@bingvaxu.cc.binghamton.edu cfields@nmsu.edu JETAI is published by Taylor & Francis, Ltd., London, New York, Philadelphia _________________________________________________________________________ Generating plausible diagnostic hypotheses with self-processing causal networks Jonathan Wald, Martin Farach, Malle Tagamets, and James Reggia Department of Computer Science, University of Maryland A recently proposed connectionist methodology for diagnostic problem solving is critically examined for its ability to construct problem solutions. A sizeable causal network (56 manifestation nodes, 26 disorder nodes, 384 causal links) served as the basis of experimental simulations. Initial results were discouraging, with less than two-thirds of simulations leading to stable solution states (equilibria). Examination of these simulation results identified a critical period during simulations, and analysis of the connectionist model's activation rule during this period led to an understanding of the model's nonstable oscillatory behavior. Slower decrease in the model's control parameters during the critical period resulted in all simulations reaching a stable equilibrium with plausible solutions. As a consequence of this work, it is possible to more rationally determine a schedule for control parameter variation during problem solving, and the way is now open for real-world experimental assessment of this problem solving method. _________________________________________________________________________ Organizing and integrating edge segments for texture discrimination Kenzo Iwama and Anthony Maida Department of Computer Science, Pennsylvania State University We propose a psychologically and psychophysically motivated texture segmentation algorithm. The algorithm is implemented as a computer program which parses visual images into regions on the basis of texture. The program's output matches human judgements on a very large class of stimuli. The program and algorithm offer very detailed hypotheses of how humans might segment stimuli, and also suggest plausible alternative explanations to those presented in the literature. In particular, contrary to Julesz and Bergen (1983), the program does not use crossings as textons and does use corners as textons. Nonetheless, the program is able to account for the same data. The program accounts for much of the linking phenomena of Beck, Pradzny, and Rosenfeld (1983). It does so by matching structures between feature maps on the basis of spatial overlap. These same mechanisms are also used to account for the feature integration phenomena of Triesman (1985). - ---------------------------------------------------------------------------- Towards a paradigm shift in belief representation methodology John Barnden Computing Research Laboratory, New Mexico State University Research programs must often divide issues into managable sub-issues. The assumption is that an approach developed to cope with a sub-issue can later be integrated into an approach to the whole issue - possibly after some tinkering with the sub-approach, but without affecting its fundamental features. However, the present paper examines a case where an AI issue has been divided in a way that is, apparently, harmless and natural, but is actually fundamentally out of tune with the realities of the issue. As a result, some approaches developed for a certain sub-issue cannot be extended to a total approach without fundamental modification. The issue in question is that of modeling people's beliefs, hopes, intentions, and other ``propositional attitudes'', and/or interpreting natural language sentences that report propositional attitudes. Researchers have, quite understandably, de-emphasized the problem of dealing in detail with nested attitudes (e.g. hopes about beliefs, beliefs about intentions about beliefs), in favor of concentrating on the sub-issue of nonnested attitudes. Unfortunately, a wide variety of approaches to attitudes are prone to a deep but somewhat subtle problem when they are applied to nested attitudes. This problem can be very roughly described as an AI system's unwitting imputation of its own arcane ``theory'' of propositional attitudes to other agents. The details of this phenomenon have been published elsewhere by the author: the present paper merely sketches it, and concentrates instead on the methodological lessons to be drawn, both for propositional attitude research and, more tentatively, for AI in general. The paper also summarizes an argument (presented more completely elsewhere) for an approach to attitude representation based in part on metaphors of mind that are commonly used by people. This proposed new research direction should ultimately coax propositional attitude research out of the logical armchair and into the pyschological laboratory. - --------------------------------------------------------------------------- The graph of a boolean function Frank Harary Department of Computer Science, New Mexico State University (Abstract not available) ------------------------------ Subject: Book Reviews,Journal of Mathematical Psychology From: INAM000 <INAM%MCGILLB.BITNET@VMA.CC.CMU.EDU> Date: Sat, 01 Apr 89 12:40:00 -0500 The purpose of this mailing is to (re)draw your attention to the fact that the Journal of Mathematical Psychology, published by Academic Press, publishes reviews of books in the general area of mathematical (social, biological,....) science. For instance, in a forthcoming issue, a review of the revised edition of Minsky and Papert's PERCEPTRONS will appear (written by Jordan Pollack). The following is a partial list of books that we have recently received that I would like to get reviewed for the Journal - -those most relevant to this group are marked by *s. As you will see, most of them are edited readings, which are hard to review. However, if you are interested in reviewing one or more of the books, I would like to hear from you. Our reviews are additions to the literature, not "straight" reviews, so writing a review for us gives you an opportunity to express your views on a field of research. I would also like to be kept informed of new books in this general area that you think we should review (or at least list in our Books Received section). And, of course, one reward for writing a review is that you receive a complimentary copy of the book. (SELECTED) Books Received The following books have been received for review.We encourage readers to volunteer themselves as reviewers.We consider our reviews contributions to the literature ,rather than "straight" reviews,and thus reviewers have considerable freedom in terms of format,length,and content of their reviews.Readers who would like to review any of these or previously listed books should contact A.A.J.Marley , Department of Psychology , McGill University,1205 Avenue Dr. Penfield,Montreal,Quebec H3A 1B1, Canada.(Email address: inam@musicb.mcgill.ca on BITNET). *Amit, D. J. Modelling brain function: The world of attractor neural networks. Cambridge, England: Cambridge University Press,1989. Pp. 500. Collins,A. and Smith,E.E. Readings in Cognitive Science.A Perspective from Psychology and Artificial Intelligence.San Mateo,California:Morgan Kaufmann,1988.661pp. *Cotterill,R. M.J. (Ed).Computer Simulation in Brain Sciences.New York,New York: Cambridge University Press,1988.576pp,$65.00. *Grossberg,S. (Ed) Neural Networks and Natural Intelligence.Cambridge, Massachusetts : MIT Press,1988. 637pp. $35.00. Hirst,W. The Making of Cognitive Science.Essays In Honor of George A.Miller.New York,New York: Cambridge University Press,1988.288pp,$29.95. Laird,P.D. Learning from Good and Bad Data.Norwell,Massachusetts: Kluwer Academic,1988.211pp. *MacGregor, R. J. Neural and Brain Modeling. San Diego, California: Academic Press, 1987. 643pp. $95.50. Ortony,A,Clore,G.L. and Collins,A. The Cognitive Structure of Emotions.New York,New York: Cambridge University Press,1988. 175pp,$24.95. *Richards, W. (Ed). Natural Computation. Cambridge, Massachusetts: MIT Press, 1988. 561pp. Shrobe,H.E. and the American Association for Artificial Intelligence (Eds). Exploring Artificial Intelligence:Survey Talks from the National Conferences on Artificial Intelligence.San Mateo,California:Morgan Kaufmann,1988.693pp. Vosniadou,S. and Ortony,A.Similarity and Analogical Reasoning.New York,New York: Cambridge University Press,1988.410pp,$44.50. *Richards,W. (Ed.) Natural Computation.Cambridge,Massachusetts: Bradford/MIT Press,1988.561pp.$25.00. Wilkins,D.E. Practical Planning:Extending the Classical AI Planning Paradigm. San Mateo, California : Morgan Kaufmann, 1988. 205pp. ------------------------------ Subject: Report available From: Catherine Harris <harris%cogsci@ucsd.edu> Date: Tue, 09 May 89 20:51:02 -0700 CONNECTIONIST EXPLORATIONS IN COGNITIVE LINGUISTICS Catherine L. Harris Department of Psychology and Program in Cognitive Science University of California, San Diego Abstract: Linguists working in the framework of cognitive linguistics have suggested that connectionist networks may provide a computational formalism well suited for the implementation of their theories. The appeal of these networks include the ability to extract the family resemblance structure inhering in a set of input patterns, to represent both rules and exceptions, and to integrate multiple sources of information in a graded fashion. The possible matches between cognitive linguistics and connectionism were explored in an implementation of the Brugman and Lakoff (1988) analysis of the diverse meanings of the preposition "over." Using a gradient-descent learning procedure, a network was trained to map patterns of the form "trajector verb (over) landmark" to feature-vectors representing the appropriate meaning of "over." Each word was identified as a unique item, but was not further semantically specified. The pattern set consisted of a distribution of form-meanings pairs that was meant to be evocative of English usage, in that the regularities implicit in the distribution spanned the spectrum from rules, to partial regularities, to exceptions. Under pressure to encode these regularities with limited resources, the nework used one hidden layer to recode the inputs into a set of abstract properties. Several of these categories, such as dimensionality of the trajector and vertical height of the landmark, correspond to properties B&L found to be important in determining which schema a given use of "over" evokes. This abstract recoding allowed the network to generalize to patterns outside the training set, to activate schemas to partial patterns, and to respond sensibly to "metaphoric" patterns. Furthermore, a second layer of hidden units self-organized into clusters which capture some of the qualities of the radial categories described by B&L. The paper concludes by describing the "rule-analogy continuum". Connectionist models are interesting systems for cognitive linguistics because they provide a mechanism for exploiting all points of this continuum. A short version of this paper will be published in The Proceedings of the Fifteenth Annual Meeting of the Berkeley Linguistics Society, 1989. Send requests to: harris%cogsci.ucsd.edu ------------------------------ Subject: sort of connectionist: From: James Hendler <hendler@icsib9.Berkeley.EDU> Date: Wed, 03 May 89 14:29:29 -0700 CALL FOR PAPERS CONNECTION SCIENCE (Journal of Neural Computing, Artificial Intelligence and Cognitive Research) Special Issue -- HYBRID SYMBOLIC/CONNECTIONIST SYSTEMS Connectionism has recently seen a major resurgence of interest among both artificial intelligence and cognitive science researchers. The spectrum of connectionist approaches is quite large, ranging from structured models, in which individual network units carry meaning, through distributed models of weighted networks with learning algorithms. Very encouraging results, particularly in ``low-level'' perceptual and signal processing tasks, are being reported across the entire spectrum of these models. Unfortunately, connectionist systems have had more limited success in those ``higher cognitive'' areas where symbolic models have traditionally shown promise: expert reasoning, planning, and natural language processing. While it may not be inherently impossible for purely connectionist approaches to handle complex reasoning tasks someday, it will require significant breakthroughs for this to happen. Similarly, getting purely symbolic systems to handle the types of perceptual reasoning that connectionist networks perform well would require major advances in AI. One approach to the integration of connectionist and symbolic techniques is the development of hybrid reasoning systems in which differing components can communicate in the solving of problems. This special issue of the journal Connection Science will focus on the state of the art in the development of such hybrid reasoners. Papers are solicited which focus on: Current artificial intelligence systems which use connectionist components in the reasoning tasks they perform. Theoretical or experimental results showing how symbolic computations can be implemented in, or augmented by, connectionist components. Cognitive studies which discuss the relationship between functional models of higher level cognition and the ``lower level'' implementations in the brain. The special issue will give special consideration to papers sharing the primary emphases of the Connection Science Journal which include: 1) Replicability of Results: results of simulation models should be reported in such a way that they are repeatable by any competent scientist in another laboratory. The journal will be sympathetic to the problems that replicability poses for large complex artificial intelligence programs. 2) Interdisciplinary research: the journal is by nature multidisciplinary and will accept articles from a variety of disciplines such as psychology, cognitive science, computer science, language and linguistics, artificial intelligence, biology, neuroscience, physics, engineering and philosophy. It will particularly welcome papers which deal with issues from two or more subject areas (e.g. vision and language). Papers submitted to the special issue will also be considered for publication in later editions of the journal. All papers will be refereed. The expected publication date for the special issue is Volume 2(1), March, 1990. DEADLINES: Submission of papers June 15, 1989 Reviews/decisions September 30, 1989 Final rewrites due December 15, 1989. Authors should send four copies of the article to: Prof. James A. Hendler Associate Editor, Connection Science Dept. of Computer Science University of Maryland College Park, MD 20742 USA Those interested in submitting articles are welcome to contact the editor via e-mail (hendler@brillig.umd.edu - US Arpa or CSnet) or in writing at the above address. ------------------------------ Subject: Subcognition and the Limits of the Turing Test From: Bob French <french@cogsci.indiana.edu> Date: Tue, 23 May 89 11:41:17 -0500 A pre-print of an article on subcognition and the Turing Test to appear in MIND: "Subcognition and the Limits of the Turing Test" Robert M. French Center for Research on Concepts and Cognition Indiana University Ostensibly a philosophy paper (to appear in MIND at the end of this year), this article is of special interest to connectionists. It argues that: i) as a REAL test for intelligence, the Turing Test is inappropriate in spite of arguments by some philosophers to the contrary; ii) only machines that have experienced the world as we have could pass the Test. This means that such machines would have to learn about the world in approximately the same way that we humans have -- by falling off bicycles, crossing streets, smelling sewage, tasting strawberries, etc. This is not a statement about the inherent inability of a computer to achieve intelligence, it is rather a comment about the use of the Turing Test as a means of testing for that intelligence; iii) (especially for connectionists) the physical, subcognitive and cognitive levels are INEXTRICABLY interwoven and it is impossible to tease them apart. This is ultimately the reason why no machine that had not experienced the world as we had could ever pass the Turing Test. The heart of the discussion of these issues revolves around humans' use of a vast associative network of concepts that operates, for the most part, below cognitive perceptual thresholds and that has been acquired over a lifetime of experience with the world. The Turing Test tests for the presence or absence of this HUMAN associative concept network, which explains why it would be so difficult -- although not theoretically impossible -- for any machine to pass the Test. This paper shows how a clever interrogator could always "peek behind the screen" to unmask a computer that had not experienced the world as we had by exploiting human abilities based on the use of this vast associative concept network, for example, our abilities to analogize and to categorize; This paper is short and non-technical but nevertheless focuses on issues that are of significant philosophical importance to AI researchers, and to connectionists in particular. If you would like a copy, please send your name and address to: Helga Keller C.R.C.C. 510 North Fess Bloomington, Indiana 47401 or send an e-mail request to helga@cogsci.indiana.edu - - Bob French french@cogsci.indiana.edu ------------------------------ Subject: Technical Report Available From: <THEPCAP%SELDC52.BITNET@VMA.CC.CMU.EDU> Date: Wed, 17 May 89 13:00:00 +0200 LU TP 89-1 A NEW METHOD FOR MAPPING OPTIMIZATION PROBLEMS ONTO NEURAL NETWORKS Carsten Peterson and Bo Soderberg Department of Theoretical Physics, University of Lund Solvegatan 14A, S-22362 Lund, Sweden Submitted to International Journal of Neural Systems ABSTRACT: A novel modified method for obtaining approximate solutions to difficult optimization problems within the neural network paradigm is presented. We consider the graph partition and the travelling salesman problems. The key new ingredient is a reduction of solution space by one dimension by using graded neurons, thereby avoiding the destructive redundancy that has plagued these problems when using straightforward neural network techniques. This approach maps the problems onto Potts glass rather than spin glass theories. A systematic prescription is given for estimating the phase transition temperatures in advance, which facilitates the choice of optimal parameters. This analysis, which is performed for both serial and synchronous updating of the mean field theory equations, makes it possible to consistently avoid chaotic bahaviour. When exploring this new technique numerically we find the results very encouraging; the quality of the solutions are in parity with those obtained by using optimally tuned simulated annealing heuristics. Our numerical study, which extends to 200-city problems, exhibits an impressive level of parameter insensitivity. For copies of this report send a request to THEPCAP@SELDC52 [don't forget to give your mailing address]. ------------------------------ Subject: TR: CONNECTIONISM AND COMPOSITIONAL SEMANTICS From: Dave.Touretzky@B.GP.CS.CMU.EDU Date: Wed, 31 May 89 21:53:16 -0400 CONNECTIONISM AND COMPOSITIONAL SEMANTICS David S. Touretzky School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890 Technical report CMU-CS-89-147 May 1989 Abstract: Quite a few interesting experiments have been done applying neural networks to natural language tasks. Without detracting from the value of these early investigations, this paper argues that current neural network architectures are too weak to solve anything but toy language problems. Their downfall is the need for ``dynamic inference,'' in which several pieces of information not previously seen together are dynamically combined to derive the meaning of a novel input. The first half of the paper defines a hierarchy of classes of connectionist models, from categorizers and associative memories to pattern transformers and dynamic inferencers. Some well-known connectionist models that deal with natural language are shown to be either categorizers or pattern transformers. The second half examines in detail a particular natural language problem: prepositional phrase attachment. Attaching a PP to an NP changes its meaning, thereby influencing other attachments. So PP attachment requires compositional semantics, and compositionality in non-toy domains requires dynamic inference. Mere pattern transformers cannot learn the PP attachment task without an exponential training set. Connectionist-style computation still has many valuable ideas to offer, so this is not an indictment of connectionism's potential. It is an argument for a more sophisticated and more symbolic connectionist approach to language. An earlier version of this paper appeared in the Proceedings of the 1988 Connectionist Models Summer School. ================ TO ORDER COPIES of this tech report: send electronic mail to copetas@cs.cmu.edu, or write the School of Computer Science at the address above. ------------------------------ Subject: TR announcement From: eric@mcc.com (Eric Hartman) Date: Wed, 17 May 89 14:29:47 -0500 The following technical report is now available. Requests may be sent to eric@mcc.com or via physical mail to the MCC address below. - --------------------------------------------------------------- MCC Technical Report Number: ACT-ST-146-89 Optoelectronic Implementation of Multi-Layer Neural Networks in a Single Photorefractive Crystal Carsten Peterson*, Stephen Redfield, James D. Keeler, and Eric Hartman Microelectronics and Computer Technology Corporation 3500 W. Balcones Center Dr. Austin, TX 78759-6509 Abstract: We present a novel, versatile optoelectronic neural network architecture for implementing supervised learning algorithms in photorefractive materials. The system is based on spatial multiplexing rather than the more commonly used angular multiplexing of the interconnect gratings. This simple, single-crystal architecture implements a variety of multi-layer supervised learning algorithms including mean-field-theory, back-propagation, and Marr-Albus-Kanerva style algorithms. Extensive simulations show how beam depletion, rescattering, absorption, and decay effects of the crystal are compensated for by suitably modified supervised learning algorithms. *Present Address: Department of Theoretical Physics, University of Lund, Solvegatan 14A, S-22362 Lund, Sweden. ------------------------------ End of Neurons Digest *********************