NetNews Usenet Archive 1992 #27

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Path: sparky!uunet!zaphod.mps.ohio-state.edu!cis.ohio-state.edu!ucbvax!CATTELL.PSYCH.UPENN.EDU!neuron-request From: neuron-request@CATTELL.PSYCH.UPENN.EDU ("Neuron-Digest Moderator") Newsgroups: comp.ai.neural-nets Subject: Neuron Digest V10 #20 Message-ID: <5312.722376316@cattell.psych.upenn.edu> Date: 21 Nov 92 20:05:16 GMT Sender: daemon@ucbvax.BERKELEY.EDU Reply-To: "Neuron-Request" <neuron-request@cattell.psych.upenn.edu> Distribution: world Organization: University of Pennsylvania Lines: 684 Neuron Digest Saturday, 21 Nov 1992 Volume 10 : Issue 20 Today's Topics: workshop announcement Call for Papers, NNSP'93 Call for Participation NIPS 92 Workshop on Training Issues Post-NIPS Robot Learning workshop program Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: workshop announcement From: Joachim Beer <beer@ICSI.Berkeley.EDU> Date: Tue, 03 Nov 92 10:47:35 -0800 *************************************************** * Workshop on Software & Programming Issues for * * Connectionist Supercomputers * *************************************************** April 19-20, 1993 at International Computer Science Institute (ICSI) 1947 Center Street Berkeley, CA 94704 Sponsored by: Adaptive Solutions, Inc. ICSI Siemens AG The goal of this workshop is to bring together connectionist researchers to address software and programming issues in the framework of large scale connectionist systems. Scope and technical theme of the workshop is outlined below. Due to space considerations the workshop will be by invitation only. Interested parties are encouraged to submit a one-page proposal outlinig their work in this area by January 31. Submissions should be send to ICSI at the address above or by e-mail to beer@icsi.berkeley.edu The increased importance of ANNs for elucidating deep conceptual questions in artificial intelligence and their potential for attacking real world problems warrant the design and construction of connectionist supercomputers. Several research labs have undertaken to develop such machines. These machines will allow researchers to investigate and apply ANNs on a scale which up to now was not computationally feasible. As with other parallel hardware, the main problem is adequate software for connectionist supercomputers. Most "solutions" offer isolated instances which deal only with a limited class of particular ANN algorithms rather than providing a comprehensive programming model for this new paradigm. This approach was acceptable for small and structurally simple ANNs. However, to fully utilize the emerging connectionist supercomputers an expressive, clean, and flexible software environment is called for. This is being recognized by the developers of the connectionist supercomputers, and an intergral part of these projects is the development of an appropriate software environment. While each connectionist supercomputer project has unique goals and possibly a focus on particular application areas, it would nevertheless be very fruitful to compare how the fundamental software questions that everybody in this field faces are being approached. The following (incomplete) list outlines some of the issues: * Embedding connectionist systems in traditional software environments, eg. client/server models vs. integrated "seamless" environments. * ANN description languages * Handling of sparse and irregular nets * Facilities for mapping nets onto the underlying architecture * Handling of complete applications including embedded non-connectionist instructions * Should there be a machine independent intermediate language? What would be the disadvantages? * Software issues for dedicated embedded ANNs vs. "general purpose" connectionist supercomputers. * Graphical user interfaces for ANN systems * System support for high I/O rates (while this is a general question in comp. sci. there are nevertheless some unique problems for ANN systems in dealing with large external data sets). ------------------------------ Subject: Call for Papers, NNSP'93 From: "Gary M. Kuhn" <gmk@osprey.siemens.com> Date: Wed, 04 Nov 92 12:33:46 -0500 CALL FOR PAPERS _______________ 1993 IEEE Workshop on Neural Networks for Signal Processing September 7-9, 1993 Baltimore, MD, USA Sponsored by the IEEE Technical Committee on Neural Networks in cooperation with the IEEE Neural Networks Council The third of a series of IEEE workshops on Neural Networks for Signal Processing will be held at the Maritime Institute of Technology and Graduate Studies, Linthicum, Maryland, USA, in September of 1993. Papers are solicited for, but not limited to, the following topics: 1. Applications: Image processing and understanding, speech recognition, communications, sensor fusion, medical diagnoses, nonlinear adaptive filtering and other general signal processing and pattern recognition topics. 2. Theory: Neural network system theory, identification and spectral estimation, and learning theory and algorithms. 3. Implementation: Digital, analog, and hybrid technologies and system development. Prospective authors are invited to submit 4 copies of extended summaries of no more than 6 pages. The top of the first page of the summary should include a title, authors' names, affiliations, address, telephone and fax numbers and email address if any. Camera-ready full papers of accepted proposals will be published in a hard-bound volume by IEEE and distributed at the workshop. Due to workshop facility constraints, attendance will be limited with priority given to those who submit written technical contributions. For further information, please contact Karin Cermele at the NNSP'93 Princeton office, (Tel.) +1 609 734 3383, (Fax) +1 609 734 6565, (e-mail) kic@learning.siemens.com. PLEASE SEND PAPER SUBMISSIONS TO: _______________ NNSP'93 Siemens Corporate Research 755 College Road East Princeton, NJ 08540 USA SCHEDULE _______________ Submission of extended summary: February 15 Notification of acceptance: April 19 Submission of photo-ready paper: June 1 Advanced registration, before: June 1 WORKSHOP COMMITTEE _______________ General Chairs Gary Kuhn Barbara Yoon Siemens Corporate Research DARPA-MTO 755 College Road East 3701 N. Fairfax Dr. Princeton, NJ 08540, USA Arlington, VA 22203-1714 USA gmk@learning.siemens.com byoon@a.darpa.mil Program Chair Proceedings Chair Rama Chellappa Candace Kamm Dept. of Electrical Engineering Box 1910 University of Maryland Bellcore, 445 South Street College Park, MD 20742, USA Morristown, NJ 07962, USA chella@eng.umd.edu cak@bellcore.com Finance Chair Raymond Watrous Siemens Corporate Research 755 College Road East Princeton, NJ 08540, USA watrous@learning.siemens.com Program Committee Joshua Alspector John Makhoul Les Atlas B.S. Manjunath Charles Bachmann Tomaso Poggio Gerard Chollet Jose Principe Frank Fallside Ulrich Ramacher Lee Giles Noboru Sonehara S.J. Hanson Eduardo Sontag Y.H. Hu J.A.A. Sorensen B.H. Juang Yoh'ichi Tohkura Shigeru Katagiri Christoph von der Malsburg S.Y. Kung Christian Wellekens Yann LeCun ------------------------------ Subject: Call for Participation From: "Dr. Francis T. Marchese" <MARCHESF%PACEVM.BITNET@BITNET.CC.CMU.EDU> Date: 07 Nov 92 16:28:14 -0500 *** Call For Participation *** Conference on Understanding Images Sponsored By NYC ACM/SIGGRAPH and Pace University's School of Computer Science and Information Systems To Be Held at: Pace University New York City, New York May 21-22,1993 Artists, designers, scientists, engineers and educators share the problem of moving information from one mind to another. Traditionally, they have used pictures, words, demonstrations, music and dance to communicate imagery. However, expressing complex notions such as God and infinity or a seemingly well defined concept such as a flower can present challenges which far exceed their technical skills. The explosive use of computers as visualization and expression tools has compounded this problem. In hypermedia, multimedia and virtual reality systems vast amounts of information confront the observer or participant. Wading through a multitude of simultaneous images and sounds in possibly unfamiliar representations, a confounded user asks: What does it all mean? Since image construction, transmission, reception, decipherment and ultimate understanding are complex tasks strongly influenced by physiology, education and culture; and since electronic media radically amplify each processing step, then we, as electronic communicators, must determine the fundamental paradigms for composing imagery for understanding. Therefore, the purpose of this conference is to bring together a breadth of disciplines, including, but not limited to, the physical, biological and computational sciences, technology, art, psychology, philosophy and education, in order to define and discuss the issues essential to image understanding within the computer graphics context. To this end we seek proposals for individual presentations, panel discussions, static displays, interactive environments, performances and beyond. Submissions: Contributors are requested to submit a one page proposal by January 15, 1993. Accepted presentations will be included in the proceedings. Direct all inquires and submissions to: Professor Francis T. Marchese Department of Computer Science Pace University New York, NY 10038 USA Email: MARCHESF@PACEVM.Bitnet Phone: 212-346-1803 Fax: 212-346-1933 ------------------------------ Subject: NIPS 92 Workshop on Training Issues From: "Scott A. Markel x2683" <sam@vaxserv.sarnoff.com> Date: Thu, 19 Nov 92 11:24:12 -0500 **************************** NIPS 92 Workshop **************************** "Computational Issues in Neural Network Training" or Why is Back-Propagation Still So Popular? ******************************************************************************* Roger Crane and I are are leading a NIPS '92 workshop on "Computational Issues in Neural Network Training". Our workshop will be on Saturday, 5 December, the second of two days of workshops in Vail. The discussion will focus on optimization techniques currently used by neural net researchers, and include some other techniques that are available. Back- propagation is still the optimization technique of choice even though there are obvious problems in training with BP: speed, convergence, ... . Several innovative algorithms have been proposed by the neural net community to improve upon BP, e.g., Scott Fahlman's QuickProp. We feel that there are classical optimization techniques that are superior to back-propagation. In fact, gradient descent (BP) fell out of favor with the mathematical optimization folks way backin the 60's! So why is BP still so popular? Topics along these lines include: * Why are classical methods generally ignored? * Computational speed * Convergence criteria (or lack thereof!) Broader issues to be discussed include: * Local minima * Selection of starting points * Conditioning (for higher order methods) * Characterization of the error surface If you would like to present something on any of these or similar topics, please contact me by e-mail and we can discuss details. Workshops are scheduled for a total of four hours. We're allowing for approximately 8 presentations of 10-20 minutes each, since we want to make sure that ample time is reserved for discussion and informal presentations. We will encourage (incite) lively audience participation. By the way, none of the NIPS workshops are limited to presenters only. People who want to show up and just listen are more than welcome. Scott Markel Computational Science Research smarkel@sarnoff.com David Sarnoff Research Center Tel. 609-734-2683 CN 5300 FAX 609-734-2662 Princeton, NJ 08543-5300 ------------------------------ Subject: Post-NIPS Robot Learning workshop program From: David Cohn <cohn@psyche.mit.edu> Date: Wed, 04 Nov 92 15:52:45 -0500 PROGRAM FOR THE POST-NIPS WORKSHOP "ROBOT LEARNING" Vail, Colorado, Dec 5th, 1992 NIPS=92 Workshop: Robot Learning ================= Intended Audience: Connectionists and Non-Connectionists in Robotics, ================== Control, and Active Learning Organizers: =========== Sebastian Thrun (CMU) Tom Mitchell (CMU) David Cohn (MIT) thrun@cs.cmu.edu mitchell@cs.cmu.edu cohn@psyche.mit.edu Program: ======== Robot learning has grasped the attention of many researchers over the past few years. Previous robotics research has demonstrated the difficulty of manually encoding sufficiently accurate models of the robot and its environment to succeed at complex tasks. Recently a wide variety of learning techniques ranging from statistical calibration techniques to neural networks and reinforcement learning have been applied to problems of perception, modeling and control. Robot learning is characterized by sensor noise, control error, dynamically changing environments and the opportunity for learning by experimentation. This workshop will provide a forum for researchers active in the area of robot learning and related fields. It will include informal tutorials and presentations of recent results, given by experts in this field, as well as significant time for open discussion. Problems to be considered include: How can current learning robot techniques scale to more complex domains, characterized by massive sensor input, complex causal interactions, and long time scales? How can previously acquired knowledge accelerate subsequent learning? What representations are appropriate and how can they be learned? Although each session has listed "speakers," the intent is that each speaker will not simply present their own work, but will introduce their work interactively, as a launching point for group discussion on their chosen area. After all speakers have finished, the remaining time will be used to discuss at length issues that the group feels need most urgently to be addressed. Below, we have listed the tentative agenda, which is followed by brief abstracts of each author's topic. For those who wish to get a head start on the workshop, we have included a list of references and/or recommended readings, some of which are available by anonymous ftp. ===================================================================== ===================================================================== AGENDA ===================================================================== ===================================================================== SESSION ONE (early morning session), 7:30 - 9:30: ------------------------------------------------- TITLE: "Robot learning: scaling up and state of the art" Keynote speaker: Chris Atkeson (30 min) "Paradigms for Robot Learning" Speakers: Steve Hanson (15 min) (title to be announced) Satinder Singh (15 min) Behavior-Based Reinforcement Learning Andrew W. Moore(15 min) The Parti-Game Algorithm for Variable Resolution Reinforcement Learning Richard Yee (15 min) Building Abstractions to Accelerate Weak Learners SESSION TWO (apres-ski session), 4:30 - 6:30: --------------------------------------------- PANEL: "Robot learning: Where are the new ideas coming from?" Keynote speaker: Andy Barto (30 min) Speakers: Tom Mitchell (10 min each) Chris Atkeson Dean Pomerleau Steve Suddarth ===================================================================== ===================================================================== ABSTRACTS ===================================================================== Session 1: Scaling up and the state of the art When: Saturday, Dec 5, 7:30-9:30 a.m. ===================================================================== ===================================================================== Keynote: Chris Atkeson (cga@ai.mit.edu) Title: Paradigms for Robot Learning Abstract: This talk will survey a variety of robot learning tasks and learning paradigms to perform those tasks. The tasks include pattern classification, regression/function approximation, root finding, function optimization, designing feedback controllers, trajectory following, stochastic modeling, stochastic control, and strategy generation. Given this wide range of tasks it seems reasonable to ask if there is any commonality among them, or any way in which solving one task might make other tasks easier to perform. In our own work we have typically taken an indirect approach: our learning algorithms explicitly form models, and then solve the problem using algorithms that assume complete knowledge. It is not at all clear which learning tasks are best dealt with using an indirect approach, and which are handled better with a direct approach in which the control strategy is learned directly. Nor is it clear how to cope with uncertainty and incomplete knowledge, either by modeling it explicitly, using stochastic models, or using game theory and assuming a malevolent world. I hope to provoke a discussion on these issues. ====================================================================== Presenter: Satinder Pal Singh (singh@cs.umass.edu) Title: Behavior-Based Reinforcement Learning Abstract: Control architectures based on reinforcement learning have been successfully applied to agents/robots that use their repertoire of primitive control actions to achieve goals in an external environment. The optimal policy for any goal is a state-dependent composition of the given "primitive" policies (a primitive policy "A" assigns action A to every state). In that sense, the primitive policies form the "basis" set from which optimal solutions can be "composed". I argue that reinforcement learning can be greatly accelerated by redefining the basis set of policies available to the agent. These redefined basis policies should correspond to "behaviors" that are useful across the set of tasks faced by the agent. Behavior-based RL, i.e., the application of RL to behavior-based robotics (ref Brooks), has several advantages: it can drastically reduce the effective dimensionality of the action space, it provides a framework for incorporating prior knowledge into RL architectures, it provides a technique for achieving transfer of learning, and finally by restricting the rules of composition and the types of behaviors it may become possible to perform "robust" reinforcement learning. I will provide examples from my own work and that of others to illustrate these ideas. (Refs 4, 5, 6) ====================================================================== Presenter: Andrew W. Moore (awm@ai.mit.edu) Title The Parti-Game Algorithm for Variable Resolution Reinforcement Learning Can we efficiently learn in continuous state-spaces, while requiring only relatively few real-world experienvces during the learning stage? Dividing a continuous state-space into a fine grid can mean a tragically large number of unnecessary experiences, while a coarse grid or parametric representation can become stuck. This talk overviews a new algorithm which, in real time, tries to adaptively alter the resolution of a state space partitioning to be coarse where it can and fine where it must to be if it is to avoid becoming stuck. The key idea turns out to be the treatment of the problem as a game instead of a Markov decision task. Possible prior reading: Ref 7 (Overview of some other uses of kd-trees in Machine learning) Ref 8 (A non-real-time algorithm which uses a different partitioning strategy) Ref 9 (A search control technique which Parti-Game uses) Refs 9, 10 ====================================================================== Presenter: Richard Yee, (yee@cs.umass.edu) Title: Building Abstractions to Accelerate Weak Learners Abstract: Learning methods based on dynamic programming (DP) are promising approaches to the problem of controlling dynamical systems. Practical DP-based learning will require function approximation methods that are well-suited for learning optimal value functions, which map system states into numeric estimates of utility. Such approximation problems are generally characterized by non-stationary, dependent training data and, in many cases, little prospect for incorporating strong {\em a priori\/} learning biases. Consequently. this talk considers learning approaches that begin weakly (e.g., using rote memorization) but strengthen their learning biases as experiences accrue. Abstracting from stored experiences should accelerate learning by improving generalization. Bootstrapping such abstraction processes (cf.\ "hypothesis boosting") might be a practical means for scaling DP-based learning across a wide variety of applications. (Refs 1, 2, 3, 4) ===================================================================== Session 2: Where are the new ideas coming from? When: Saturday, Dec 5, 4:30-6:30 p.m. ===================================================================== ===================================================================== Keynote: Andrew G. Barto (barto@cs.umass.edu) Title: Reinforcement Learning Theory Although reinforcement learning is being studied more widely than ever before, especially methods based on approximating dynamic programming (DP), its theoretical foundations are not yet highly developed. In this talk, I discuss what I percieve to be the current state and the missing links in this theory. This topic raises such questions as the following: Just what is DP-based reinforcement learning from a mathematical perspective? What is the relationship between DP-based reinforcement learning and other methods for approximating DP? What theoretical justification exists for combining function approximation methods (such as artificial neural networks) with DP-based learning? What kinds of problems are best suited to DP-based reinforcement learning? Is theory important? ===================================================================== Presenter: Dean Pomerleau Title: Combining artificial neural networks and symbolic processing for autonomous robot guidance Artificial neural networks are capable of performing the reactive aspects of autonomous driving, such as staying on the road and avoiding obstacles. This talk describes an efficient technique for training individual networks to perform these reactive driving tasks. But driving requires more than a collection of isolated capabilities. To achieve true autonomy, a system must determine which capabilities should be employed in the current situation to achieve its objectives. Such goal directed behavior is difficult to implement in an entirely connectionist system. This talk describes a rule-based technique for combining multiple artificial neural networks with map-based symbolic reasoning to achieve high level behaviors. The resulting system is not only able to stay on the road, it is able follow a route to a predetermined destination, turning appropriately at intersections and stopping when it has reached its goal. (Refs 11, 12, 13, 14, 15) ===================================================================== ===================================================================== References ===================================================================== ===================================================================== (#1) Yee, Richard, "Abstraction in Control Learning", Department of Computer and Information Science, University of Massachusetts, Amherst, MA 01003, COINS Technical Report 92-16, March 1992. anonymous ftp: envy.cs.umass.edu:pub/yee.abstrn.ps.Z (#2) Barto, Andrew G. and Richard S. Sutton and Christopher J. C. H. Watkins, Sequential decision problems and neural networks, in Advances in Neural Information Processing Systems 2, 1990, Touretzky, D. S., ed. (#3) Barto, Andrew G. and Richard S. Sutton and Christopher J. C. H. Watkins", Learning and Sequential Decision Making, in Learning and Computational Neuroscience: Foundations of Adaptive Networks, 1990. anonymous ftp: archive.cis.ohio-state.edu:pub/neuroprose/barto.sequential_decisions.ps.Z (#4) Barto, Andrew G. and Steven J. Bradtke and Satinder Pal Singh, Real-time learning and control using asynchronous dynamic programming, Computer and Information Science, University of Massachusetts, Amherst, MA 01003, COINS Technical Report TR-91-57, August 1991. anonymous ftp: archive.cis.ohio-state.edu:pub/neuroprose/barto.realtime-dp.ps.Z (#5) Singh, S.P.," Transfer of Learning by Composing Solutions for Elemental Sequential Tasks, Machine Learning, 8:(3/4):323-339, May 1992. anonymous ftp: envy.cs.umass.edu:pub/singh-compose.ps.Z (#6) Singh, S.P., "Scaling reinforcement learning algorithms by learning variable temporal resolution models, Proceedings of the Ninth Machine Learning Conference, D. Sleeman and P. Edwards, eds., July 1992. anonymous ftp: envy.cs.umass.edu:pub/singh-scaling.ps.Z (#7) S. M. Omohundro, Efficient Algorithms with Neural Network Behaviour, Journal of Complex Systems, Vol 1, No 2, pp 273-347, 1987. (#8) A. W. Moore, Variable Resolution Dynamic Programming: Efficiently Learning Action Maps in Multivariate Real-valued State-spaces, in "Machine Learning: Proceedings of the Eighth International Workshop", edited by Birnbaum, L. and Collins, G., published by Morgan Kaufman. June 1991. (#9) A. W. Moore and C. G. Atkeson, Memory-based Reinforcement Learning: Converging with Less Data and Less Real Time, 1992. See the NIPS92 talk or else preprints available by request to awm@ai.mit.edu (#10) J. Peng and R. J. Williams, Efficient Search Control in Dyna, College of Computer Science, Northeastern University, March, 1992 (#11) Pomerleau, D.A., Gowdy, J., Thorpe, C.E. (1991) Combining artificial neural networks and symbolic processing for autonomous robot guidance. In {\it Engineering Applications of Artificial Intelligence, 4:4} pp. 279-285. (#12) Pomerleau, D.A. (1991) Efficient Training of Artificial Neural Networks for Autonomous Navigation. In {\it Neural Computation 3:1} pp. 88-97. (#13) Touretzky, D.S., Pomerleau, D.A. (1989) What's hidden in the hidden units? {\it BYTE 14(8)}, pp. 227-233. (#14) Pomerleau, D.A. (1991) Rapidly Adapting Artificial Neural Networks for Autonomous Navigation. In {\it Advances in Neural Information Processing Systems 3}, R.P. Lippmann, J.E. Moody, and D.S. Touretzky (ed.), Morgan Kaufmann, pp. 429-435. (#15) Pomerleau, D.A. (1989) ALVINN: An Autonomous Land Vehicle In a Neural Network. In {\it Advances in Neural Information Processing Systems 1}, D.S. Touretzky (ed.), Morgan Kaufmann, pp. 305-313. ------------------------------ End of Neuron Digest [Volume 10 Issue 20] *****************************************