From BairdLC%DFCS%USAFA@dfssmail.usafa.af.mil Mon May 1 04:42:40 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Mon, 1 May 95 04:42:37 -0500; AA07967 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Mon, 1 May 95 04:42:32 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id OAA03037; Mon, 1 May 1995 14:47:18 +0800 Message-Id: <199505010647.OAA03037@cs.uwa.oz.au> From: BairdLC%DFCS%USAFA@dfssmail.usafa.af.mil To: reinforce@cs.uwa.oz.au Cc: malcolmr@cse.unsw.edu.au Subject: re:Drawing the boxes? Date: Sun, 30 Apr 95 13:57:07 MDT Malcom malcolmr@cse.unsw.edu.au wrote: > I'm doing a thesis (Honours) on getting a six-legged robot to learn > to walk, using reinforcement learning, and I have run up against a wall. I > have read a reasonable number of papers on RL, and in almost all of them I > have met with the assumption that the state space for the problem is small > or can be easily divided into a ssmall number of "boxes". A number of people have done research on RL with function approximation systems that are more general than a lookup table, and so don't have boxes. The best result has probably been Tesauro's TD-Gammon program that learned to play backgammon with a simple backprop net. Watkin's thesis on Q-learning used a CMAC rather than boxes for a simple problem. These systems aren't guaranteed to converge, but it is possible to modify the algorithms to guarantee convergence with any neural network (see the Machine Learning paper in http://kirk.usafa.af.mil/~baird). Overall, it appears wise to use neural networks rather than lookup tables and boxes. I hope that's of some help. Leemon Baird baird@cs.usafa.af.mil http://kirk.usafa.af.mil/~baird From hpan@ecn.purdue.edu Tue May 2 06:53:03 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 06:53:00 -0500; AA04143 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 06:52:58 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa09145; 1 May 95 20:57:31 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa09143; 1 May 95 20:38:13 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa17870; 1 May 95 20:38:04 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa04174; 1 May 95 15:34:33 EDT Received: from en.ecn.purdue.edu by RI.CMU.EDU id aa19521; 1 May 95 15:33:02 EDT Received: from en.ecn.purdue.edu (hpan@localhost) by en.ecn.purdue.edu (8.6.12/3.6davy) id OAA23341; Mon, 1 May 1995 14:32:52 -0500 Message-Id: <199505011932.OAA23341@en.ecn.purdue.edu> Date: Mon, 1 May 1995 14:32:52 -0500 From: Hong Pan To: Connectionists@cs.cmu.edu Subject: TR aval: Linsker's Network: Qualitative Analysis On Parameter Space *************** PLEASE DO NOT FORWARD TO OTHER BBOARDS ***************** FTP-host: archive.cis.ohio-state.edu Mode: binary FTP-filename: /pub/neuroprose/pan.purdue-tr-ee-95-12.ps.Z URL: file://archive.cis.ohio-state.edu/pub/neuroprose/ ------------------------------------------------------------------------ The following Technical Report concerning the dynamical mechanism of a class of network models that use the limiter function (or the piecewise linear sigmoidal function) as the constraint limiting the size of the weight or the state variables, has been placed in the Neuroprose archive (see above for FTP-host) and is currently available as a compressed postscript file named pan.purdue-tr-ee-95-12.ps.Z (65 pages with 5 tables & 18 figures) Comments, questions and suggestions about the work can be sent to: hpan@ecn.purdue.edu ***** Hardcopies cannot be provided ***** ------------------------------------------------------------------------ Linsker-type Hebbian Learning: A Qualitative Analysis On The Parameter Space Jianfeng Feng Hong Pan Vwani P. Roychowdhury Mathematisches Institut School of Electrical Engineering Universit\"{a}t M\"{u}nchen 1285 Electrical Engineering Building Theresienstr. 39 Purdue University D-80333 M\"{u}nchen West Lafayette Germany IN 47907-1285 ------------------------------------------------------------------------ Abstract: >From the perspective of nonlinear analysis, we propose a novel rigorous approach for the analysis of the Linsker's unsupervised Hebbian learning network. The behavior of this model is determined by the underlying nonlinear dynamics that are parameterized by a set of parameters originating from the Hebbian rule and the arbor density of the synapses. These parameters determine the presence or absence of a specific receptive field (also referred to as a connection pattern) as a saturated fixed point attractor of the model. In this paper, we perform a qualitative analysis of the underlying nonlinear dynamics over the parameter space, determine the effects of the system parameters on the emergence of various receptive fields, and provide a rigorous criterion for the parameter regime in which the network will have the potential to develop a specially designated connection pattern. In particular, this approach analytically demonstrates, for the first time, the crucial role played by the synaptic arbor density. For example, our analytic predictions indicate that no structured connection pattern can emerge in a Linsker's network that is fully feedforward connected without localized synaptic arbor density. Our general theorems lead to a complete and precise picture of the parameter space that defines the relationships between the different sets of system parameters and the corresponding fixed point attractors, and yield a method to predict whether a given connection pattern will emerge under a given set of parameters without running a numerical simulation of the model. The theoretical results are corroborated by our examples (including center- surround and certain oriented receptive fields), and match key observations reported in Linsker's numerical simulation. The rigorous approach presented here provides a unified treatment of many diverse problems about the dynamical mechanism of a class of models that use the limiter function (also referred to as the piecewise linear sigmoidal function) as the constraint limiting the size of the weight or the state variables, and applies not only to the Linsker's network but also to other learning or retrieval models of this class. ------------------------------------------------------------------------ Key Words: Unsupervised Hebbian learning, Network self-organization, Linsker's developmental model, Brain-State-in-a-Box model, Ontogenesis of primary visual system, Afferent receptive field, Synaptic arbor density, Correlations, Limiter function, Nonlinear dynamics, Qualitative analysis, Parameter space, Coexistence of attractors, Fixed point, Stability. ------------------------------------------------------------------------ Contents: {1} Introduction {1.1} Formulation Of The Linsker's Developmental Model {1.2} Qualitative Analysis Of Nonlinear System And Afferent Receptive Fields {1.3} Summary Of Our Approach {2} General Theorems About Fixed Points And Their Stability {3} The Criterion For The Division Of Parameter Regimes For The Occurrence Of Attractors {3.1} The Necessary And Sufficient Condition For The Emergence Of Afferent Receptive Fields {3.2} The General Principal Parameter Regimes {4} The Afferent Receptive Fields In The First Three Layers {4.1} Description Of The First Three Layers Of The Linsker's Network {4.2} Development Of Connections Between Layers A And B {4.3} Analytic Studies Of Synaptic Density Functions' Influences In The First Three Layers {4.4} Examples Of Structured Afferent Receptive Fields Between Layers B And C {5} Concluding Remarks {5.1} Synaptic Arbor Density Function {5.2} The Linsker's Network And The Brain-State-in-a-Box Model {5.3} Dynamics With Limiter Function {5.4} Intralayer Interaction And Biological Discussion References Appendix A: On the Continuous Version of the Linsker's Model Appendix B: Examples of Structured Afferent Receptive Fields between Layers B and C of the Linsker's Network ------------------------------------------------------------------------ FTP Instructions: unix> ftp archive.cis.ohio-state.edu login: anonymous password: (your e-mail address) ftp> cd pub/neuroprose ftp> binary ftp> get pan.purdue-tr-ee-95-12.ps.Z ftp> quit unix> uncompress pan.purdue-tr-ee-95-12.ps.Z unix> ghostview pan.purdue-tr-ee-95-12.ps (or however you view or print) *************** PLEASE DO NOT FORWARD TO OTHER BBOARDS ***************** From cyril@psychvax.psych.su.OZ.AU Tue May 2 07:39:05 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 07:39:02 -0500; AA05382 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 07:38:59 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa09455; 2 May 95 1:59:39 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa09453; 2 May 95 1:47:15 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18110; 2 May 95 1:47:08 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa09579; 1 May 95 21:56:23 EDT Received: from psychvax.psych.su.OZ.AU by CS.CMU.EDU id aa02229; 1 May 95 21:55:18 EDT Received: from [129.78.83.170] by psychvax.psych.su.oz.au (8.6.7/Ultrix4.3-C) id LAA09480; Tue, 2 May 1995 11:54:03 +1000 Date: Tue, 2 May 1995 11:54:03 +1000 X-Sender: cyril@psychvax.psych.su.oz.au Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" To: Connectionists@cs.cmu.edu From: Cyril Latimer Subject: Modelling Symmetry Detection with Back-propagation Networks The following paper appeared in Spatial Vision, and reprints may be requested from the address given below. Latimer, C.R., Joung, W., & Stevens, C.J. Modelling symmetry detection with back-propagation networks. Spatial Vision, 1994, 8(4), 415-431. Abstract This paper reports experimental data and results of network simulations in a project on symmetry detection in small 6 x 6 binary patterns. Patterns were symmetrical about the vertical, horizontal, positive-oblique or negative-oblique axis, and were viewed on a computer screen. Encouraged to react quickly and accurately, subjects indicated axis of symmetry by pressing one of four designated keys. Detection times and errors were recorded. Back-propagation networks were trained to categorize the patterns on the basis of axis of symmetry, and, by employing cascaded activation functions on their output units, it was possible to compare network performance with subjects' detection times. Best correspondence between simulated and human detection-time functions was observed after the networks had been given significantly more training on patterns symmetrical about the vertical and the horizontal axes. In comparison with no pre-training and pre-training with asymmetric patterns, pre-training networks with sets of single vertical, horizontal, positive-oblique or negative-oblique bars speeded subsequent learning of symmetrical patterns. Results are discussed within the context of theories suggesting that faster detection of symmetries about the vertical and horizontal axes may be due to significantly more early experience with stimuli oriented on these axes. ------------------------------- * ---------------------------------- Dr. Cyril R. Latimer Ph: +61 2 351-2481 Department of Psychology * * Fax: +61 2 351-2603 University of Sydney * NSW 2006, Australia email: cyril@psych.su.oz.au ------------------------------ * ----------------------------------- From markey@dendrite.cs.colorado.edu Tue May 2 22:23:32 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:23:30 -0500; AA15519 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:27 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa09476; 2 May 95 2:11:09 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa09457; 2 May 95 1:48:53 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18117; 2 May 95 1:48:31 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa11610; 2 May 95 1:40:55 EDT Received: from dendrite.cs.Colorado.EDU by RI.CMU.EDU id aa21445; 2 May 95 1:40:08 EDT Received: (from markey@localhost) by dendrite.cs.colorado.edu (8.6.12/8.6.12) id XAA15632; Mon, 1 May 1995 23:40:03 -0600 Date: Mon, 1 May 1995 23:40:03 -0600 From: Kevin Markey Message-Id: <199505020540.XAA15632@dendrite.cs.colorado.edu> To: Connectionists@cs.cmu.edu Subject: Thesis/TR: Sensorimotor foundations of phonology -- a model. Cc: markey@dendrite.cs.colorado.edu FTP-host: archive.cis.ohio-state.edu FTP-filename: /pub/neuroprose/Thesis/markey.thesis.ps.Z Ph.D. Thesis available by anonymous ftp (128 pages) The Sensorimotor Foundations of Phonology: A Computational Model of Early Childhood Articulatory and Phonetic Development Kevin L. Markey Department of Computer Science University of Colorado at Boulder ABSTRACT This thesis describes HABLAR, a computational model of the sensorimotor foundations of early childhood phonological development. HABLAR is intended to replicate the major milestones of emerging speech and demonstrate key characteristics of normal development, including the phonetic characteristics of babble, systematic and context-sensitive patterns of sound substitutions and deletions, overgeneralization errors, and the emergence of adult phonemic organization. HABLAR simulates a complete sensorimotor system consisting of an auditory system that detects and categorizes speech sounds using only acoustic cues drawn from its linguistic environment, an articulatory system that generates synthetic speech based on a realistic computer model of the vocal tract, and a hierarchical cognitive architecture that bridges the two. The environment in which the model resides is also simulated. The model is an autonomous agent which actively experiments within this environment. The principal hypothesis guiding the model is that phonological development emerges from the interaction of auditory perception and hierarchical motor control. The model's auditory perception is specialized to segment and categorize acoustic signals into discrete phonetic events which closely correspond to discrete sets of functionally coordinated gestures learned by the model's articulatory control apparatus. HABLAR learns the correspondence between discrete phonetic and articulatory events, not between continuous speech and continuous vocal tract motion. HABLAR's perceptual and motor organization is initially syllabic. Phonemes are not built into the model but emerge (along with an adult-like phonological organization) due to the differentiation of early syllable-sized motor patterns into phoneme-sized patterns while the model learns a large lexicon. Learning occurs in two phases. In the first phase, HABLAR's auditory perception employs soft competitive learning to acquire phonetic features which categorize the spectral properties of utterances in the linguistic environment. In the second phase, reinforcement based on the phonetic proximity of target and actual utterances guides learning by the model's two levels of motor control. The phonological control level uses Q-learning to learn an optimal policy linking phonetic and articulatory events. The articulatory control level employs a parallel Q-learning architecture to learn a policy which controls the vocal tract's twelve degrees-of-freedom. HABLAR has been fully implemented as a computational model. Simulations of the model's auditory perception demonstrate that it faithfully preserves and makes explicit phonetic properties of the acoustic signal. Auditory simulations also mimic categorical vowel and consonant perception which develops in human infancy. Other results demonstrate the feasibility of learning multi-dimensional articulatory control with a parallel reinforcement learning architecture, and the effectiveness of shaping motor control with reinforcement based on the phonetic proximity of target and actual utterances. The model provides qualitative accounts of developmental data. It is predicted to make pronunciation errors similar to those observed among children because of the relative articulatory difficulty of its producing different speech sounds, its tendency to eliminate the biggest phonetic errors first, its generalization of already mastered sounds across phonetic similarities, and contextual effects of phonetic representations and internal distributed representations which underlie speech production. ----------------------------------------------------------------------------- Sorry, hard copies are not available. Thanks to Jordan Pollack for maintaining neuroprose. Kevin L. Markey Department of Psychology 2155 S. Race Street University of Denver Denver, CO 80208 markey@cs.colorado.edu ------------------------------------------------------------------------------ From jhoh@vision.postech.ac.kr Tue May 2 22:23:43 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:23:41 -0500; AA15527 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:36 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa10280; 2 May 95 16:33:06 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10274; 2 May 95 16:00:20 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18595; 2 May 95 15:57:35 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa18725; 2 May 95 11:20:41 EDT Received: from vision.postech.ac.kr by RI.CMU.EDU id aa22971; 2 May 95 11:19:57 EDT Received: from [141.223.40.5] (tico.postech.ac.kr [141.223.40.5]) by vision.postech.ac.kr (8.6.9H1/8.6.6) with SMTP id AAA03178; Wed, 3 May 1995 00:09:49 +0900 Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Wed, 3 May 1995 00:24:56 +0900 To: colt@cs.uiuc.edu From: "Prof. Jong-Hoon Oh" Subject: Post-doc Position, Statisitical Physics of Neural Networks Cc: Connectionists@cs.cmu.edu Postoctoral Position at POHANG UNIVERSITY OF SCIENCE AND TECHNOLOGY "Statistical Physics of Neural Networks" A post doctorial position is available at the Basic Science Research Institute of Pohang Institute of Science and technology. Main research area will be statistical mechanics of neural networks. Background in statistical physics of neural networks, spin glasses or other condensed matter systems is prefered, but someone with a strong theoretical or computational physics background who is willing to explore this exciting new field can also be considered for this position. Current research is mainly concentrated to statisitical physics of learning in the multi-layered neural networks, including issues such as generalization, storage capacity, population learning, model selection. Now we are extending our research area to the biological neural networks and time series prediction. We have computing facilities such as two parallel computers and several high-end workstations. We hope the successful applicant can start to work either in June or in September, but we have some flexibility. We will support him/her for a year, and it can be extened for one more year according to his/her performance. Further information can be asked through e-mail. An applicant should send a CV and a list of publications to the following address, and arrange two recommendation letters (or at least one from Ph. D. adviser) to be arrived before May 15. CV in TeX/LaTeX format by e-mail is welcome. We prefer e-mail communication. Recommendation letters can also be sent by e-mail. Prof. Jong-Hoon OH Department of Physics jhoh@vision.postech.ac.kr Pohang Institute of Science Technology Tel) +82-562-2792069 Hyoja San 31 Pohang, 790-784 Kyoungbuk, Korea Fax) +82-562-2793099 **************************************************************************** Jong-Hoon Oh Associate Professor, Department of Physics jhoh@vision.postech.ac.kr Pohang Institute of Science Technology Tel) +82-562-2792069 Hyoja San 31 Pohang, 790-784 Kyoungbuk, Korea Fax) +82-562-2793099 **************************************************************************** From PREFENES@lbs.lon.ac.uk Tue May 2 22:23:47 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:23:36 -0500; AA15525 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:32 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa10270; 2 May 95 16:18:55 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10265; 2 May 95 15:58:17 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18582; 2 May 95 15:56:04 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa15104; 2 May 95 6:27:09 EDT Received: from [163.119.254.3] by EDRC.CMU.EDU id aa00949; 2 May 95 6:26:28 EDT Received: from charon.lbs.lon.ac.uk (charon.lbs.lon.ac.uk [163.119.254.8]) by pluto.lbs.lon.ac.uk (8.6.9/8.6.9) with SMTP id LAA01811; Tue, 2 May 1995 11:10:28 +0100 Received: From TERRA/WORKQUEUE by charon.lbs.lon.ac.uk via Charon-4.0A-VROOM with IPX id 100.950502110439.576; 02 May 95 11:06:19 -0100 Message-Id: From: Paul Refenes Organization: London Business School To: connectionists@cs.cmu.edu Date: Tue, 2 May 1995 11:04:59 BST Subject: Doctoral Research Scholarships Cc: nonlin-l@list.nih.gov, csemlist%hasara11.BITNET@earn-relay.ac.uk, corryfee%hasara11.BITNET@earn-relay.ac.uk, reinforce@cs.uwa.edu.au, gannout@cs.iastate.edu, ml@ics.uci.edu Priority: normal X-Mailer: Pegasus Mail/Windows (v1.22) Collaborative PhD Research Scholarships Department of Decision Science London Business School University of London The Department of Decision Science at London Business School is offering three scholarships on its Doctoral programme. Commencing in October 1995 the research areas will include Neural Networks, Non-parametric statistics, Financial Engineering, Simulation, Optimisation and Decision Analysis. Principled Model Selection for Neural Network Applications in Nonlinear Time Series: to utilise developments from multinomial,times series theory and from the non-parametric statistics field for developing distribution theories, statistical diagnostics, and test procedures for recurrent neural network model identification. The methodology will be used to develop models of nonlinear cointegration in equity markets and in telecommunications data. Advanced Decision Technology in Financial Risk Management: The use of advanced decision technologies such as neural networks, non parametric statistics and genetic algorithms for the development of financial risk management models in the currency and soft commodity markets. Our industrial collaborator has special interest on robust neural network models for hedging and arbitrage strategies in the currency, soft commodity and equity markets. Intelligent systems in Industry Modelling and Simulation Environments: the use of simulation for the development of business strategy and the facilitation of executive debate is now well established and popular. Neural network technology will be used for the development of "intelligent simulation agents" that can process the vast amount of data generated by the simulations and adapt their behaviour by learning from the feed back patterns. London Business School offers students enrolled in the doctoral programme core courses on Research Methodology, Statistical Analysis, as well as a choice of advanced specialised subject area courses including Financial Economics, Equity Investment, Derivatives Research, etc. Candidates with a strong background in mathematics, oprerations research, computer science, nonparametric statistics, and/or econometrics who wish to apply are invited to write with a copy of their CV to: Professor D. Bunn or Dr A-P. N. Refenes London Business School Regents Park, London NW1 4SA tel: ++ 44 171 262 5050 fax: ++ 44 171 728 78 75 The Department =========== The Department of Decision Sciences of the London Business School is actively involved in innovative multi-disciplinary research on the application of new business modelling methodologies to individual and organisation decision- making. In seeking to extend the effectiveness of conventional methods of management science, statistical methods and decision support systems, with the latest generation of software platforms, artificial intelligence, neural networks, genetic algorithms and computationally intensive methods, the research themes of the department remain at the forefront of new practice. The NeuroForecasting Research Unit ================================== The NeuroForecasting Research Unit at London Business School is the major centre in Europe for research into neural networks, non-parametric statistics and financial engineering. With funding from the DTI, the European Commission and a consortium of leading financial institutions the research unit has attained a world-wide reputation for collaborative research. Doctoral students work in a team of highly motivated post-doctoral fellows, research fellows, doctoral students and faculty who are amongst Europe's leading authorities in the field. Advanced Decision Support Platforms =================================== The current trend in the design of decision support is towards a synthesis of multiple approaches and integration of business modelling techniques (optimisation with simulation, forecasting with decision analysis, etc. Using object-oriented software architectures, the group has developed innovative approaches for model structuring, strategic analysis, forecasting and decision- analytic procedures. Several companies and the ESRC are currently supporting this work. From kak@gate.ee.lsu.edu Tue May 2 22:23:53 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:23:48 -0500; AA15540 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:45 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab10296; 2 May 95 16:46:35 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10286; 2 May 95 16:25:35 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18622; 2 May 95 16:24:54 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa22507; 2 May 95 14:50:56 EDT Received: from gate.ee.lsu.edu by CS.CMU.EDU id aa08700; 2 May 95 14:49:53 EDT Received: by gate.ee.lsu.edu (4.1/SMI-4.1) id AA19752; Tue, 2 May 95 13:48:44 CDT Date: Tue, 2 May 95 13:48:44 CDT From: Subhash Kak Message-Id: <9505021848.AA19752@gate.ee.lsu.edu> To: connectionists@cs.cmu.edu Subject: Paper The following paper is available by anonymous ftp. Comments on the paper are most welcome. INFORMATION, PHYSICS AND COMPUTATION Subhash C. Kak Louisiana State University Baton Rouge, LA 70803-5901 Abstract: The paper presents several observations on the connections between information, physics and computation. This includes energy and computing speed and the question of quantum computing in the style of Feynman and others. Technical Report ECE-95-04, April 19, 1995 --------- ftp://gate.ee.lsu.edu/pub/kak/inf.ps.Z From maggini@McCulloch.Ing.UniFI.IT Tue May 2 22:23:54 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:23:44 -0500; AA15534 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:40 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa10296; 2 May 95 16:44:00 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10284; 2 May 95 16:24:38 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18615; 2 May 95 16:24:00 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa20406; 2 May 95 12:49:21 EDT Received: from ingfi1.ing.unifi.it by CS.CMU.EDU id aa07359; 2 May 95 12:47:43 EDT Received: from McCulloch.Ing.UniFI.IT by INGFI1.ING.UNIFI.IT with SMTP; Tue, 2 May 1995 18:46:41 +0300 (MET-DST) Received: by McCulloch.Ing.UniFI.IT (5.x/SMI-SVR4) id AA03087; Tue, 2 May 1995 18:42:38 +0200 Date: Tue, 2 May 1995 18:42:38 +0200 From: Marco Maggini Message-Id: <9505021642.AA03087@McCulloch.Ing.UniFI.IT> Organization: DSI - University of Florence (Italy) To: Connectionists@cs.cmu.edu Subject: Neurap 95 WWW page Reply-To: marco@McCulloch.Ing.UniFI.IT X-Sun-Charset: ISO-8859-1 NEURAP'95 8th International Conference on Neural Networks and their Applications Marseilles - December 13-14-15, 1995 France First announcement and call for papers WWW: http://www-dsi.ing.unifi.it/neural/neurap/neurap.html SCOPE OF THE CONFERENCE Following the previous conferences in Nimes, in 1995 the eighth Neural Networks and their Applications Conference will be organized in Marseilles, France. The attendance is unique in its kind composed half by industrial engineers and half by university scientists, coming from all over the world. The purpose of the NEURAP conference is to present the latest results in the application of artificial neural networks. Theoretical aspects of Artificial Neural Networks are to be presented at the ESANN (European Symposium on Artificial Neural Networks) conference. This edition will give a particular place, but not exclusively, to the three following application domains: * Automation * Robotics * Electrical Engineering. To this end, leading international researchers in these domains have been added to the scientific committee. The program committee of NEURAP'95 welcomes papers covering any kind of applications, methods, techniques, or tools that help to understand or develop neural networks applications. To help the prospective authors, the following is a non exhaustive list of topics which will be covered: * Speech or image recognition * Fault tolerance * Data or sensor fusion * Process control * Forecasting * Classification * Knowledge acquisition * Planning * Methods or tools for evaluating neural networks performance * Preprocessing of data * Simulation tools (research, education, development) * Hybrid systems (fuzzy, genetic algorithms, symbolic representation, etc.) * etc. ... The conference will be held in Marseilles, second largest city in France. Due to the proximity of the Mediterrannean sea, winter is usually sunny and temperate. Marseilles is well served by airways and railways, and is connected to the major European cities. CALL FOR CONTRIBUTIONS Prospective authors are invited to submit six originals of their contribution (full paper) before June 15, 1995. The proceedings will be publish in English. Papers should not exceed eight A4 pages (double columms, including figures and references). Printing area will be 17 x 23.5 cm (centered on the A4 pages); left, right, top and bottom margins will thus respectively be 1.9, 1.9, 2.5 and 3.4 cm. 10-point Times font will be used for the main text; headings will be in bold characters (but not underlined), and will be separate from the main text by two blank lines before and one after. Manuscripts prepared in this format will be reproduced in the same size in the book. Originals of the figures will be pasted into the manuscript and centered between the margins. The lettering of the figures should be in 10-point Times font size. Figures should be numbered. The legends also should be centered between the margins and be written in 9-point Times font size. The pages of the manuscript will not be numbered (numbering decided by the editor). A separate page (not included in the manuscript) will indicate: * the title of the manuscript * author(s) name(s) * the complete address (including phone & fax numbers and E-mail) of the corresponding author * a list of five keywords or topics On the same page, the authors will copy and sign the following paragraph: "in case of acceptation of the paper for presentation at NEURAP'95: * at least one of the authors will register to the conference and will present the paper * the author(s) give their rights up over the paper to the organizers of NEURAP'95, for the proceedings and any publication that could directly be generated by the conference * if the paper does not match the format requirements for the proceedings, the author(s) will send a revised version within two weeks of the notification of acceptation." Presentations will be oral or poster, depending on the wish of theauthor(s) and, also, of organisation constraints. 20 minutes will be allowed for oral presentation. Each poster will be allowed an oral presentation of 3 minutes at the beginning of the poster presentation. The full paper of either oral or poster presentation will be published in the proceedings. REGISTRATION FEES (indicative) Registration before Registration after October 1st, 1995 October 1st, 1995 Students 1000 FF 1200 FF Unversities 1600 FF 1800 FF Industries 2000 FF 2300 FF An "advanced registration form" is available by writing to the conference secretariat (see reply form below). Please ask for this form in order to benefit from the reduced registration fee before October 1st, 1995. DEADLINES Submission of papers June 15, 1995 Notification of acceptance September 18, 1995 Conference December 13-14-15, 1995 CONFERENCE SECRETARIAT Dr. Claude TOUZET IUSPIM Email: diam_ct@vmesa11.u-3mrs.fr Domaine Universitaire de Saint-Jrme Phone: +33 91 05 60 60 F-13397 Marseille Cedex 20 (France) Fax: +33 91 05 60 09 REPLY FORM If you wish to receive the final program of NEURAP'95, for any address change, or to add one of your colleagues in our database, please send this form to the conference secretariat. Please indicate if you wish to receive the advanced registration form. Please return this form under stamped envelope to: NEURAP'95 IUSPIM Domaine Universitaire de Saint-Jrme Avenue Escadrille Normandie-Niemen F-13397 Marseille Cedex 20 France - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Name: ............................................................ First Name: ...................................................... University or Comany: ............................................ Address: ......................................................... .................................................................. ZIP: ....................... Town: .............................. Country: ......................................................... Tel: ............................................................. Fax: ............................................................. E-mail: .......................................................... [ ] Please send me the "advanced registration form". - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - SCIENTIFIC COMMITEE (to be confirmed) Jeanny Herault INPG (Grenoble, F) - President Karl Goser Universitt Dortmund (D) - President Bernard Amy IMAG (Grenoble, F) Xavier Arreguit CSEM (CH) Jacob Bahren CESAR (Oak Ridge, USA) Gaston Baudat Sodeco (Genve, CH) Jean-Marie Bernassau Sanofi Recherche (Montpellier, F) Pierre Bessire IMAG/LIFIA (Grenoble, F) Jean Bigeon INPG (Grenoble, F) Giacomo Bisio Universit di Genova (I) Franois Blayo SAMOS - Univ. Paris I (F) Jean Bourjault Universit de Besanon (F) Paul Bourret Onera-Cert (Toulouse, F) Joan Cabestany UPC (Barcelone, E) Leon O. Chua University of California (USA) Mauricio Cirrincione Universita di Palermo (I) Ian Cloete University of Stellenbosch (South Africa) Daniel Collobert CNET (lannion, F) Philippe Coiffet CRIIF (Gif sur Yvette, F) Marie Cottrell SAMOS - Universit Paris I (F) Alexandru Cristea Institut of Virology (Bucharest, Romania) Dante Del Corso Politecnico di Torino (I) Marc Duranton LEP (Limeil-Brvannes, F) Franoise Fogelman Sligos (Clamart, F) Kunihiko Fukushima Osaka University (J) Patrick Gallinari Univ. Pierre et Marie Curie (Paris, F) Josef Gppert University of Tbingen (D) Marita Gordon CEA (Grenoble, F) Marco Gori Universita di Firenze (I) Erwin Groospietsch GMD (Sankt Augustin, D) Martin Hasler EPFL (Lausanne, CH) Jean-Paul Haton Crin- inria (Nancy, F) Jaap Hoekstra Delft University of Technology (NL) Yujiro Inouye Osaka University (Japan) Masumi Ishikawa Kyushu Institute of Technology (J) Christian Jutten INPG (Grenoble, F) Heinrich Klar Technische Universitt Berlin (D) Jean-Franois Lavignon DRET (Arcueil, F) John Lazzaro Univ. of California (Berkeley, USA) Vincent Lorquet ITMI (Grenoble, F) Daniel Memmi CNRS/LIMSI (Orsay, F) Ruy Milidiu University of Rio (Bresil) Pietro Morasso University of Genoa (I) Fabien Moutarde Alcatel Alsthom Recherche (F) Alan F. Murray University of Edinburgh (GB) Akira Namatame National Defence Academy (J) Josef A. Nossek Technische Univ. Mnchen (D) Erkki Oja Lappeenranta Univ. of Tech. (FIN) Stanislaw Osowski University of Warsaw (Poland) Carsten Peterson University of Lund (S) Alberto Prieto Universidad de Granada (E) Pierre Puget CEA (Grenoble, F) Ulrich Ramacher Technische Universitt Dresden (D) Leornardo Reyneri Universita di Pisa (I) Tamas Roska MTA-SZTAKI (Budapest, H) Jean-Claude Sabonnadiere INPG (Grenoble, F) Juan Miguel Santos University of Buenos Aires (Argentina) Leslie S. Smith University of Stirling (GB) John T. Taylor University College London (GB) Carme Torras Institut de Cibernetica/CSIC (E) Claude Touzet DIAM/IUSPIM (Marseille, F) Michel Verleysen UCL (Louvain-La-Neuve, B) Eric Vittoz CSEM (Neuchtel, CH) Alexandre Wallyn CGInn (Boulogne-Billancourt, F) ORGANIZING COMMITTEE Norbert Giambiasi DIAM/IUSPIM - President Jean-Claude Bertrand IUSPIM Claudia Frydman DIAM/IUSPIM J.-Franois Lemaitre IIRIAM Danielle Bertrand IUSPIM From Alex.Monaghan@CompApp.DCU.IE Tue May 2 22:23:59 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:23:57 -0500; AA15556 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:53 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ad10296; 2 May 95 16:51:55 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10294; 2 May 95 16:28:52 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18646; 2 May 95 16:28:20 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa17417; 2 May 95 10:14:07 EDT Received: from Janitor.CompApp.DCU.IE by CS.CMU.EDU id aa05715; 2 May 95 10:13:34 EDT Received: by janitor.compapp.dcu.ie (4.1/SMI-4.1) id AA21392; Tue, 2 May 95 15:13:32 BST Date: Tue, 2 May 95 15:13:32 BST Message-Id: <9505021413.AA21392@janitor.compapp.dcu.ie> From: Alex.Monaghan@CompApp.DCU.IE To: connectionists@cs.cmu.edu Subject: CSNLP Conference at Dublin City University PLEASE POST! PLEASE POST! PLEASE POST! PLEASE POST! PLEASE POST! Call for Participation in the Fourth International Conference on The COGNITIVE SCIENCE of NATURAL LANGUAGE PROCESSING Dublin City University, 5-7 July 1995 Theme: The Role of Syntax There is currently considerable debate regarding the place and importance of syntax in NLP. Papers dealing with this matter will feature strongly in the programme. Invited Speakers: The following speakers have agreed to give keynote talks: Mark Steedman, University of Pennsylvania Alison Henry, University of Ulster Other areas addressed will include: Machine Translation Connectionism Semantic inferencing Spoken dialogue Prosody Hybrid approaches Assessment tools and methods This is a small conference, limited to about 40 delegates. We aim to keep things relatively informal, and to promote discussion and debate. With two dozen contributed papers and two invited talks, all outstanding, there should be plenty of material to interest a wide range of researchers. Registration and Accommodation: The registration fee will be IR#60, and will include proceedings, lunches and one evening meal. Accommodation can be reserved in the campus residences at DCU. A single room is IR#16 per night, with full Irish breakfast an additional IR#4. Accommodation will be "First come, first served": there is a heavy demand for campus rooms in the summer. There are also several hotels and B&B establishments nearby: addresses will be provided on request. To register, contact Alex Monaghan at the addresses given below. Payment in advance is possible but not obligatory. Please state gender (for accommodation purposes) and any unusual dietary requirements. CSNLP Alex Monaghan School of Computer Applications Dublin City University Dublin 9 Ireland Email registrations are preferred, please mail alex@compapp.dcu.ie (internet) --------- Deadlines: 26th June --- Final date for registration, accommodation, meals etc. A provisional programme will be sent out in due course. From B344DSL@utarlg.uta.edu Tue May 2 22:24:06 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:24:03 -0500; AA15564 Message-Id: <9505030323.AA03921@lucy.cs.wisc.edu> Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:58 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa10422; 2 May 95 19:36:41 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10420; 2 May 95 19:24:50 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18728; 2 May 95 19:24:07 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa26760; 2 May 95 18:28:28 EDT Received: from acs.uta.edu by CS.CMU.EDU id aa10915; 2 May 95 18:27:30 EDT Received: from utarlg.uta.edu by utarlg.uta.edu (PMDF V4.3-10 #8453) id <01HQ1AEJO7SG004BFP@utarlg.uta.edu>; Tue, 02 May 1995 17:22:23 -0500 (CDT) Date: Tue, 02 May 1995 17:22:22 -0500 (CDT) From: B344DSL@utarlg.uta.edu Subject: Tentative program for MIND meeting at Texas A&M To: connectionists@cs.cmu.edu Conference on Neural Networks for Novel High-Order Rule Formation Sponsored by Metroplex Institute for Neural Dynamics (MIND) and For a New Social Science (NSS) Forum Theatre, Rudder Theatre Complex, Texas A&M University, May 20-21, 1995 Tentative Schedule and List of Abstracts Saturday, May 20 ~ 4:30 - 5:30 PM Karl Pribram, Radford University Brain, Values, and Creativity Sunday, May 21 ~ 9:00 - 10:00 John Taylor, University of London Building the Mind from Neural Networks 10:00 - 10:45 Daniel Levine, University of Texas at Arlington The Prefrontal Cortex and Rule Formation 10:45 - 11:00 Break 11:00 - 11:45 Sam Leven, For a New Social Science Synesthesia and S.A.M.: Modeling Creative Process 11:45 - 12:15 Richard Long, University of Central Florida A Computational Model of Emotion Based Learning: Variation and Selection of Attractors in ANNs 12:15 - 1:45 Lunch 1:45 - 2:30 Ron Sun, University of Alabama An Agent Architecture with On-line Learning of Conceptual and Subconceptual Knowledge 2:30 - 3:00 Madhav Moganti, University of Missouri, Rolla Generation of FAM Rules Using DCL Network in PCB Inspection 3:30 - 3:45 Break 3:45 - 4:30 Ramkrishna Prakash, University of Houston Towards Neural Bases of Cognitive Functions: Sensorimotor Intelligence 4:30 - 5:15 Risto Miikkulainen, University of Texas Learning and Performing Sequential Decision Tasks Through Symbiotic Evolution of Neural Networks 5:15 - 5:45 Richard Filer, University of York Correlation Matrix Memory in Rule-based Reasoning and Combinatorial Rule Match Posters (to be up continuously): Risto Miikkulainen, University of Texas Parsing Embedded Structures with Subsymbolic Neural Networks Haejung Paik and Caren Marzban, University of Oklahoma Predicting Television Extreme Viewers and Nonviewers: A Neural Network Analysis Haejung Paik, University of Oklahoma Television Viewing and Mathematics Achievement Doug Warner, University of New Mexico Modeling of an Air Combat Expert: The Relevance of Context Abstracts for talks: Pribram Perturbation, internally or externally generated, produces an orienting reaction which interrupts ongoing behavior and demarcates an episode. As the orienting reaction habituates, the weightings (values) of polarizations of the junctional microprocess become (re)structured on the basis of protocritic processing. Temporary stability characterizes the new structure which acts as a reinforcing attractor for the duration of the episode, i.e., until dishabituation (another orienting reaction) occurs. Habituation leads to extinction and under suitable conditions an extinguished experience can become reactivated, i.e., made relevant. Innovation depends on such reactivation and is enhanced not only by adding randomness to the process, but also by adding structured variety produced by prior experience. Taylor After a description of a global approach to the mind, the manner in which various modules in the brain can contribute will be explored, and related to the European Human Brain Project and to developments stemming from non-invasive instruments and single neuron measurements. A possible neural model of the mind will be presented, with suggestions outlined as to how it could be it could be tested. Levine Familiar modeling principles (e.g., Hebbian or associative learning, lateral inhibition, opponent processing, neuromodulation) could recur, in different combinations, in architectures that can learn diverse rules. These rules include, for example: go to the most novel object; alternate between two given objects; touch three given objects, without repeats, in any order. Frontal lobe damage interferes with learning all three of those rules. Hence, network models of rule learning and encoding should include a module analogous to the prefrontal cortex. They should also include modules analogous to the hippocampus, for episode setting, and the amygdala, for emotional evaluation. Through its connections with the parietal association cortex, with secondary cortical areas for individual sensory modalities, and with supplementary motor and premotor cortices, the dorsolateral part of the prefrontal cortex contains representations of movement sequences the animal has performed or thought about performing. Through connections to the amygdala via the orbital prefrontal cortex (which seems to be extensively and diffusely connected to the dorsolateral part), selective enhancement occurs of those motor sequence representations which have led to reward. I propose that the prefrontal cortex also contains "coincidence detectors" which respond to commonalities in any spatial or temporal attributes among all those reinforced representations. Such coincidence detection is a prelude to generating rules and thereby making inferences about classes of possible future movements. This general prefrontal function includes the function of tying together working memory representations that has been ascribed to it by other modelers (Kimberg & Farah, 1993) but goes beyond it. It also encompasses the ability to generate new rules, in coordination with the hippocampus, if current rules prove to be unsatisfactory. Leven (To be added) Long (with Leon Hardy) We propose a novel neural network architecture which is based on a broader theory of learning and cognitive self-organization. The model is designed to be loosely based on the mammalian brain's limbic and cortical neurophysiology and which possesses a number of unique and useful properties. This architecture uses a variation and selection algorithm similar to those found in evolutionary programming (EP), and genetic algorithms (GA). In this case, however, selection does not operate on bit strings, or even neuronal weights; instead, variation and selection acts on attractors in a dynamical system. Furthermore, hierarchical processing is imposed on a single neuronal layer in a manner that is easily scalable by simply adding additional nodes. This is accomplished using a large, uniform-intensity input signal that sweeps across a neural layer. This "sweeping activation" alternately pushes nodes into their active threshold regime, thus turning them "on". In this way, the active portion of the network settles into an attractor, becoming the preprocessed "input" to the newly recruited nodes. The attractor neural network (ANN) which forms the basis of this system is similar to a Hopfield neural network in that it has the same node update rule and is asynchronous, but differs from a traditional Hopfield network in two ways. First, unlike a fully connected Hopfield network, we use a sparse connection scheme using a random walk or gaussian distribution. Second, we allow for positive-weighted self connections which dramatically improves attractor stability when negative or inhibitory weights are allowed. This model is derived from a more general theory of emotion and emotion- based learning in the mammalian brain. The theory postulates that negative and positive emotion is synonymous with variation and selection respectively. The theory further classifies various emotions according to their role in learning, and so makes predictions as to the functions of various brain regions and their interconnections. Sun In developing autonomous agents, we usually emphasize only the procedural and situated knowledge, ignoring generic and declarative knowledge that is more explicit and more widely applicable. On the other hand, in developing AI symbolic reasoning models, we usually emphasize only the declarative and context-free knowledge. In order to develop versatile cognitive agents that learn in situated contexts and generalize resulting knowledge to different environments, we explore the possibility of learning both declarative and procedural knowledge in a hybrid connectionist architecture. The architecture is based on the two-level idea proposed earlier by the author. Declarative knowledge is represented conceptually, while procedural knowledge is represented subconceptually. The architecture integrates embodied reactions, rules, learning, and decision-making in a unified framework, and structures different learning components (including Q-learning and rule induction) in a synergistic way to perform on-line and integrated learning. Moganti Many vision problems are solved using knowledge-based approaches. The conventional knowledge-based systems use domain experts to generate the initial rules and their membership functions, and then by trial and error refine the rules and membership functions to optimize the final system's performance. However, it would be difficult for human experts to examine all the input-output data in complex vision applications to find and tune the rules and functions within the system. In this presentation, the speaker introduces the application of fuzzy logic in complex computer vision applications. It will be shown that neural networks could be effectively used in the estimation of fuzzy rules, thus making the knowledge acquisition simple, robust, and complete. As an example application, the problem of visual inspection of defects in printed circuit boards (PCBs) will be presented. The speaker will present the work carried out by him where the inspection problem is characterized as a pattern classification problem. The process involves a two-level classification of the printed circuit board image sub-patterns into either a non- defective class or a defective class. The PCB sub-patterns are checked for geometric shape and dimensional verification using fuzzy information extracted from the scan-line grid with an adaptive fuzzy data algorithm that uses differential competitive learning (DCL) in updating winning synaptic vectors. The fuzzy feature vectors drastically reduce the conventional inspection systems. The presentation concludes with experimental results showing the superiority of the approach. It will be shown that the basic method presented is by no means limited to the PCB inspection application. The model can easily be extended to other vision problems like silicon wafer inspection, automatic target recognition (ATR) systems, etc. Prakash (with Haluk Ogmen) A developmental neural network model was proposed (Ogmen, 1992, 1995) that ties higher level cognitive functions to lower level sensorimotor intelligence through stage transitions and the decalage vertical" (Piaget, 1975). Our neural representation of a sensorimotor reflex comprises of sensory, motor, and affective elements. The affective elements establish an internal organization: The primary affective and secondary affective elements dictate the totality and the relationship aspects of the organization, respectively. In order to study sensorimotor intelligence in detail the network was elaborated for the sucking and rooting reflexes. During the first two sub-stages of the sensorimotor stage, as proposed by Piaget (1952), assimilation predominates over accommodation. We will present simulations of recognitory and functional assimilations in the sucking reflex, and reciprocal assimilation between the sucking and rooting reflexes. We will then consider possible subcortical neural substrates for our sensorimotor model of the rooting reflex in order to bring the model closer to neurophysiology. The subcortical areas believed to be involved in the rooting reflex are the spinal trigeminal nuclei which receive facial somatosensory afferents and the cervical motor neurons that control the neck muscles. Neurons in these regions are proposed to correspond to the sensory and motor elements of our model, respectively. The reticular formation which receives and sends projections to these two regions and which receives inputs from visceral regions is a good candidate for the loci of the affective elements of our model. In this talk, we will discuss these three areas and their mapping to our model in further detail. Miikkulainen A new approach called SANE (Symbiotic, Adaptive Neuro-Evolution) for learning and performing sequential decision tasks is presented. In SANE, a population of neurons is evolved through genetic algorithms to form a neural network for the given task. Compared to problem-general heuristics, SANE forms more effective decision strategies because it learns to utilize domain-specific information. Applications of SANE to controlling the inverted pendulum, performing value ordering in constraint satisfaction search, and focusing minimax search in game playing will be described and compared to traditional methods. Filer (with James Austin) This abstract is taken from a paper that presents Correlation Matrix Memory, a form of binary associative neural network, and the potential of using this technology in expert systems. The particular focus of this paper is on a comparison with an existing database technique used for achieving partial match, Multi-level Superimposed Coding (Kim & Lee, 1992), and how using Correlation Matrix Memory (CMM) enables very efficient rule matching, and a combinatorial rule match in linear time. We achieve this utilising a comparatively simple network approach, which has obvious implications for advancing models of reasoning in the brain. Rule-based reasoning has been the subject of a lot of work in AI, and some expert systems have proved very useful, e.g., PROSPECTOR (Gaschnig, 1980) and DENDRAL (Lindsay et al., 1980), but it is clear that the usefulness of an expert system is not necessarily the result of a particular architecture. We suggest that efficient partial match is a fundamental requirement, and combinatorial pattern match is a facility that is directly related to dealing with partial information, but a brute force approach invariably takes combinatorial time to do this. Combinatorial match we take to mean the ability to answer a sub-maximally specified query that should succeed if a subset of these attributes match (i.e., specify A attributes and a number N, N s A, and the query succeeds if any N of A match). This sort of match is fundamental, not only in knowledge-based reasoning, but also in (vision) occlusion analysis. Touretzky and Hinton (1988) were the first to emulate a symbolic, rule-based system in a connectionist architecture. A connectionist approach held the promise of better performance with partial information and being generally less brittle. Whether or not this is the case, Touretzky and Hinton usefully demonstrated that connectionist networks are capable of symbolic reasoning. This paper describes CMM, which maintains a truly distributed knowledge representation, and the use of CMM as an inference engine (Austin, 1994). This paper is concerned with some very useful properties; we believe we have shown a fundamental link between database technology and an artificial neural network technology that has parallels in neurobiology. Abstracts for posters: Miikkulainen A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case-role representations, and keeping track of the recursive embeddings into different modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures, but to novel structures as well. SPEC exhibits plausible memory degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high-level process rather than one based on automatic reflex responses. Paik and Marzban In an attempt to better understand the attributes of the "average" viewer, an analysis of the data characterizing television nonviewers and extreme viewers is performed. The data is taken from the 1988, 1989, and 1990 General Social Surveys (GSS), conducted by the National Opinion Research Center (NORC). Given the assumption-free, model-independent representation that a neural network can offer, we perform such an analysis and discuss the significance of the findings. For comparison, a linear discriminant analysis is also performed, and is shown to be outperformed by the neural network. Furthermore, the set of demographic variables are identified as the strongest predictor of nonviewers, and the combination of family-related and social activity-related variables as the strongest attribute of extreme viewers. Paik This study examines the correlation between mathematics achievement and television viewing, and explores the underlying processes. The data consists of 13,542 high school seniors from the first wave of the High School and Beyond project, conducted by the National Opinion Research Center on behalf of the National Center for Education Statistics. A neural network is employed for the analysis; unlike methods employed in prior studies, with no a priori assumptions about the underlying model or the distributions of the data, the neural network yields a correlation impervious to errors or inaccuracies arising from possibly violated assumptions. A curvilinear relationship is found, independent of viewer characteristics, parental background, parental involvement, and leisure activities, with a maximum at about one hour of viewing, and persistent upon the inclusion of statistical errors. The choice of mathematics performance as the measure of achievement elevates the found curvilinearity to a content-independent status, because of the lack of television programs dealing with high school senior mathematics. It is further shown that the curvilinearity is replaced with an entirely positive correlation across all hours of television viewing, for lower ability students. A host of intervening variables, and their contributions to the process, are examined. It is shown that the process, and especially the component with a positive correlation, involves only cortical stimulations brought about by the formal features of television programs. Warner A modeling approach was used to investigate the theorized connection between expertise and context. Using the domain of air-combat maneuvering, an expert was modeled both with and without respect to context. Neural networks were used for each condition. One network used a simple multi-layer perceptron with inputs for five consecutive time segments from the data as well as a quantitative descriptor for context in this domain. The comparison used a set of networks with identical structure to the first network. The same data were provided to each condition, however the data were divided by context before being provided to separate networks for the comparison. It was discovered, after training and generalization testing on all networks, that the comparison condition using context-explicit networks performed better for strict definitions of offensive context. This distinction implies the use of context in an air-combat task by the expert human pilot. Simulating problems using a standard model and comparing it against the same model incorporating hypothesized explicit divisions within the data should prove to be a useful tool in psychology. Transportation and Hotel Information Texas A&M is in College Station, TX, about 1.5 to 2 hours NW of Houston and NE of Austin. Bryan/College Station Airport (Easterwood) is only about five minutes from the conference site, and is served by American (American Eagle), Continental, and Delta (ASA). The College Station Hilton (409-693-7500) has a block of rooms reserved for the Creative Concepts Conference (of which MIND is a satellite) at $60 a night, and a shuttle bus to and from the A&M campus. There are also rooms available at the Memorial Student Union on campus (409-845-8909) on campus for about $40 a night. Other nearby hotels include the Comfort Inn (409-846-7333), Hampton Inn (409-846-0184), LaQuinta (409-696-7777), and Ramada-Aggieland (409-693-9891), all of which have complimentary shuttles to campus. From yorick@dcs.shef.ac.uk Tue May 2 22:24:46 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Tue, 2 May 95 22:23:52 -0500; AA15552 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Tue, 2 May 95 22:23:50 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ac10296; 2 May 95 16:49:11 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa10292; 2 May 95 16:28:00 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18629; 2 May 95 16:25:34 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa22031; 2 May 95 14:19:42 EDT Received: from gw.dcs.shef.ac.uk by EDRC.CMU.EDU id aa02848; 2 May 95 14:17:09 EDT Received: from spock.dcs.shef.ac.uk by dcs.shef.ac.uk (4.1/DAVE-1.0) with SMTP id AA11579; Tue, 2 May 95 18:18:44 BST Date: Tue, 2 May 95 18:18:44 BST From: Yorick Wilks Message-Id: <9505021718.AA11579@dcs.shef.ac.uk> To: Sigart@logkon.arpa, aaai@sumex-aim.stanford.edu, aisb@cogs.sussex.ac.uk, aisb@cogs.susx.ac.uk, alisonw@cogs.susx.ac.uk, arpanet-bboards@mc.lcs.mit.edu, comp-ai-nat-lang@cs.utexas.edu, comp-ai-nlang-know-rep@cs.utexas.edu, comp-ai@cs.utexas.edu, comp-phon@cogsci.ed.ac.uk, comp-phon@uk.ac.ed.cogsci, comp-speech@cs.utexas.edu, comp.nat-lang@ucbvax.berkeley.ed, connectionists@cs.cmu.edu, corpora@hd.uib.no, empiricists@CSLI.Stanford.EDU, epsynet@uhupvm1.bitnet, fj-ai@etl.go.jp, fj-ai@jp.go.etl, gerda@at.ac.univie.ai, humanist@brownvm.brown.edu, ikbsbb@inf.rl.ac.uk, ikbsbb@uk.ac.rl.inf, ir-l@bitnet.uccmvsa, ir-l@uccmvsa.bitnet, jqrqc@cunyvm.cuny.edu, jqrqc@edu.cuny.cunyvm, linguist@edu.tamu.tamvm1, linguist@tamvm1.tamu.edu, llsfonet@uk.ac.rdg.am.cms, ln@bitnet.frmop11, ln@frmop11.bitnet, mantaras@ceab.es, nesca@frlim51.bitnet, nick@zermatt.lcs.mit.edu, nl-kr@cs.rochester.edu, nl-kr@cs.rpi.edu, nnsc@nnsc.nsf.net, robert@at.ac.univie.ai, salt@cstr.ed.ac.uk, sigart@vaxa.isi.edu, steve5@cluster.middlesex.ac.uk, stuart@vax.ox.ac.uk, welty@cs.rpi.edu Subject: Research in CS, AI, NLP and Speech University of Sheffield, UK Department of Computer Science RESEARCH DEGREES IN COMPUTER SCIENCE ************************************ This department intends to recruit a number of postgraduate research students to commence studies in October 1995. Successful applicants will be registered for an M.Phil or Ph.D. The department has four research groups, with interests as follows: Formal Methods and Software Engineering --------------------------------------- Telematics, Formal Specification, Verification and Testing, Object-Oriented Languages and Design, Proof Theory. Parallel Processing ------------------- Parallel Database Machines, Parallel CASE Tools, Safety-Critical systems. Artificial Intelligence and Neural Networks ------------------------------------------- Natural Language Processing (including corpus and lexically based methods, information extraction and pragmatics), Neural Networks, Computer Graphics, Intelligent Tutoring Systems, Computer Argumentation. Speech and Hearing ------------------ Auditory Scene Analysis, Models of Auditory Perception, Automatic Speech Recognition. It is expected that a number of (British Government) EPSRC awards will be available to UK residents, in addition to the University's own studentship and bursary schemes, some of which are open to all. Candidates for these awards should have a good honours degree in a relevant discipline (not necessarily Computer Science), or should attain such a degree by October 1995. Part-time registration is also possible. We especially welcome applications from (non-British) EU citizens elegible for support under the EU's Research Training Grants schemes (with application deadlines in May and September). Application forms and further particulars are available from The Departmental Secretary, Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello St, Sheffield S1 4DP. More details can also be obtained from world-wide-web address http://www.dcs.shef.ac.uk. Informal enquiries may be addressed to Dr. Phil. Green, phone 0114-282-5578, email p.green@dcs.sheffield.ac.uk Prof Yorick Wilks, phone 0114-282-5563, email yorick@dcs.sheffield.ac.uk From PREFENES@lbs.lon.ac.uk Thu May 4 09:26:36 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 4 May 95 09:26:32 -0500; AA04567 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Thu, 4 May 95 09:26:27 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id TAA21518; Thu, 4 May 1995 19:20:24 +0800 Message-Id: <199505041120.TAA21518@cs.uwa.oz.au> From: "Paul Refenes" To: reinforce@cs.uwa.edu.au Subject: Doctoral Research Scholarships [connectionists] Date: Tue, 2 May 1995 11:04:59 BST Collaborative PhD Research Scholarships Department of Decision Science London Business School University of London The Department of Decision Science at London Business School is offering three scholarships on its Doctoral programme. Commencing in October 1995 the research areas will include Neural Networks, Non-parametric statistics, Financial Engineering, Simulation, Optimisation and Decision Analysis. Principled Model Selection for Neural Network Applications in Nonlinear Time Series: to utilise developments from multinomial,times series theory and from the non-parametric statistics field for developing distribution theories, statistical diagnostics, and test procedures for recurrent neural network model identification. The methodology will be used to develop models of nonlinear cointegration in equity markets and in telecommunications data. Advanced Decision Technology in Financial Risk Management: The use of advanced decision technologies such as neural networks, non parametric statistics and genetic algorithms for the development of financial risk management models in the currency and soft commodity markets. Our industrial collaborator has special interest on robust neural network models for hedging and arbitrage strategies in the currency, soft commodity and equity markets. Intelligent systems in Industry Modelling and Simulation Environments: the use of simulation for the development of business strategy and the facilitation of executive debate is now well established and popular. Neural network technology will be used for the development of "intelligent simulation agents" that can process the vast amount of data generated by the simulations and adapt their behaviour by learning from the feed back patterns. London Business School offers students enrolled in the doctoral programme core courses on Research Methodology, Statistical Analysis, as well as a choice of advanced specialised subject area courses including Financial Economics, Equity Investment, Derivatives Research, etc. Candidates with a strong background in mathematics, oprerations research, computer science, nonparametric statistics, and/or econometrics who wish to apply are invited to write with a copy of their CV to: Professor D. Bunn or Dr A-P. N. Refenes London Business School Regents Park, London NW1 4SA tel: ++ 44 171 262 5050 fax: ++ 44 171 728 78 75 The Department =========== The Department of Decision Sciences of the London Business School is actively involved in innovative multi-disciplinary research on the application of new business modelling methodologies to individual and organisation decision- making. In seeking to extend the effectiveness of conventional methods of management science, statistical methods and decision support systems, with the latest generation of software platforms, artificial intelligence, neural networks, genetic algorithms and computationally intensive methods, the research themes of the department remain at the forefront of new practice. The NeuroForecasting Research Unit ================================== The NeuroForecasting Research Unit at London Business School is the major centre in Europe for research into neural networks, non-parametric statistics and financial engineering. With funding from the DTI, the European Commission and a consortium of leading financial institutions the research unit has attained a world-wide reputation for collaborative research. Doctoral students work in a team of highly motivated post-doctoral fellows, research fellows, doctoral students and faculty who are amongst Europe's leading authorities in the field. Advanced Decision Support Platforms =================================== The current trend in the design of decision support is towards a synthesis of multiple approaches and integration of business modelling techniques (optimisation with simulation, forecasting with decision analysis, etc. Using object-oriented software architectures, the group has developed innovative approaches for model structuring, strategic analysis, forecasting and decision- analytic procedures. Several companies and the ESRC are currently supporting this work. From rob@comec4.mh.ua.edu Thu May 4 12:25:55 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 4 May 95 12:25:51 -0500; AA06520 Received: from bilby.cs.uwa.oz.au by lucy.cs.wisc.edu; Thu, 4 May 95 12:25:43 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id TAA21540; Thu, 4 May 1995 19:21:53 +0800 Message-Id: <199505041121.TAA21540@cs.uwa.oz.au> From: Robert Elliott Smith To: reinforce@cs.uwa.edu.au[connectionists] Subject: Papers to be presented at ICGA6 Date: Wed, 03 May 95 08:34:15 -0600 The organizers of the Sixth International Conference on Genetic Algorithms, to be held in Pittsburgh, PA, July 15-19, 1995, are please to present the following list of papers that will be presented at the conference. This list is followed by registration information for the conference. =================== ICGA-95: PAPERS ACCECPTED FOR PRESENTATION SELECTION Generalized Convergence Models for Tournament- and (mu,lambda)-Selection Thomas Baeck A Mathematical Analysis of Tournament Selection Tobias Blickle, Lothar Thiele On Decentralizing Selection Algorithms Kenneth De Jong, Jayshree Sarma Finding Multimodal Solutions Using Restricted Tournament Selection Georges Harik Analysis of Genetic Algorithms Evolution under Pure Selection Filippo Neri, Lorenza Saitta MUTATION AND RECOMBINATION A New Class of Crossover Operators for Numerical Optimization Jaroslaw Arabas, Jan J. Mulawka, Jacek Pokrasniewicz On Multi-Dimensional Encoding/Crossover Thang N. Bui, Byung-Ro Moon On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation Nikolaus Hansen, Andreas Ostermeier, Andreas Gawelczyk The Nature of Mutation in Genetic Algorithms Robert Hinterding, Harry Gielewski, T. C. Peachey Crossover, Macromutation, and Population-based Search Terry Jones What Have You Done for Me Lately? Adapting Operator Probabilities in a Steady-State Genetic Algorithm Bryant A. Julstrom Metabits: Generic Endogenous Crossover Control Jim Levenick Toward More Powerful Recombinations Byung Ro Moon, Andrew B. Kahng Fuzzy Recombination for the Continuous Breeder Genetic Algorithm H.-M. Voigt, H. Muhlenbein, D. Cvetkovic EVOLUTIONARY COMPUTATION TECHNIQUES The Distributed Genetic Algorithm Revisited Theodore C. Belding Solving Constraint Satisfaction Problems Using a Genetic/Systematic Search Hybrid That Realizes When to Quit James Bowen, Gerry Dozier Enhancing GA Performance Through Incest Prohibitions Based on Ancestry Robert Craighurst, Worthy Martin A Comparison of Parallel and Sequential Niching Methods Samir W. Mahfoud Selectively Destructive Re-start Jonathan Maresky, Yuval Davidor, Daniel Gitler, Gad Aharoni, Amnon Barak Genetic Algorithms, Numerical Optimization, and Constraints Zbigniew Michalewicz, Sita S. Raghavan A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization Khim Peow Ng, Kok Cheong Wong When Seduction Meets Selection Edmund Ronald Population-Oriented Simulated Annealing: A Genetic/Thermodynamic Hybrid Approach to Optimization James M. Varanelli, James P. Cohoon FORMAL ANALYSIS OF EVOLUTIONARY COMPUTATION AND PROBLEM DIFFICULTY Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms Terry Jones, Stephanie Forrest Signal-to-noise, Crosstalk and Long Range Problem Difficulty in Genetic Algorithms Hillol Kargupta Efficient Tracing of the Behaviour of Genetic Algorithms using Expected Values of Bit and Walsh Products J.N. Kok, P. Floreen Optimization Using Replicators Anil Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka Epistasis in Genetic Algorithms: An Experimental Design Perspective Colin Reeves, Christine Wright Epistasis in Periodic Programs Stefan Voget Hyperplane Ranking in Simple Genetic Algorithms Darrell Whitley, Keith Mathias, Larry Pyeatt Building Better Test Functions D. Whitley, K. Mathias, S. Rana, J Dzubera GENETIC PROGRAMMING The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming David Andre Causality in Genetic Programming Dana H. Ballard, Justinian Rosca Solving Complex Problems with Genetic Algorithms Bertrand Daniel Dunay, Frederic E. Petry Strongly Typed Genetic Programming in Evolving Cooperation Strategies Thoms Haynes, Roger L. Wainwright, Sandip Sen, Dale A. Schoenefeld Temporal Data Processing Using Genetic Programming Hitoshi Iba, Hugo de Garis, Taisuke Sato Two Ways of Discovering the Size and Shape of a Computer Program to Solve a Problem John R. Koza Evolving Data Structures Using Genetic Programming W.B. Langdon Accurate Replication in Genetic Programming Nicholas Freitag McPhee, Justin Darwin Miller Complexity Compression and Evolution Peter Nordin, Wolfgang Banzhaf Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code Peter Nordin, Wolfgang Banzhaf CO-EVOLUTION AND EMERGENT ORGANIZATION Biological Symbiosis as a Metaphor for Computational Hybridization Jason M. Daida, Steven J. Ross, Brian C. Hannan Evolving Globally Synchronized Cellular Automata Rajarshi Das, James P. Crutchfield, Melanie Mitchell, James E. Hanson The Evolution of Emergent Organization in Immune System Gene Libraries Ron Hightower, Stephanie Forrest, Alan S. Perelson Co-evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces Hugues Juille The Symbiotic Evolution of Solutions and their Representations Jan Paredis A Coevolutionary Approach to Learning Sequential Decision Rules Mitchell A. Potter, Kenneth A. De Jong, John J. Grefenstette Methods for Competitive Co-evolution: Finding Opponents Worth Beating Christopher D. Rosin, Richard K. Belew EVOLUTIONARY COMPUTATION IN COMBINATION WITH MACHINE LEARNING OR NEURAL NETS Evolution in Multi-agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrapedal Robot Lawrence Bull, Terrence C. Fogarty Adaptive Distributed Routing using Evolutionary Fuzzy Control Brian Carse, Terry Fogarty, Alistair Munro Relational Schemata: A Way to Improve the Expressiveness of Classifiers Philippe Collard, Cathy Escazut The Mating Pool: A Testbed for Experiments in the Evolution of Symbol Systems Lawrence Davis, David Orvosh Genetic Algorithm Enlarges the Capacity of Associative Memory Akira Imada, Keijiro Araki A Genetic Algorithm for Optimizing Fuzzy Decision Trees Cezary Z. Janikow PLEASE: A Prototype Learning System using Genetic Algorithms Leslie Knight, Sandip Sen A Parallel Genetic Algorithm for Concept Learning Filippo Neri, Attilio Giordana Evolutionary Grown Semi-Weighted Neural Networks Steve G. Romaniuk Combining Genetic Algorithms with Memory Based Reasoning John W. Sheppard, Steven L. Salzberg Cellular Encoding Applied to Neurocontrol Darrell Whitley, Frederic Gruau, Larry Pyeatt EVOLUTIONARY COMPUTATION APPLICATIONS I Determining Factorization: A New Encoding Scheme for Spanning Trees Applied to the Probabilistic Minimum Spanning Tree Problem Faris N. Abuali, Roger L. Wainwright, Dale A. Schoenefeld A Hybrid Genetic Algorithm for the Maximum Clique Problem Thang Nguyen Bui, Paul H. Eppley Finding (Near-)Optimal Steiner Trees in Large Graphs Henrik Esbensen Solving Equal Piles with the Grouping Genetic Algorithm Emanuel Falkenauer A Study of Genetic Algorithm Hybrids for Facility Layout Problems Kazuhiro Kado, Dave Corne, Peter Ross An Efficient Genetic Algorithm for Job Shop Scheduling Problems Shigenobu Kobayashi, Isao Ono, Masayuki Yamamura A Comparative Study of Genetic Search Kihong Park Inference of Stochastic Regular Grammars by Massively Parallel Genetic Algorithms Markus Schwehm, Alexander Ost Genetic Algorithm Approach to the Search for Golomb Rulers Stephen W. Soliday, Abdollah Homaifar, Gary L. Lebby An Adaptive Clustering Method using a Geometric Shape for Vehicle Routing Problems with Time Windows Sam R. Thangiah EVOLUTIONARY COMPUTATION APPLICATIONS II Applying Genetic Algorithms to Outlier Detection Kelly D. Crawford, Roger L. Wainwright Design of Statistical Quality Control Procedures Using Genetic Algorithms Aristides T. Hatjimihail, Theophanes T. Hatjimihail A Segregated Genetic Algorithm for Constrained Structural Optimization R. Le Riche, C. Knopf-Lenoir, R.T. Haftka A Preliminary Study of Genetic Data Compression Wee K. Ng A Standard GA Approach to Native Protein Conformation Prediction Arnold L. Patton, W. F. Punch, III, E. D. Goodman Using GAs to Characterize Workloads Chrisila C. Pettey, Thomas D. Wagner, Lawrence W. Dowdy Development of the Genetic Function Approximation Algorithm David Rogers A Parallel Genetic Algorithm for Multi-objective Microprocessor Design Timothy J. Stanley, Trevor Mudge A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems Rupert Weare, Edmund Burke, Dave Ellilman Evolutionary Computation in Air Traffic Control Planning C.H.M. van Kemenade, C.F.W. Hendriks, J.N. Kok Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses Frank Vavak, Terence C. Fogarty, Philip Cheng ========= Registration Information: 6TH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS July 15-19, 1995 University of Pittsburgh Pittsburgh, Pennsylvania, USA CONFERENCE COMMITTEE Stephen F. Smith, Chair Carnegie Mellon University Peter J. Angeline, Finance Loral Federal Systems Larry J. Eshelman, Program Philips Laboratories Terry Fogarty, Tutorials University of the West of England, Bristol Alan C. Schultz, Workshops Naval Research Laboratory Alice E. Smith, Local Arrangements University of Pittsburgh Robert E. Smith, Publicity University of Alabama The 6th International Conference on Genetic Algorithms (ICGA-95) brings together an international community from academia, government, and industry interested in algorithms suggested by the evolutionary process of natural selection, and will include pre-conference tutorials, invited speakers, and workshops. Topics will include: genetic algorithms and classifier systems, evolution strategies, and other forms of evolutionary computation; machine learning and optimization using these methods, their relations to other learning paradigms (e.g., neural networks and simulated annealing), and mathematical descriptions of their behavior. The conference host for 1995 will be the University of Pittsburgh located in Pittsburgh, Pennsylvania. The conference will begin Saturday afternoon, July 15, for those who plan on attending the tutorials. A reception is planned for Saturday evening. The conference meeting will begin Sunday morning July 16 and end Wednesday afternoon, July 19. The complete conference program and schedule will be sent later to those who register. TUTORIALS ICGA-95 will begin with three parallel sessions of tutorials on Saturday. Conference attendees may attend up to three tutorials (one from each session) for a supplementary fee (see registration form). Tutorial Session I 11:00 a.m.-12:30 p.m. I.A Introduction to Genetic Algorithms Melanie Mitchell - A brief history of Evolutionary Computation. The appeal of evolution. Search spaces and fitness landscapes. Elements of Genetic Algorithms. A Simple GA. GAs versus traditional search methods. Overview of GA applications. Brief case studies of GAs applied to: the Prisoner's Dilemma, Sorting Networks, Neural Networks, and Cellular Automata. How and why do GAs work? I.B Application of Genetic Algorithms Lawrence Davis - There are hundreds of real-world applications of genetic algorithms, and a considerable body of engineering expertise has grown up as a result. This tutorial will describe many of those principles, and present case studies demonstrating their use. I.C Genetics-Based Machine Learning Robert Smith - This tutorial discusses rule-based, neural, and fuzzy techniques that utilize GAs for exploration in the context reinforcement learning control. A rule-based technique, the learning classifier system (LCS), is shown to be analogous to a neural network. The integration of fuzzy logic into the LCS is also discussed. Research issues related to GA-based learning are overviewed. The application potential for genetics-based machine learning is discussed. Tutorial Session II 1:30-3:00 p.m. II.A Basic Genetic Algorithm Theory Darrell Whitley - Hyperplane Partitions and the Schema Theorem. Binary and Nonbinary Representations; Gray coding, Static hyperplane averages, Dynamic hyperplane averages and Deception, the K-armed bandit analogy and Hyperplane ranking. II.B Basic Genetic Programming John Koza - Genetic Programming is an extension of the genetic algorithm in which populations of computer programs are evolved to solve problems. The tutorial explains how crossover is done on program trees and illustrates how the user goes about applying genetic programming to various problems of different types from different fields. Multi-part programs and automatically defined functions are briefly introduced. II.C Evolutionary Programming David Fogel - Evolutionary programming, which originated in the early 1960s, has recently been successfully applied to difficult, diverse real-world problems. This tutorial will provide information on the history, theory, and practice of evolutionary programming. Case-studies and comparisons will be presented. Tutorial Session III 3:30-5:00 p.m. III.A Advanced Genetic Algorithm Theory Darrell Whitley - Exact Non-Markov models of simple genetic algorithms. Markov models of simple genetic algorithms. The Schema Theorem and Price's Theorem. Convergence Proofs, Exact Non-Markov models for permutation based representations. III.B Advanced Genetic Programming John Koza - The emphasis is on evolving multi-part programs containing reusable automatically defined functions in order to exploit the regularities of problem environments. ADFs may improve performance, improve parsimony, and provide scalability. Recursive ADFs, iteration-performing branches, various types of memories (including indexed memory and mental models), architecturally diverse populations, and point typing are explained. III.C Evolution Strategies Hans-Paul Schwefel and Thomas Baeck - Evolution Strategies in the context of their historical origin for optimization in Berlin in the 1960s. Comparison of the computer-versions (1+1) and (10,100) ES with classical optimum seeking methods for parameter optimization. Formal descriptions of ES. Global convergence conditions. Time efficiency in some simple situations. The role of recombination. Auto-adaptation of internal models of the environment. Multi-criteria optimization. Parallel versions. Short list of application examples. GETTING TO PITTSBURGH The Pittsburgh International Airport is served by most of the major airlines. Information on transportation from the airport and directions to the University of Pittsburgh campus, will be sent along with your conference registration confirmation letter. LODGING University Holiday Inn, 100 Lytton Avenue two blocks from convention site $92/day (single) $9 /day parking charge pool (indoor), exercise facilities Reserve by June 18. Call 412-682-6200. Hampton Inn, 3315 Hamlet Street 12 blocks from convention site $72/day (single) free parking, breakfast, and one-way airport transportation Reserve by July 1. Call 412-681-1000. Howard Johnson's, 3401 Boulevard of the Allies 12 blocks from convention site $56/day (single) free parking and Oakland transportation pool (outdoor) Reserve by June 13. Call 412-683-6100. Sutherland Hall (dorm), University Drive-Pitt campus 10 blocks from convention site (steep hill) $30/day, single no amenities (phone, TV, etc.) shared bathroom Reserve by July 1. Call 412-648-1100. CONFERENCE FEES REGISTRATION FEE Registrations received by June 11 are $250 for participants and $100 for students. Registrations received on or after June 12 and walk-in registrations at the conference will be $295 for participants and $125 for students. Included in the registration fee are entry to all technical sessions, several lunches, coffee breaks, reception Saturday evening, conference materials, and conference proceedings. TUTORIALS There is a separate fee for the Saturday tutorial sessions. Attendees may register for up to three tutorials (one from each tutorial session). The fee for one tutorial is $40 for participants and $15 for students; two tutorials, $75 for participants and $25 for students; three tutorials, $110 for participants and $35 for students. The deadline to register without a late fee is June 11. After this date, participants and students will be assessed a flat $20 late fee, whether they register for one, two, or all three tutorials. CONFERENCE BANQUET Not included in the registration fee is the ticket for the banquet. Participants may purchase banquet tickets for an additional $30. Note - Please purchase your banquet tickets nowQyou will be unable to buy them upon arrival. GUEST TICKETS Guest tickets for the Saturday evening reception are $10 each; guest tickets for the conference banquet are $30 each for adults and $10 each for children. Note - Please purchase additional tickets now - you will be unable to buy them upon arrival. CANCELLATION/REFUND POLICY For cancellations received up to and including June 1, a full refund will be given minus a $25 handling fee. FINANCIAL ASSISTANCE FOR STUDENTS With support from the Naval Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, a limited fund has been set aside to assist students with travel expenses. Students should have their advisor certify their student status and that sufficient funds are not available. Students interested in obtaining such assistance should send a letter before May 22 describing their situation and needs to: Peter J. Angeline, c/o Advanced Technologies Dept, Loral Federal Systems, State Route 17C, Mail Drop 0210, Owego, NY 13827-3994 USA. TO REGISTER Early registration is recommended. You may register by mail, fax, or email using a credit card (MasterCard or VISA). You may also pay by check if registering by mail. Note: Students must also send with their registration a photocopy of their valid university student ID or a letter from a professor. Complete the registration form and return with payment. If more than one registrant from the same institution will be attending, make additional copies of the registration form. Mail ICGA 95 Department of Industrial Engineering University of Pittsburgh 1048 Benedum Hall Pittsburgh, PA 15261 USA Fax Fax the registration form to 412-624-9831 Email Receive email form by contacting: icga@engrng.pitt.edu Up-to-date conference information is available on the World Wide Web (WWW) http://www.aic.nrl.navy.mil/galist/icga95/ CALL FOR ICGA '95 WORKSHOP PROPOSALS ICGA workshop proposals are now being solicited. Workshops tend to range from informal sessions to more formal sessions with presentations and working notes. Each accepted workshop will be supplied with space and an overhead projector. VCRs might be available. If you are interested in organizing a workshop, send a workshop title, short description, proposed format, and name of the organizers to the workshop coordinator by April 15, 1995. Alan C. Schultz - schultz@aic.nrl.navy.mil Code 5510, Navy Center for Artificial Intelligence Naval Research Laboratory Washington DC 30375-5337 USA REGISTRATION FORM Prof / Dr / Mr / Ms / Mrs Name ______________________________________________________ Last First MI I would like my name tag to read _____________________________________________ Affiliation/Business ______________________________________________________ Address ______________________________________________________ City ______________________________________________________ State ___________________ Zip ________________________ Country_____________________________________________ Telephone (include area code) Business _______________________________ Home______________________________ FEES (all figures in US dollars) Conference Registration Fee By June 11 ___ participant, $250 ___ student, $100 =$_________ On or after June 12 ___ participant, $295 ___ student, $125 =$_________ July 15 Tutorials Select up to three tutorials, but no more than one tutorial per tutorial session. Tutorial Session I: ___I.A Introduction to Genetic Algorithms ___I.B Application of Genetic Algorithms ___I.C Genetics-Based Machine Learning Tutorial Session II: ___II.A Basic Genetic Algorithm Theory ___II.B Basic Genetic Programming ___II.C Evolutionary Programming Tutorial Session III: ___III.A Advanced Genetic Algorithm Theory ___III.B Advanced Genetic Programming ___III.C Evolution Strategies Tutorial Registration Fee By June 11 ___one tutorial: participant, $40 student, $15 ___two tutorials: participant, $75 student, $25 = $_________ ___three tutorials: participant, $110 student, $35 On or after June 12, participants and students add a $20 late fee for tutorials = $_________ Banquet Ticket (not included in the Registration Fee; no tickets may be purchased upon arrival) participants/adult guest #______ ticket(s) @ $30 = $_________ child #______ ticket(s) @ $10 = $_________ Additional Saturday reception tickets (no tickets may be purchased upon arrival) guest #______ ticket(s) @ $10 = $_________ TOTAL (US dollars) $____________ METHOD OF PAYMENT ___ Check (payable to the University of Pittsburgh, US banks only) ___ MasterCard ___ VISA #__________________________________________ Expiration Date ____________________ Signature of card holder ______________________________________________ Note: Students must submit with their registration a photocopy of their valid student ID or a letter from a professor. Mail ICGA 95, Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall, Pittsburgh, PA 15261 USA Fax 412-624-9831 Email To receive email form: icga@engrng.pitt.edu World Wide Web (WWW) For up-to-date conference information: http://www.aic.nrl.navy.mil/galist/icga95/ ------------------------------------------- Robert Elliott Smith Department of Engineering Science and Mechanics Room 210 Hardaway Hall The University of Alabama Box 870278 Tuscaloosa, Alabama 35487 <> rob@comec4.mh.ua.edu <> (205) 348-1618 <> (205) 348-7240 <> http://hamton.eng.ua.edu/college/home/mh/faculty/rsmith/Web/smith.html ------------------------------------------- From raffaele@caio.irmkant.rm.cnr.it Thu May 4 23:10:59 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 4 May 95 23:10:57 -0500; AA12908 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 4 May 95 23:10:54 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab10270; 2 May 95 16:21:27 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab10265; 2 May 95 15:58:23 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa18589; 2 May 95 15:56:28 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa18014; 2 May 95 10:42:47 EDT Received: from caio.irmkant.rm.cnr.it by CS.CMU.EDU id aa06050; 2 May 95 10:40:49 EDT Received: by caio.irmkant.rm.cnr.it (AIX 3.2/UCB 5.64/4.03) id AA09110; Tue, 2 May 1995 16:44:23 -0500 Date: Tue, 2 May 1995 16:44:23 -0500 From: raffaele@caio.irmkant.rm.cnr.it Message-Id: <9505022144.AA09110@caio.irmkant.rm.cnr.it> To: connectionists@cs.cmu.edu Subject: simulation of protein folding process (paper) FTP-host: kant.irmkant.rm.cnr.it FTP-filename: /pub/econets/calabretta.folding.ps.Z The following paper has been placed in the anonymous-ftp archive (see above for ftp-host) and is now available as a compressed postscript file named calabretta.folding.ps.Z (14 pages of output) The paper is also available by World Wide Web: http://kant.irmkant.rm.cnr.it/gral.html It will appear in Proceedings of 3rd European Conference on Artificial Life (Granada, Spain, 4-6 June 1995). Comments welcome. Raffaele Calabretta email address: raffaele@caio.irmkant.rm.cnr.it ------------------------------------------------------------------ "An Artificial Model for Predicting the Tertiary Structure of Unknown Proteins that Emulates the Folding Process" Raffaele Calabretta, Stefano Nolfi, Domenico Parisi Department of Neural Systems and Artificial Life Institute of Psychology National Research Council V.le Marx, 15 00137 ROME ITALY ---------------------------------------------------------------------------- Abstract: We present an "ab initio" method that tries to determine the tertiary structure of unknown proteins by modelling the folding process without using potentials extracted from known protein structures. We have been able to obtain appropriate matrices of folding potentials, i.e. 'forces' able to drive the folding process to produce correct tertiary structures, using a genetic algorithm. Some initial simulations that try to simulate the folding process of a fragment of the crambin that results in an alpha- helix, have yielded good results. We discuss some general implications of an Artificial Life approach to protein folding which makes an attempt at simulating the actual folding process rather than just trying to predict its final result. ---------------------------------------------------------------------------- From chiva@biologie.ens.fr Thu May 4 23:11:23 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 4 May 95 23:11:22 -0500; AA12914 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 4 May 95 23:11:18 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa11392; 3 May 95 14:51:59 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa11390; 3 May 95 14:30:44 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa19608; 3 May 95 14:30:09 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id ab08416; 3 May 95 10:43:05 EDT Received: from nef.ens.fr by RI.CMU.EDU id aa27374; 3 May 95 10:41:53 EDT Received: from apollon.ens.fr by nef.ens.fr (5.65c8/ULM-1.0) Id AA15523 ; Wed, 3 May 1995 15:54:38 +0200 Date: Wed, 3 May 1995 15:54:37 +0200 From: Emmanuel CHIVA Message-Id: <9505031354.AA12362@apollon.ens.fr> To: Connectionists@cs.cmu.edu Subject: Groupe de BioInformatique WWW Home Page X-Sun-Charset: US-ASCII ** ANNOUNCING ** There is now a homepage for the Groupe de BioInformatique, Ecole Normale Superieure, Paris (France) at the following URL: http://www.ens.fr/bioinfo/www which includes: - the description of our research areas (e.g, the animat approach, NNets, GAs Image Processing and vision, Cell metabolism), complete bibliography (some articles can be retrieved) and personal pages - the Adaptive Behavior journal homepage - the SAB conference homepage - numerous pointers to additional related servers Please send reactions and comments to chiva@wotan.ens.fr ============================================================================ Ecole Normale Superieure | Emmanuel M. Chiva | Departement de Biologie | | Groupe de BioInformatique | Tel: + 33 1 44323633 | 46, rue d'Ulm | Fax: + 33 1 44323901 | 75230 Paris cedex 05 France | email: chiva@wotan.ens.fr | ============================================================================ From sbh@eng.cam.ac.uk Thu May 4 23:11:52 1995 Received: from lucy.cs.wisc.edu by sea.cs.wisc.edu; Thu, 4 May 95 23:11:35 -0500; AA12922 Received: from TELNET-1.SRV.CS.CMU.EDU by lucy.cs.wisc.edu; Thu, 4 May 95 23:11:29 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa12524; 4 May 95 11:21:09 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa12522; 4 May 95 11:03:25 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa20833; 4 May 95 11:02:49 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa24624; 4 May 95 9:50:48 EDT Received: from spanner.eng.cam.ac.uk by RI.CMU.EDU id aa01640; 4 May 95 9:49:51 EDT Received: from club.eng.cam.ac.uk (via sbh@club.eng.cam.ac.uk [129.169.16.2]) by spanner.eng.cam.ac.uk with ESMTP id OAA10308 for ; Thu, 4 May 1995 14:49:42 +0100 From: "S.B. Holden" Message-Id: <199505041349.19030@club.eng.cam.ac.uk> Received: (via sbh@localhost) by club.eng.cam.ac.uk id OAA19030 for Connectionists@cs.cmu.edu; Thu, 4 May 1995 14:49:42 +0100 Subject: New technical report To: Connectionists@cs.cmu.edu Date: Thu, 04 May 1995 14:49:41 BST X-Mailer: Elm [revision: 109.14] The following technical report is available by anonymous ftp from the archive of the Speech, Vision and Robotics Group at the Cambridge University Engineering Department. Average-Case Learning Curves for Radial Basis Function Networks Sean B. Holden and Mahesan Niranjan Technical Report CUED/F-INFENG/TR.212 Cambridge University Engineering Department Trumpington Street Cambridge CB2 1PZ England Abstract The application of statistical physics to the study of the learning curves of feedforward connectionist networks has, to date, been concerned mostly with networks that do not include hidden layers. Recent work has extended the theory to networks such as committee machines and parity machines; however these are not networks that are often used in practice and an important direction for current and future research is the extension of the theory to practical connectionist networks. In this paper we investigate the learning curves of a class of networks that has been widely, and successfully applied to practical problems: the Gaussian radial basis function networks (RBFNs). We address the problem of learning linear and nonlinear, realizable and unrealizable, target rules from noise-free training examples using a stochastic training algorithm. Expressions for the generalization error, defined as the expected error for a network with a given set of parameters, are derived for general Gaussian RBFNs, for which all parameters, including centres and spread parameters, are adaptable. Specializing to the case of RBFNs with fixed basis functions we then study the learning curves for these networks in the limit of high temperature. ************************ How to obtain a copy ************************ a) Via FTP: unix> ftp svr-ftp.eng.cam.ac.uk Name: anonymous Password: (type your email address) ftp> cd reports ftp> binary ftp> get holden_tr212.ps.Z ftp> quit unix> uncompress holden_tr212.ps.Z unix> lpr holden_tr212.ps (or however you print PostScript) b) Via postal mail: Request a hardcopy from Dr. Sean B. Holden Department of Computer Science University College London Gower Street London WC1E 6BT U.K. or email me: s.holden@cs.ucl.ac.uk