Special Issue of MLJ on Unsupervised Learning Correction to UAI '99 Call for Papers New Book on Agents and Learning Graduate Study in Digital Libraries 5th EUROPEAN CONFERENCE ON ARTIFICIAL LIFE -- ECAL99 Hybrid Neural Symbolic Integration CALL FOR INFORMATION ON COURSES IN EVOLUTIONARY COMPUTATION SWARM workshop at Charles Sturt Univ. KBCS-98: Call for Participation NIPS*98 PAAM99 Call for Papers The Machine Learning List is moderated. Contributions should be relevant to the scientific study of machine learning. Mail contributions to ml@ics.uci.edu. Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues may be obtained from http://www.ics.uci.edu/~mlearn ---------------------------------------------------------------------- ----------------------------- From: Douglas H. Fisher [dfisher@vuse.vanderbilt.edu] Subject: Special Issue of MLJ on Unsupervised Learning Date: Sat 11/7/98 9:20 AM Call for Papers Special Issue of Machine Learning on Unsupervised Learning Doug Fisher Special Issue Editor http://cswww.vuse.vanderbilt.edu/~dfisher/mlj-unsup.html Several forms of unsupervised learning extract relationships from data that can be then exploited for inference. The primary unsupervised techniques include clustering, learning (usually Bayesian) belief networks, and learning association rules. The unsupervised "pattern" or "concept" learning methods that are of most interest in this special issue differ from supervised concept learning methods in that there is no single, dependent variable, dimension, or predicate that is the *a priori* focus of inference. Rather, an unsupervised method may support inference along more than one dimension (variable, property), typically many dimensions/properties. Authors are encouraged to submit papers in the primary unsupervised learning paradigms of clustering, belief- network learning, and association-rule learning (and possibly others) for consideration as contributions to the Special Issue on Unsupervised Learning of the journal, Machine Learning. Articles that relate different paradigms are especially welcome. Review Criteria Each submission will be reviewed by two to three reviewers, as well as the Special Issue Editor. Each submission should clearly describe an unsupervised learning algorithm from one of the major unsupervised paradigms mentioned above, or if the learning approach is from some novel paradigm, then it should explain the relationships to one or more of these established paradigms. In addition, the successful submission will describe an inference procedure or other performance task that operates on learned knowledge. Inference of unobserved variable values and/or joint probability distributions are examples of inference tasks. Experimental and/or formal analysis should support the merits of the unsupervised method's ability to improve the performance task(s) along dimensions such as accuracy and cost, though application-oriented contributions (see below) may relax the formality of experimental and theoretical demonstrations if practical gains are documented in the submission. The requirement for well-defined performance and learning procedures does not preclude the possibility of interactive approaches, in which human and machine share learning and inference responsibilities. Cognitive modeling submissions are also possible, where fits to human data are desired. In all cases, submissions should be scholarly, with thorough links to related research, and report methods/results previously unreported in the archival (typically journal, book) literature. However, exceptional surveys, particularly those that cross paradigm boundaries, may be considered at the discretion of the Special Issue Editor and the Executive Editor. Except in extraordinary circumstances, submissions should not exceed 25 pages (journal-formatted pages), excluding references. Research areas Submissions should report novel methods/results, including along any of the following dimensions. pattern and concept representations and presentations, including visualization strategies data representation (e.g., relational data, continuous data, hierarchical data) search strategies (e.g., anytime algorithms, incremental algorithms, optimization strategies, human-machine interactive approaches, parallelism) performance tasks, including complex forms of problem solving analytical and analytical/inductive hybrid approaches cognitive modeling Application areas Submissions may also contribute primarily in terms of application (e.g., data mining in a particular area). Appropriate application-oriented papers will report well-defined, generic learning and performance procedures, which need not be novel relative to the literature, but such papers are expected to describe successful applications of unsupervised methods for a particular application task, and provide informed recommendations on the class of applications for which the reported methods appear appropriate. Careful thought should be given to whether an application-oriented paper can be best treated as a technical note submission (i.e., 10 or fewer journal-formatted pages), by appropriately relying on previous research publications. Of course, submissions with both strong research and application contributions are welcome. Important Dates Jan. 8, 1999 Title, authors, and abstract should be sent electronically to mlj-unsup@vuse.vanderbilt.edu with an intent-to-submit cover page. This is an important deadline that will facilitate reviewing, but minor changes to title, authors, and abstract are acceptable prior to the submission of full papers. Submissions of title/authors/abstract should be in plain text. Feb. 8, 1999 Full submissions should be received by mlj-unsup@vuse.vanderbilt.edu Full submissions should be in postscript format, and may be COMPRESSed if larger than 1M. Other formats may be accepted upon request. Submissions must be in English. April 30, 1999 Decisions sent to authors; papers accepted with no more than minor revisions accepted to the special issue. June 14, 1999 Final versions of accepted papers should be received by the Special Issue editor in the format specified for full submissions, using Kluwer style guidelines.
From: Kathryn Blackmond Laskey [klaskey@gmu.edu]
Subject: Correction to UAI '99 Call for Papers
Date: Wed 11/4/98 5:30 AM
The UAI-99 Call for Papers erroneously stated the following:
"Location TBA (near the site of AAAI-99)".
The correct statement is:
"Location TBA (near the site of IJCAI-99)".
IJCAI-99 will take place in Stockholm, Sweden.
UAI-99 will take place near that location, as correctly stated elsewhere in the Call for Papers.
Kathryn Laskey
From: Gheorghe Tecuci [tecuci@gmu.edu]
Subject: New Book on Agents and Learning
Date: Tue 11/10/98 2:32 PM
New Book:
G.Tecuci, BUILDING INTELLIGENT AGENTS: An Apprenticeship Multistrategy
Learning Theory, Methodology, Tool and Case Studies, Academic Press
Gheorghe Tecuci (George Mason University, http://lalab.gmu.edu/),
BUILDING INTELLIGENT AGENTS: An Apprenticeship Multistrategy
Learning Theory, Methodology, Tool and Case Studies
Academic Press, 1998
ISBN: 0126851255
http://www.apcatalog.com/cgi-bin/AP?ISBN=0126851255&LOCATION;=US&FORM;=FORM2
GENERAL DESCRIPTION
This book presents a theory, methodology and tool for building
intelligent agents, along with detailed case studies. The most
significant, and unique, characteristic of building these agents is that
a person directly teaches them how to perform domain-specific tasks in
much the same way he or she would teach a student or apprentice: by
giving the agent examples and explanations, and by supervising and
correcting its behavior. This approach, in which the agent learns its
behavior from its teacher, integrates many machine learning and
knowledge acquisition techniques, taking advantage of their
complementary strengths to compensate for each other weaknesses. As a
consequence, it significantly reduces the involvement of a knowledge
engineer in the process of building an intelligent agent. The book is
unique in the comprehensive coverage of its subject. The first part of
the book presents an original theory for building intelligent agents and
a methodology and tool that implement the theory. The second part of the
book presents complex and detail case studies of building different
types of agents: an educational assessment agent that enhances the
capability, generality and usefulness of an educational system for
teaching higher-order thinking skills in the context of history; a
statistical analysis assessment and support agent to support a
university-level introductory science course; an engineering design
assistant that cooperates with its user in configuring computer systems;
and a virtual military commander integrated into a distributed
interactice simulation environment.
Preface. Intelligent Agents. General Presentation of the Disciple Approach for Building Intelligent Agents. Knowledge Representation and Reasoning. Knowledge Acquisition and Learning. The Disciple Shell and Methodology. Case Study: Assessment Agent for Higher-Order Thinking Skills in History. Case Study: The Statistical Analysis Assessment and Support Agent. Case Study: Design Assistant for Configuring Computer Systems. Case Study: Virtual Agent for Distributed Interactive Simulations. Selected Bibliography of Machine Learning, Knowledge Acquisition, and Intelligent Agents Research. Notation. Subject Index.
From: Haym Hirsh [hirsh@cs.rutgers.edu]
Subject: Graduate Study in Digital Libraries
Date: Sat 11/7/98 3:02 PM
GRADUATE AWARDS FOR INTERDISCIPLINARY STUDY IN DIGITAL LIBRARIES
The Rutgers University Distributed Laboratory for Digital Libraries
(RDLDL) is pleased to announce the availability of competitive
graduate awards for interdisciplinary doctoral studies in digital
libaries, including contributing technologies and relevant basic
research.
Digital libraries is an exciting new domain for research, and for
society at large, and the RDLDL is taking a leading role in
establishing an explicitly interdisciplinary approach to the variety
of problems in this burgeoning area. Current faculty members of the
RDLDL come from the disciplines of cognitive science, computer
science, library and information science and psychology, and students
in the program are expected to participate in research and to take
courses in two or more of the disciplines represented in the RDLDL.
Successful candidates will participate in digital library-oriented
research projects with several faculty members of the RDLDL, as well
as being enrolled in a Ph.D. program in one of the disciplines
associated with the RDLDL. They will also take part in an
interdisciplinary seminar involving the faculty and all of the other
graduate students associated with the RDLDL.
The RDLDL Awards offer tuition remission and an annual stipend of
$13,000, and the opportunity to achieve an interdisciplinary education
for research in the interdisciplinary field of digital libaries.
These awards are tenable for one year in the first instance, beginning
in September 1999, and are renewable.
For further information, including the specific interests of the RDLDL
faculty, and application materials, please see the RDLDL WWW site,
http://diglib.rutgers.edu/RDLDL
or contact any one of the members of the RDLDL Steering Committee,
listed below.
Steering Committee of the Rutgers Distributed Laboratory for Digital
Libraries:
Nicholas J. Belkin, Department of Library and Information Science,
New Brunswick Campus nick@belkin.rutgers.edu
Benjamin M. Bly, Department of Psychology,
Newark Campus ben@psychology.rutgers.edu
Sven Dickinson, Department of Computer Science,
New Brunswick Campus sven@ruccs.rutgers.edu
Stephen Hanson, Department of Psychology,
Newark Campus jose@tractatus.rutgers.edu
Haym Hirsh, Department of Computer Science,
New Brunswick Campus hirsh@cs.rutgers.edu
Paul Kantor, Department of Library and Information Science,
New Brunswick Campus kantor@scils.rutgers.edu
Zenon Pylyshyn, Rutgers Center for Cognitive Science,
New Brunswick Campus zenon@ruccs.rutgers.edu
To contact us by mail, please write to:
Rutgers Distributed Laboratory for Digital Libraries
c/o School of Communication, Information and Library Studies
Rutgers University
4 Huntington Street
New Brunswick, NJ 08901-1071, USA
From: Dario Floreano [dario.floreano@epfl.ch]
Subject: ECAL99: CFP
Date: Wed 11/4/98 5:38 AM
Conference Announcement and Call for Papers
5th EUROPEAN CONFERENCE ON ARTIFICIAL LIFE
ECAL99
Swiss Federal Institute of Technology in Lausanne (EPFL)
September 13-17 1999
Artificial Life is an interdisciplinary research enterprise aimed at
understanding life-as-it-is and life-as-it-could-be, and at synthetizing
life-like phenomena in chemical, electronic, software, and other artificial
media. Artificial Life redefines the concepts of artificial and natural,
blurring the borders between traditional disciplines and providing new
insights into the origin and principles of life.
In addition to all traditional topics, ECAL99 will encourage contributions
that address the role of embodiment and physical constraints for the
self-organization of life-like systems in chemical, electronic, mechanical,
and other artificial media.
The conference will be single-track and will feature selected oral
presentations, spotlight presentations (short talk + poster), and posters.
All accepted contributions will be published in the proceedings by Springer
Verlag. ECAL99 will also host invited talks, thematic debates,
and public demonstrations. One day of tutorials before the conference will
provide the necessary background to better enjoy the contributions from
different disciplines; participation is strongly recommended.
IMPORTANT DATES
28 February 1999: Submission deadline
30 April 1999: Notification to authors
31 May 1999: Camera-ready version due
13-17 September 1999: Conference dates
Official Language: English
Publisher: Springer-Verlag
WWW: http://www.epfl.ch/ecal99
E-mail: ecal99@epfl.ch
SUBMISSIONS
Papers should not be longer than 10 pages (including figures) in the
Springer-Verlag llncs style (http://www.springer.de/comp/lncs/authors.html)
with an abstract of 100-150 words. All submissions will be reviewed by at
least two referees. Authors should make all possible efforts to state the
relevance of their contribution to the field of Artificial Life and explain
the implications of their results. Novelty, clarity of presentation, and
scientific content will be the main criteria used by referees.
Both electronic and traditional submissions will be accepted (in the latter
case, please send 6 hard-copies). Demonstrations, videos, and proposals
for thematic debates are also welcome.
EXAMPLES OF BROAD AREAS TO BE ADDRESSED (but not limited to):
Self-organization. Chemical origins of life. Autocatalytic systems.
Prebiotic evolution. RNA systems. Evolutionary chemistry. Fitness
landscapes. Natural selection. Artificial evolution. Ecosystem evolution.
Multicellular development. Natural and artificial morphogenesis. Learning
and development. Bio-morphic and neuro-morphic engineering. Artificial
worlds. Simulation tools. Artificial organisms. Synthetic actors. Artificial
(virtual and robotic) humanoids. Intelligent autonomous robots. Evolutionary
Robotics. Applications of Alife technologies. Life detectors. Self-repairing
hardware. Evolvable hardware. Emergent collective behaviors. Swarm
intelligence. Evolution of social behaviors. Evolution of communication.
Epistemology. Artificial Life and Art.
VENUE
The conference will take place on the EPFL campus located by lake Leman,
10 minutes away by metro from the historic centre of Lausanne. The Alps
can be quickly reached by public transportation. Geneva international airport
is 40 minutes by train (more info on ECAL99 web page).
REGISTRATION
ECAL99 will encourage debate, contacts, and interdisciplinary exchanges
in an open-minded and informal atmosphere. Several student fellowships will
be available. Registration fees (starting at 400 CHF) will include almost
everything for both regular and student participants. Special offers on
accomodation. Considerable savings on early registration (before 15 June 1999).
Check out ECAL99 web page frequently http://www.epfl.ch/ecal99
ORGANIZERS: Dario Floreano, Jean-Daniel Nicoud, Francesco Mondada
LOCAL ORGANIZATION: Monique Dubois and Joseba Urzelai
CONTACT: ECAL99 Secretariat, LAMI-DI-EPFL, CH-1015 Lausanne
ADVISORY BOARD:
Inman Harvey (UK) Hiroaki Kitano (JP) Daniel Mange (CH) Jean-Arcady
Meyer (F) Stefano Nolfi (I) Charles Taylor (USA) Daniel Thalmann (CH)
SCIENTIFIC COMMITTEE (under formation):
Agnessa Babloyantz (B) Wolfgang Banzhaf (D) Randall Beer (USA)
Hugues Bersini (B) Eric Bonabeau (USA) Paul Bourgine (F) Rodney
Brooks(USA) Heinrich Buelthoff (D) Raffaele Calabretta (I) Pablo
Chacon (E) Dave Cliff (UK) Jean-Louis Deneubourg (B) Marco Dorigo (B)
Rodney Douglas (CH) Claus Emmeche (DK) Boi Faltings (CH) Toshio
Fukuda (JP) Takashi Gomi (CA) Stephen Grand (UK) Howard Gutowitz (F)
Tetsuya Higuchi (JP) Phil Husbands (UK) Takashi Ikegami (JP) George
Kampis (H) Kunihiko Kaneko (JP) Laurent Keller (CH) Yasuo Kuniyoshi (JP)
Chris Langton (USA) Kristian Lindgren (SE) Pier Luisi (CH) Henrik Lund (DK)
Hanspeter Mallot (D) John McCaskill (D) Barry McMullin (IE) Olivier
Michel (CH) Orazio Miglino (I) Melanie Mitchell (USA) Federico Moran (E)
Alvaro Moreno (E) Domenico Parisi (I) Rolf Pfeifer (CH) Tom Ray (USA)
Eduardo Sanchez (CH) Chris Sander (UK) Peter Schuster (A) Katsunori
Shimohara (JP) Karl Sims (USA) Moshe Sipper (CH) Tim Smithers (E) Ricardo
Sole (E) Emmet Spier (UK) Luc Steels (F) Jun Tani (JP) Adrian Thompson (UK)
Peter Todd (D) Marco Tomassini (CH) Gunter Wagner (USA) Barbara Webb (UK)
Michael Wheeler (UK) Norm White (CA) Tom Ziemke (SE) Stephane Zrehen (USA)
From: Stefan.Wermter@sunderland.ac.uk
Subject: Hybrid Neural Symbolic Integration
Date: Wed 11/4/98 10:10 AM
International Nips Workshop on
Hybrid Neural Symbolic Integration
Stefan Wermter, University of Sunderland, UK
Ron Sun, University of Alabama, USA
December 4 and 5, 1998, Breckenridge, Colorado, USA
~~~~~~~~~~~~~~~~~~~~~~~~
Dec. 4 Morning Session:
Structured connectionism, rule representation
~~~~~~~~~~~~~~~~~~~~~~~~
Stefan Wermter, Ron Sun
Introduction and welcome to the workshop
***Jerome Feldman, David Bailey
Layered hybrid connectionist models for cognitive science
***Lokendra Shastri
Types and quantifiers in SHRUTI: a connectionist model of rapid
reasoning and relational processing
Steffen Hoelldobler, Yvonne Kalinke, Joerg Wunderlich
A recursive neural network for reflexive reasoning
Rafal Bogacz, Christophe Giraud-Carrier
A novel modular neural architecture for rule-based and
similarity-based reasoning
Nam Seog Park
Addressing knowledge representation issues in connectionist
symbolic rule encoding for general inference
Nelson A. Hallack and Gerson Zaverucha and Valmir C. Barbosa
Towards a hybrid model of first order theory refinement
Panel on "the issues of representation in hybrid models"
Chair: Ron Sun
Panelists: Jerry Feldman, Lee Giles, Risto Miikkulainen, David Waltz
5 min opening statement by each panelist
The focus of the panel is the issue of representation:
how can neural representation contribute to the power
of hybrid models? how can symbolic representation supplement
neural represnetation? how each type of representations
can be developed, acquired, or learned? What are the
principled ways these two types of representation
can be combined, synergistically ?
Dec. 4 Afternoon Session:
Neural language processing, distributed representations
~~~~~~~~~~~~~~~~~~~~~~~~
***Marshall R. Mayberry, Risto Miikkulainen
SARDSRN: a neural network shift-reduce parser
***William C. Morris, Garrison W. Cottrell, Jeffrey L. Elman
The empirical acquisition of grammatical relations
Whitney Tabor
Context free grammar representation in neural networks
Curt Burgess, Kevin Lund
The transduction of symbolic environmental input into
high-dimensional distributed representations
Pentti Kanerva
Large patterns make great symbols: an example of learning
from example
Stephen I. Gallant
Context vectors: a step toward a "grand unified representation"
Paolo Frasconi, Marco Gori, Alessandro Sperduti
Integration of graphical-based rules with adaptive learning
of structured information
Stefan C. Kremer and John Kolen
Dynamical Recurrent Networks as Symbolic Processors
Dec. 5 Morning Session:
Neural and hybrid systems for cognitive processing
~~~~~~~~~~~~~~~~~~~~~~~~
***David Waltz
The importance of importance
***Noel Sharkey, Tom Ziemke
Life, mind and robots: Biological inspirations and rooted cognition
G.K. Kraetzschmar, S. Sablatnoeg, S. Enderle, G. Palm
Using neurosymbolic integration in modelling robot environments:
a preliminary report
Timo Honkela
Self-organizing maps in symbol processing
Ronan Reilly
Evolution of symbolisation: Signposts to a bridge between
connectionist and symbolic systems
Christos Orovas, James Austin
A cellular neural associative array for symbolic vision
Panel on "hybrid and neural systems for the future"
Chair: Stefan Wermter
Panelists: Jim Austin, Joachim Diederich, Lee Giles, Noel Sharkey,
Hava Siegelman
5 min opening statement by each panelist
The focus of the panel is the impact of hybrid and neural
techniques in the future. How can we develop neural and
hybrid systems for new media? internet communication?
multimedia, web searching, data mining, neurocontrol for
robotics, integrating image/speech/language. What are the
strengths and weaknesses of hybrid neural techniques for
these tasks. Are current principles and methodologies in
neural and hybrid systems useful? How can they be
extende? What will be the impact of hybrid and neural
techniques in the future?
Dec. 5 Afternoon session:
Explanation and composition
~~~~~~~~~~~~~~~~~~~~~~~~
***H. Lipson, H. T. Siegelmann
High order shape neurons for data structure decomposition
***Alan Tickle, Frederic Maire, Joachim Diederich
Extracting the knowledge embedded within trained artificial
networks: defining the agenda
Guido Bologna
Symbolic rule extraction form the DIMLP neural network
Gerhard Paass, Joerg Kindermann
Explaining bayesian ensemble classifier models
Peter Tino, Georg Dorffner, Christian Schittenkopf
Understanding state space organization in recurrent neural
networks with iterative function systems dynamics
M.L. Vaughn, S.J. Cavill, S.J. Taylor, M.A. Foy, A.J.B. Fogg
Direct knowledge extraction and interpretation from a
multilayer perceptron network that performs low back pain
classification
James A. Hammerton, Barry L. Kalman
Holistic computation and the sequential RAAM: an evaluation
Stefan Wermter
Conclusion
More information on the NIPS98 conference can be found at:
http://www.cs.cmu.edu/Groups/NIPS/index.html
********************************************
Professor Stefan Wermter
Research Chair in Intelligent Systems
University of Sunderland
School of Computing & Information Systems
St Peters Way
Sunderland SR6 0DD
United Kingdom
phone: +44 191 515 3279
fax: +44 191 515 2781
email: stefan.wermter@sunderland.ac.uk
http://osiris.sunderland.ac.uk/~cs0stw/
From: Riccardo Poli [R.Poli@cs.bham.ac.uk]
Subject: CALL FOR INFORMATION ON COURSES IN EVOLUTIONARY COMPUTATION
Date: Fri 11/6/98 1:14 AM
CALL FOR INFORMATION ON COURSES
IN EVOLUTIONARY COMPUTATION
Teaching and learning play a crucial role in disseminating and
transferring new knowledge and know-how to students and new comers to
a field. This is more so for a relatively young field like
evolutionary computation. It has been nearly four years since John
Koza [1] compiled a valuable list of university courses on genetic
algorithms. At the time, around 30 courses were available.
The field of evolutionary computation has grown rapidly since
1995. More universities are offering courses on evolutionary
computation. There is an urgent need to look at various teaching
issues in evolutionary computation again, e.g., how we can help
students to best learn the subject effectively, what kind of topics we
should cover in a one-semester course, how much practical lab work
should be involved, what a suitable textbook should be, etc.
For these reasons we would like to collect information on university
courses in evolutionary computation in a common format, make it
available to the community and start discussions on teaching issues.
If you would like to contribute and share information with others,
please complete the form attached and email it to:
ec-teaching@cs.bham.ac.uk
THE DEADLINE FOR SUBMISSION IS: FRIDAY THE 4TH OF DECEMBER 1998.
We will compile the first draft of the course information provided and
make it available on-line early next year.
[1] J. R. Koza, University Courses on Genetic Algorithms 1995,
(a.k.a. "The GA 30"), Stanford Bookstore, Stanford University,
CA 94305-3079, 1995.
Xin Yao and Riccardo Poli
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
SURVEY ON EVOLUTIONARY COMPUTATION COURSES
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
COURSE TITLE:
LECTURER(S):
INSTITUTION AND DEPARTMENT:
TARGET STUDENTS:
(e.g. UG, MSc, PhD)
STUDENT BACKGROUND:
(e.g. CS, EE, Economics)
PREREQUISITES:
(e.g. programming, basic math, advanced math)
AIMS AND OBJECTIVES:
TEACHING METHOD:
(e.g. lectures, tutorials, labs, reading)
TOPICS COVERED AND TIME SPENT ON EACH TOPIC:
TEXTBOOKS, HANDOUTS AND READING MATERIAL USED:
ASSESSMENT METHOD:
(e.g. assignments, project(s), exam, presentations)
DETAILED SYLLABUS:
NUMBER OF TIMES THE COURSE HAS BEEN OFFERED:
AVERAGE NUMBER OF STUDENTS:
COURSE URL (IF ANY):
COMMENTS/SUGGESTIONS:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
PLEASE RETURN THE COMPLETED FORM TO ec-teaching@cs.bham.ac.uk
BY FRIDAY THE 4TH OF DECEMBER 1998
From: xli@csu.edu.au
Subject: SWARM workshop at Charles Sturt Uni.
Date: Fri 11/6/98 5:55 PM
Dear all
Swarm is a software package being developed at the Santa Fe Institute
for the simulation of complex adaptive systems. The Swarm website is
at
http://www.santafe.edu/projects/swarm/
Charles Sturt University of Australia is pleased to offer a Swarm
workshop in conjunction with the Summer School in Complex Systems on
its Bathurst campus on December 14 and 15, 1998.
The presenter of the summer school is Alex Lancaster from the Swarm
team at Santa Fe. Registration is available online at
http://clio.mit.csu.edu.au/admin/registrnew.html
************************************
Leanne Jones
School of Information Technology
Charles Sturt University
Panorama Avenue
Bathurst NSW 2795
Australia
Phone: (02) 6338 4724
Fax: (02) 6338 4649
Email: lejones@csu.edu.au
From: KBCS-98 Secretariat [kbcs@konark.ncst.ernet.in]
Subject: KBCS-98: Call for Participation
Date: Wed 11/11/98 12:57 PM
K B C S-98
Call for Participation
INTERNATIONAL CONFERENCE
ON
KNOWLEDGE BASED COMPUTER SYSTEMS
National Centre for Software Technology
Mumbai, India
December 17-19, 1998
The International Conference on Knowledge Based Computer Systems will be held
in Mumbai, India during December 17-19, 1998. The conference is intended
to act as a forum for promoting interaction among researchers in the field
of Artificial Intelligence in India and abroad. There will be a two day
conference during December 17-18, 1998 followed by a day of post-conference
tutorial on December 19, 1998.
Papers were submitted to the conference on the following topics.
o AI Applications o AI Architectures
o Automatic Programming o Cognitive Modeling
o Expert Systems o Foundations of AI
o Genetic Algorithms o Information Retrieval
o Intelligent Agents o Intelligent Tutoring Systems
o Knowledge Acquisition o Knowledge Management
o Knowledge Representation o Machine Learning
o Machine Translation o Natural Language Processing
o Neural Networks o Planning and Scheduling
o Reasoning o Robotics
o Search Techniques o Speech Processing
o Theorem Proving o Uncertainty Handling
o Vision
About 30 papers will be presented during the conference.
Post-Conference Tutorial
The tutorial will be conducted on December 19, 1998 at NCST, Juhu, Mumbai.
o An Introduction to Information Extraction - (11 AM to 6 PM)
Amit Bagga, GE Corporate R & D Centre, USA
Registration
Last date for registration is 11th December 1998. On-site registration will
be subject to availability of seats. All payments should be made by a crossed
Mumbai cheque or a demand draft, payable to KBCS-98.
Fees:
Conference
Students : Rs 600
Delegates from not-for-profit : Rs 800
organisations
Other Delegates : Rs 1200
Tutorials:
Full Day : Rs 700
For further information please refer to the KBCS-98 home page or write to the
KBCS-98 Secretariat.
KBCS-98 Secretariat Phone: +91 (22) 620 1606
National Centre for Software Technology Fax: +91 (22) 621 0139
Gulmohar Cross Rd No. 9 E-mail: kbcs@konark.ncst.ernet.in
Juhu, Mumbai 400 049, India
URL: http://konark.ncst.ernet.in/~kbcs/kbcs98/
From: Jonathan Baxter [Jon.Baxter@keating.anu.edu.au]
Subject: NIPS '98
Date: Mon 11/9/98 2:25 PM
*********************** NIPS*98 FINAL PROGRAM **********************
SUN NOV 29
18:00-22:00 Registration
MON NOV 30
08:30-18:00 Registration
09:30-17:30 Tutorials
18:30 Reception and Conference Banquet
20:30 The laws of the WEB (Banquet talk)
B. Huberman
Xerox PARC
TUE DEC 1
Oral Session 1:
08:30 Statistics of visual images: neural representation and synthesis
(Invited)
E. Simoncelli
New York University
09:20 Attentional modulation of human pattern discrimination
psychophysics reproduced by a quantitative model
(VS1, Oral)
L. Itti, J. Braun, D. Lee, C. Koch
California Institute of Technology
09:40 Orientation, scale, and discontinuity as emergent
properties of illusory contour shape
(VS2, Oral)
K. Thornber, L. Williams
NEC Research Institute, University of New Mexico
10:00 DTs: dynamic trees
(AA1, Spotlight)
C. Williams, N. Adams
Aston University
Modeling stationary and integrated time series with
autoregressive neural networks
(LT1, Spotlight)
F. Leisch, A. Trapletti, K. Hornik
Technical University of Vienna
Analog neural nets with Gaussian or other common noise
distributions cannot recognize arbitrary regular languages
(LT2, Spotlight)
W. Maass, E. Sontag
Technical University of Graz, Rutgers University
Semiparametric support vector and linear programming machines
(AA8, Spotlight)
A. Smola, T. Friess, B. Schoelkopf
GMD FIRST
Blind separation of filtered source using state-space approach
(AA13, Spotlight)
L. Zhang, A. Cichocki
RIKEN Brain Science Institute
10:15-11:00 Break
Oral Session 2:
11:00 The bias-variance tradeoff and the randomized GACV
(AA4, Oral)
G. Wahba, X. Lin, F. Gao, D. Xiang, R. Klein, B. Klein
University of Wisconsin-Madison, SAS Institute
11:20 Kernel PCA and de-noising in feature spaces
(AA7, Oral)
S. Mika, B. Schoelkopf, A. Smola, K. Mueller, M Scholz, G. Raetsch
GMD FIRST
11:40 Sparse code shrinkage: denoising by maximum likelihood estimation
(AA12, Oral)
A. Hyvaarinen, P. Hoyer, E. Oja
Helsinki University of Technology
12:00-14:00 Lunch
Oral Session 3:
14:00 Temporally asymmetric Hebbian learning, spike timing
and neuronal response variability
(Invited)
L. Abbott
Brandeis University
14:50 Information maximization in single neurons
(NS1, Oral)
M. Stemmler, C. Koch
California Institute of Technology
15:10 Multi-electrode spike sorting by clustering transfer functions
(NS2, Oral)
D. Rinberg, H. Davidowitz, N. Tishby
NEC Research Institute
15:30 Distributional population codes and multiple motion models
(NS3, Spotlight)
R. Zemel, P. Dayan
University of Arizona, Massachusetts Institute of Technology
Population coding with correlated noise
(NS4, Spotlight)
H. Yoon, H. Sompolinsky
Hebrew University
Bayesian modeling of human concept learning
(CS1, Spotlight)
J. Tenenbaum
Massachusetts Institute of Technology
Mechanisms of generalization in perceptual learning
(CS2, Spotlight)
Z. Liu, D. Weinshall
NEC Research Institute, Hebrew University
An entropic estimator for structure discovery
(SP1, Spotlight)
M. Brand
Mitsubishi Electric Research Laboratory
15:45-16:15 Break
Oral Session 4:
16:15 The role of lateral cortical competition in ocular
dominance development
(NS6, Oral)
C. Piepenbrock, K. Obermayer
Technical University of Berlin
16:35 Evidence for learning of a forward dynamic model in
human adaptive control
(CS3, Oral)
N. Bhushan, R. Shadmehr
Johns Hopkins University
16:55-18:00 Poster Preview
19:30 Poster Session
WED DEC 2
Oral Session 5:
08:30 Computation by Cortical Modules
(Invited)
H. Sompolinsky
Hebrew University
09:20 Learning curves for Gaussian processes
(LT14, Oral)
P. Sollich
University of Edinburgh
9:40 Mean field methods for classification with Gaussian processes
(LT15, Oral)
M. Opper O. Winther
Aston University, Niels Bohr Institute
10:00 Dynamics of supervised learning with restricted training sets
(LT16, Spotlight)
A. Coolen, D. Saad
King's College London, Aston University
Finite-dimensional approximation of Gaussian processes
(LT18, Spotlight)
G. Trecate, C. Williams, M. Opper
University of Pavia, Aston University
Inference in multilayer networks via large deviation bounds
(LT20, Spotlight)
M. Kearns, L. Saul
AT&T; Labs
Gradient descent for general reinforcement learning
(CN14, Spotlight)
L. Baird, A. Moore
Carnegie Mellon University
Risk sensitive reinforcement learning
(CN15, Spotlight)
R. Neuneier, O. Mihatsch
Siemens AG
10:15-11:00 Break
Oral Session 6:
11:00 VLSI implementation of motion centroid localization for
autonomous navigation
(IM6, Oral)
R. Etienne-Cummings, M. Ghani, V. Gruev
Southern Illinois University
11:20 Improved switching among temporally abstract actions
(CN16, Oral)
R. Sutton, S. Singh, D. Precup, B. Ravindran
University of Massachusetts, University of Colorado
11:40 Finite-sample convergence rates for Q-learning and
indirect algorithms
(CN17, Oral)
M. Kearns, S. Singh
AT&T; Labs, University of Colorado
12:00-14:00 Lunch
Oral Session 7:
14:00 Statistical natural language processing: better living
through floating-point numbers
(Invited)
E. Charniak
Brown University
14:50 Markov processes on curves for automatic speech recognition
(SP3, Oral)
L. Saul, M. Rahim
AT&T; Labs
15:10 Approximate learning of dynamic models
(AA22, Oral)
X. Boyen, D. Koller
Stanford University
15:30 Learning nonlinear stochastic dynamics using the
generalized EM algorithm
(AA23, Spotlight)
Z. Ghahramani, S. Roweis
University of Toronto, California Institute of Technology
Reinforcement learning for trading systems
(AP9, Spotlight)
J. Moody, M. Saffell
Oregon Graduate Institute
Bayesian modeling of facial similarity
(AP13, Spotlight)
B. Moghaddam, T. Jebara, A. Pentland
Mitsubishi Electric Research Laboratory,
Massachusetts Institute of Technology
Computation of smooth optical flow in a feedback
connected analog network
(IM8, Spotlight)
A. Stocker, R. Douglas
University and ETH Zurich
Classification on pairwise proximity data
(AA26, spotlight)
T. Graepel, R. Herbrich, P. Bollmann-Sdorra, K. Obermayer
Technical University of Berlin
15:45-16:15 Break
Oral Session 8:
16:15 Learning from dyadic data
(AA27, Oral)
T. Hofmann, J. Puzicha, M. Jordan
Massachusetts Institute of Technology, University of Bonn
16:35 Classification in non-metric spaces
(VS7, Oral)
D. Weinshall, D. Jacobs, Y. Gdalyahu
NEC Research Institute, Hebrew University
16:55-18:00 Poster Preview
19:30 Poster Session
THU DEC 3
Oral Session 9:
08:30 Convergence of the wake-sleep algorithm
(LT22, Oral)
S. Ikeda, S. Amari, H. Nakahara
RIKEN Brain Science Institute
08:50 Learning a continuous hidden variable model for binary data
(AA32, Oral)
D. Lee, H. Sompolinsky
Bell Laboratories, Hebrew University
09:10 Direct optimization of margins improves generalization
in combined classifiers
(LT23, Oral)
L. Mason, P. Bartlett, J. Baxter
Australian National University
09:30 A polygonal line algorithm for constructing principal curves
(AA39, Oral)
B. Kegl, A. Krzyzak, T. Linder, K. Zeger
Concordia University, Queen's University, UC San Diego
09:50-10:30 Break
Oral Session 10:
10:30 Graphical models for recognizing human interactions
(AP15, Oral)
N. Oliver, B. Rosario, A. Pentland
Massachusetts Institute of Technology
10:50 Fast neural network emulation of physics-based models
for computer animation
(AP16, Oral)
R. Grzeszczuk, D. Terzopoulos, G. Hinton
Intel Corporation, University of Toronto
11:10 Things that think
(Invited)
N. Gershenfeld
Massachusetts Institute of Technology
12:00 End of main conference
POSTERS: TUE DEC 1
Basis selection for wavelet regression
(AA2, Poster)
K. Wheeler
NASA Ames Research Center
Boxlets: a fast convolution algorithm for signal processing
and neural networks
(AA3, Poster)
P. Simard, L. Bottou, P. Haffner, Y. LeCun
AT&T; Labs
Least absolute shrinkage is equivalent to quadratic penalization
(AA5, Poster)
Y. Grandvalet, S. Canu
Universite de Technologie de Compiegne
Neural networks for density estimation
(AA6, Poster)
M. Magdon-Ismail, A. Atiya
California Institute of Technology
Semi-supervised support vector machines
(AA9, Poster)
K. Bennett, A. Demiriz
Rensselaer Polytechnic Institute
Exploiting generative models in discriminative classifiers
(AA10, Poster)
T. Jaakkola, D. Haussler
UC Santa Cruz
Using analytic QP and sparseness to speed training of support
vector machines
(AA11, Poster)
J. Platt
Microsoft Research
Source separation as a by-product of regularization
(AA14, Poster)
S. Hochreiter, J. Schmidhuber
Technical University of Munich, IDSIA
Unsupervised classification with non-Gaussian mixture
models using ICA
(AA15, Poster)
T-W. Lee, M. Lewicki, T. Sejnowski
The Salk Institute
Hierarchical ICA belief networks
(AA16, Poster)
H. Attias
UC San Francisco
Efficient Bayesian parameter estimation in large discrete domains
(AA17, Poster)
N. Friedman, Y. Singer
UC Berkeley, AT&T; Labs
Discovering hidden features with Gaussian processes regression
(AA18, Poster)
F. Vivarelli, C. Williams
Aston University
Bayesian PCA
(AA19, Poster)
C. Bishop
Microsoft Research
Replicator equations, maximal cliques, and graph isomorphism
(AA20, Poster)
M. Pelillo
University of Venice
Convergence rates of algorithms for perceptual organization:
detecting visual contours
(AA21, Poster)
A. Yuille, J. Coughlan
Smith-Kettlewell Eye Research Institute
Independent component analysis of intracellular calcium spike data
(AP1, Poster)
K. Prank, J.Boerger, A. von zur Muehlen, G. Brabant, C. Schoefl
Medical School Hannover
Applications of multi-resolution neural networks to mammography
(AP2, Poster)
P. Sajda, C. Spence
Sarnoff Corporation
Making templates rotationally invariant: an application to
rotated digit recognition
(AP3, Poster)
S. Baluja
Carnegie Mellon University
Graph matching for shape retrieval
(AP4, Poster)
B. Huet, A. Cross, E. Hancock
University of York
Vertex identification in high energy physics experiments
(AP5, Poster)
G. Dror, H. Abramowicz, D. Horn
The Academic College of Tel-Aviv-Yaffo, Tel-Aviv University
Familiarity discrimination of radar pulses
(AP6, Poster)
E. Granger, S. Grossberg, M. Rubin, W. Streilein
Ecole Polytechnique de Montreal, Boston University
Robot docking using mixtures of Gaussians
(AP7, Poster)
M. Williamson, R. Murray-Smith, V. Hansen
Massachusetts Institute of Technology, Technical University
of Denmark, Daimler-Benz
Call-based fraud detection in mobile communication networks using
a hierarchical regime-switching model
(AP8, Poster)
J. Hollmen, V. Tresp
Helsinki University of Technology, Siemens AG
Multiple paired forward-inverse models for human motor learning
and control
(CS4, Poster)
M. Haruno, D. Wolpert, M. Kawato
ATR Human Information Processing Research Laboratories,
University College London
A neuromorphic monaural sound localizer
(IM1, Poster)
J. Harris, C-J. Pu, J. Principe
University of Florida
Active noise canceling using analog neuro-chip with on-chip
learning capability
(IM2, Poster)
J-W. Cho, S-Y. Lee
Korea Advanced Institute of Science and Technology
Optimizing correlation algorithms for hardware-based
transient classification
(IM3, Poster)
R. Edwards, G. Cauwenberghs, F. Pineda
Johns Hopkins University
A high performance k-NN classifier using a binary correlation
matrix memory
(IM4, Poster)
P. Zhou, J. Austin, J. Kennedy
University of York
A micropower CMOS adaptive amplitude and shift invariant
vector quantizer
(IM5, Poster)
R. Coggins, R. Wang, M. Jabri
University of Sidney
Where does the population vector of motor cortical cells point
during reaching movements?
(NS5, Poster)
P. Baraduc, E. Guigon, Y. Burnod
Universite Pierre et Marie Curie
Heeger's normalization, line attractor network and ideal observers
(NS7, Poster)
S. Deneve, A. Pouget, P. Latham
Georgetown University
Image statistics and cortical normalization models
(NS8, Poster)
E. Simoncelli, O. Schwartz
New York University
Learning instance-independent value functions to enhance
local search
(CN1, Poster)
R. Moll, A. Barto, T. Perkins, R. Sutton
University of Massachusetts
Exploring unknown environments with real-time heuristic search
(CN2, Poster)
S. Koenig
Georgia Institute of Technology
GLS: a hybrid classifier system based on POMDP research
(CN3, Poster)
A. Hayashi, N. Suematsu
Hiroshima City University
Non-linear PI control inspired by biological control systems
(CN4, Poster)
L. Brown, G. Gonye, J. Schwaber
E.I. DuPont deNemours
Coordinate transformation learning of hand position feedback
controller by using change of position error norm
(CN5, Poster)
E. Oyama, S. Tachi
University of Tokyo
Optimizing admission control while ensuring quality of service in
multimedia networks via reinforcement learning
(CN6, Poster)
T. Brown, H. Tong, S. Singh
University of Colorado
Barycentric interpolators for continuous space & time
reinforcement learning
(CN7, Poster)
R. Munos, A. Moore
Carnegie Mellon University
Modifying the parti-game algorithm for increased robustness,
higher efficiency and better policies
(CN8, Poster)
M. Al-Ansari, R. Williams
Northeastern University
Coding time-varying signals using sparse, shift-invariant
representations
(SP2, Poster)
M. Lewicki, T. Sejnowski
The Salk Institute
Phase diagram and storage capacity of sequence storing neural
networks
(LT3, Poster)
A. Duering, A. Coolen, D. Sherrington
Oxford University, King's College London
Discontinuous recall transitions induced by competition between
short- and long-range interactions in recurrent networks
(LT4, Poster)
N. Skantzos, C. Beckmann, A. Coolen
King's College London
Computational differences between asymmetrical and symmetrical
networks
(LT5, Poster)
Z. Li, P. Dayan
Massachusetts Institute of Technology
Shrinking the tube: a new support vector regression algorithm
(LT6, Poster)
B. Schoelkopf, P. Bartlett, A. Smola, R. Williamson
GMD FIRST, Australian National University
Dynamically adapting kernels in support vector machines
(LT7, Poster)
N. Cristianini, C. Campbell, J. Shawe-Taylor
University of Bristol, University of London
A theory of mean field approximation
(LT8, Poster)
T. Tanaka
Tokyo Metropolitan University
The belief in TAP
(LT9, Poster)
Y. Kabashima, D. Saad
Tokyo Institute of Technology, Aston University
Unsupervised clustering: the mutual information between parameters
and observations
(LT10, Poster)
D. Herschkowitz, J-P. Nadal
Ecole Normale Superieure
Classification with linear threshold functions and the linear loss
(LT11, Poster)
C. Gentile, M. Warmuth
University of Milan, UC Santa Cruz
Almost linear VC dimension bounds for piecewise polynomial networks
(LT12, Poster)
P. Bartlett, V. Maiorov, R. Meir
Australian National University, Technion
Tight bounds for the VC-dimension of piecewise polynomial networks
(LT13, Poster)
A. Sakurai
Japan Advanced Institute of Science and Technology
Learning Lie transformation groups for invariant visual perception
(VS3, Poster)
R. Rao, D. Rudermann
The Salk Institute
Support vector machines applied to face recognition
(VS4, Poster)
J. Phillips
National Institute of Standards and Technology
Learning to find pictures of people
(VS5, Poster)
S. Ioffe, D. Forsyth
UC Berkeley
Probabilistic sensor fusion
(VS6, Poster)
R. Sharma, T. Leen, M. Pavel
Oregon Graduate Institute
POSTERS: WED DEC 2
Learning multi-class dynamics
(AA24, Poster)
A. Blake, B. North, M. Isard
Oxford University
Fisher scoring and a mixture of modes approach for approximate
inference and learning in nonlinear state space models
(AA25, Poster)
T. Briegel, V. Tresp
Siemens AG
A randomized algorithm for pairwise clustering
(AA28, Poster)
Y. Gdalyahu, D. Weinshall, M. Werman
Hebrew University
Visualizing group structure
(AA29, Poster)
M. Held, J. Puzicha, J. Buhmann
University of Bonn
Probabilistic visualization of high-dimensional binary data
(AA30, Poster)
M. Tipping
Microsoft Research
Restructuring sparse high dimensional data for effective retrieval
(AA31, Poster)
C. Isbell, P. Viola
Massachusetts Institute of Technology
Exploratory data analysis using radial basis function latent
variable models
(AA33, Poster)
A. Marrs, A. Webb
DERA
SMEM algorithm for mixture models
(AA34, Poster)
N. Ueda, R. Nakano, Z. Ghahramani, G. Hinton
NTT Communication Science Laboratories, University of Toronto
Learning mixture hierarchies
(AA35, Poster)
N. Vasconcelos, A. Lippman
Massachusetts Institute of Technology
On-line and batch parameter estimation of Gaussian mixtures
based on the relative entropy
(AA36, Poster)
Y. Singer, M. Warmuth
AT&T; Labs, UC Santa Cruz
Very fast EM-based mixture model clustering using multiresolution
kd-trees
(AA37, Poster)
A. Moore
Carnegie Mellon University
Maximum conditional likelihood via bound maximization and
the CEM algorithm
(AA38, Poster)
T. Jebara, A. Pentland
Massachusetts Institute of Technology
Lazy learning meets the recursive least squares algorithm
(AA40, Poster)
M. Birattari, G. Bontempi, H. Bersini
Universite Libre de Bruxelles
Global optimization of neural network models via sequential sampling
(AA41, Poster)
J. de Freitas, M. Niranjan, A. Doucet, A. Gee
Cambridge University
Regularizing AdaBoost
(AA42, Poster)
G. Raetsch, T. Onoda, K. Mueller
GMD FIRST
Using collective intelligence to route Internet traffic
(AP10, Poster)
D. Wolpert, K. Tumer, J. Frank
NASA Ames Research Center
Scheduling straight-line code using reinforcement learning
and rollouts
(AP11, Poster)
A. McGovern, E. Moss
University of Massachusetts
Probabilistic modeling for face orientation discrimination:
learning from labeled and unlabeled data
(AP12, Poster)
S. Baluja
Carnegie Mellon University
Adding constrained discontinuities to Gaussian process models
of wind fields
(AP14, Poster)
D. Cornford, I. Nabney, C. Williams
Aston University
A principle for unsupervised hierarchical decomposition of
visual scenes
(CS5, Poster)
M. Mozer
University of Colorado
Facial memory is kernel density estimation (almost)
(CS6, Poster)
M. Dailey, G. Cottrell, T. Busey
UC San Diego, Indiana University
Perceiving without learning: from spirals to inside/outside
relations
(CS7, Poster)
K. Chen, D. Wang
Ohio State University
Utilizing time: asynchronous binding
(CS8, Poster)
B. Love
Northwestern University
A model for associative multiplication
(CS9, Poster)
G. Christianson, S. Becker
McMaster University
Analog VLSI cellular implementation of the boundary contour system
(IM7, Poster)
G. Cauwenberghs, J. Waskiewicz
Johns Hopkins University
An integrated vision sensor for the computation of optical
flow singular points
(IM9, Poster)
C. Higgins, C. Koch
California Institute of Technology
Spike-based compared to rate-based Hebbian learning
(NS9, Poster)
R. Kempter, W. Gerstner, L. van Hemmen
Technical University of Munich, Swiss Federal Institute of Technology
Neuronal regulation implements efficient synaptic pruning
(NS10, Poster)
G. Chechik, I. Meilijson, E. Ruppin
Tel-Aviv University
Signal detection in noisy weakly-active dendrites
(NS11, Poster)
A. Manwani, C. Koch
California Institute of Technology
Influence of changing the synaptic transmitter release probability
on contrast adaptation of simple cells in the primary visual cortex
(NS12, Poster)
P. Adorjan, K. Obermayer
Technical University of Berlin
Complex cells as cortically amplified simple cells
(NS13, Poster)
F. Chance, S. Nelson, L. Abbott
Brandeis University
Synergy and redundancy among brain cells of behaving monkeys
(NS14, Poster)
I. Gat, N. Tishby
Hebrew University, NEC Research Institute
Visualizing and analyzing single-trial event-related potentials
(NS15, Poster)
T-P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne,
T. Sejnowski
The Salk Institute, Naval Health Research Center, UC San Diego
A reinforcement learning algorithm in partially observable
environments using short-term memory
(CN9, Poster)
N. Suematsu, A. Hayashi
Hiroshima City University
Experiments with a memoryless algorithm which learns locally optimal
stochastic policies for partially observable Markov decision
processes
(CN10, Poster)
J. Williams, S. Singh
University of Colorado
The effect of eligibility traces on finding optimal memoryless
policies in partially observable Markovian decision processes
(CN11, Poster)
J. Loch
University of Colorado
Learning macro-actions in reinforcement learning
(CN12, Poster)
J. Randlov
Niels Bohr Institute
Reinforcement learning based on on-line EM algorithm
(CN13, Poster)
M. Sato, S. Ishii
ATR Human Information Processing Research Laboratories,
Nara Institute of Science and Technology
Controlling the complexity of HMM systems by regularization
(SP4, Poster)
C. Neukirchen, G. Rigoll
Gerhard-Mercator-University
Maximum-likelihood continuity mapping (MALCOM): an alternative
to HMMs
(SP5, Poster)
D. Nix, J. Hogden
Los Alamos National Laboratory
On-line learning with restricted training sets: exact solution
as benchmark for general theories
(LT17, Poster)
H. Rae, P. Sollich, A. Coolen
King's College London, University of Edinburgh
General bounds on Bayes errors for regression with
Gaussian processes
(LT19, Poster)
M. Opper, F. Vivarelli
Aston University
Variational approximations of graphical models using
undirected graphs
(LT21, Poster)
D. Barber, W. Wiegerinck
University of Nijmegen
Optimizing classifiers for imbalanced training sets
(LT24, Poster)
G. Karakoulas, J. Shawe-Taylor
Canadian Imperial Bank of Commerce, University of London
On the optimality of incremental neural network algorithms
(LT25, Poster)
R. Meir, V. Maiorov
Technion
General-purpose localization of textured image regions
(VS8, Poster)
R. Rosenholtz
Xerox PARC
A V1 model of pop out and asymmetry in visual search
(VS9, Poster)
Z. Li
Massachusetts Institute of Technology
Minutemax: a fast approximation for minimax learning
(VS10, Poster)
J. Coughlan, A. Yuille
Smith-Kettlewell Eye Research Institute
Using statistical properties of a labelled visual world to
estimate scenes
(VS11, Poster)
W. Freeman, E. Pasztor
Mitsubishi Electric Research Laboratory
Example based image synthesis of articulated figures
(VS12, Poster)
T. Darrell
Interval Research
Subject: PAAM99 Call for Papers Date: Wed 11/11/98 11:02 AM PAAM99 - CALL FOR PAPERS AND PARTICIPATION The Fourth International Conference and Exhibition on The Practical Application of Intelligent Agents and Multi-Agent Technology Monday 19th April - Wednesday 21st April 1999 Commonwealth Institute, London, UK http://www.practical-applications.co.uk/PAAM99 PAAM99 is sponsored and supported to date by: AgentLink, Agent Society CompulogNet, FIPA, IF Computer, LPA. ***UPDATE: Invited Programme to date*** TUTORIALS: Monday 19th April, 1999 Dr Matthias Klusch, Carnegie Mellon University, USA Dr Peter Edwards, University of Aberdeen, UK Dr H. Van Dyke Parunak, Center for Electronic Commerce, USA Anand Rao, Mitchell Madison Group, Australia INVITED SPEAKERS: Tuesday 20th and Wednesday 21st April, 1999 Eric Horvitz, Microsoft, USA Dr H. Van Dyke Parunak, Center for Electronic Commerce, USA Anand Rao, Mitchell Madison Group, Australia PAAM99 Agent technology is an exciting and fast moving field of IT which is now making the transition from Universities and research labs into industrial and commercial applications. PAAM99 will demonstrate how agent technology is creating new opportunities for business and industry by focusing on the central issues. PAAM and PA Expo99 PAAM is a key part of the Practical Application Expo: a unique five-day multi-technology, multi-track event which takes place every Spring in London. PA Expo99 will also include conferences on KDD, Logic Programming, Knowledge Management and JAVA. PA Expo combines the peer-to-peer review process of an academic conference with the commercial relevence of an applied industrial event. This contrast of theory and practice, research and deployment is rarely found elsewhere, and makes an ideal forum for participants to network and share ideas. PA Expo offers a rich blend of tutorials, invited talks, refereed papers, panel discussions, poster sessions, social agenda and a full industrial exhibition. The result is a varied technical programme which caters for delegates of various levels of expertise, from beginner to advanced practitioner, in a pleasant and productive environment. You are invited to register your interest in PAAM99 by completing the reply form below. Venue PA EXPO99 will take place at The Commonwealth Conference and Events Centre. It enjoys a prime location in the heart of London, strategically situated in Royal Kensington and Chelsea, leading onto High Street Kensington which is home to one of London's premier shopping areas, and a selection of quality hotels and restaurants. Call for Submissions Your submission should describe one of the following: Commercially available products Internally deployed solutions Fully advanced pre-production prototypes There are two types of submission available: 1.Paper 2.Industrial Report 1 Paper Paper submissions will be refereed by the programme committee. Accepted papers will have 30 minutes to present their papers at the conference. Papers should be no longer than 20 pages. Topics of interest include but are not limited to: Personal agents for: Email PDA's Finding & retrieving information / WWW Scheduling/diary management Mobility management Business/service agents for: Workflow management Network Management and control Buying & selling services Information Management, Retrieval & Understanding Information Customisation WWW and Internet Negotiation Price Discovery Information Arbitrage Manufacturing Planning & Control Tools and Techniques for: Process Control Traffic Control Process Re-engineering Knowledge Management Languages for developing agent systems Coordination standards Standards for information transfer Identity Management Monetary Instrument Standards Design methodologies for agent-based systems Also of interest are survey papers which indicate the future direction this important technology will take. 2 Industrial Report In recognition of the applied nature of PAAM, we encourage the submission of Industrial Reports. This type of submission is intended for professionals who have little time to write a full paper, but who nevertheless, would like the opportunity to present the benefits of their application at the conference. Please forward a short paper (no more than 6 pages) describing your application based on the guidelines to be found at our web site. Industrial Reports will receive a fast track review. Submission Policies Submissions need to meet the conference objectives and achieve a balance between application and theory and with this in mind we have produced guidelines to help you achieve this. Please visit our web site for further information Papers: http://www.practical-applications.co.uk/PAAM99/guidelines Reports: http://www.practical-applications.co.uk/PAAM99//report Please indicate which category your paper falls into on submission. Papers and Industrial Reports will be published in the proceedings but note that there will be a clear indication as to the type of submission. In some cases revisions to submissions will be asked for by the programme committee. Authors are responsible for making the required revisions and submitting the final or revised version of the paper by the due date for the final version. Papers not meeting the revision requirements will not be included in the Proceedings. Papers from non English speakers are strongly encouraged, however we would ask that your paper is proof read by a native English speaker, if possible, before submission. Submission Details Dates: Submission Deadline: January 11th, 1999 Notification/Comments: February 15th, 1999 Final Papers due: March 15th, 1999 Five copies of your paper, in English, should be received by the conference organisers, on or before January 11, 1999. Formatting instructions (Word), can be downloaded from our web site. Please also submit an electronic version of your paper as a PDF file or Word document. The email address for this will be made available in due course. Please include a cover page including: 1.Full Contact Details 2.A Short Abstract Please use the following address when sending your papers to us. PAAM99 54 Knowle Avenue Blackpool Lancs FY2 9UD UK Call for Exhibitors The conference also provides an opportunity for software vendors and developers to demonstrate Agent systems. You are invited to contact the organiser to arrange for your application to be exhibited at the event. Programme Committee The PAAM99 Programme Committee comprises a number of highly influential figures who are recognised experts in the field of agent technology. Chaired by Divine T. Ndumu and Hyacinth S. Nwana of British Telecom Laboratories, this programme committee is drawn from industry leaders and first class universities and research organisations. Joern Altmann University of California,Berkeley, USA Jeffrey Bradshaw Boeing, USA Stephan Brunessaux MATRASystemes & Information, France Stefan Bussmann Daimler-Benz AG, Germany Paolo Ciancarini University of Bologna, Italy Barry Crabtree BT Laboratories, UK Peter Edwards University of Aberdeen, UK Innes Ferguson Active On-line Systems Ltd., UK Tim Finin University of Maryland, USA Klaus Fischer DFKI, Germany Benjamin Grosof IBM, USA Fumio Hattori NTT, Japan Michael Huhns University of South Carolina, USA Toru Ishida Kyoto University, Japan Sverker Janson SICS, Sweden Nick Jennings University of London, UK Paul Kearney BT Laboratories, UK Matthias Klusch Carnegie Mellon University, USA Yannis Labrou University of Maryland, Baltimore County, USA Danny Lange General Magic, USA Michael Lee INSEAD, France Victor Lesser University of Massachusetts, USA David Martin SRI International, USA Cindy Mason University of California, Berkeley, USA Joerg Mueller John Wiley & Sons Ltd, UK Katashi Nagao Sony Computer Science Lab. Inc., Japan Remo Pareschi Telecom Italia, Italy Barney Pell NASA, USA Tony Rutkowski General Magic, USA Donald Steiner Siemens AG, Germany Arvindra Sehmi CSK Software Ltd, UK Leon Sterling University of Melbourne, Australia Hiroyuki Tarumi Kyoto University, Japan Jan Treur Vrije Univ. Amsterdam, The Netherlands Dr.H.Van Dyke Parunak Industrial Technology Institute, USA Peter Wayner Newray Inc, USA Michael Wooldridge Queen Mary & Westfield College, UK Organisation The Conference is organised by The Practical Application Company. The Sponsorship and Exhibition Coordinator is Clive Spenser, PMG Treasurer, and Marketing Director of Logic Programming Associates. ~~~~~~~~~~~~~~~~~~~~ To register interest please take the time to fill in this quick reply form and send it to agents@pap.com Name: Position: Organisation: Address: Postcode: Country: Telephone: Fax: E-mail: Web: [ ] I may submit a paper to PAAM99 (Give provisional title if possible) [ ] I may submit an Industrial Report to PAAM99 (Give provisional title if possible) [ ] I may wish to attend PAAM99 as a delegate [ ] My company may wish to sponsor or exhibit at the event [ ] I have no interest in Intelligent Agents. But keep me informed of other events at the Practical Application Expo [ ] I have no interest in this. Please remove me from the mailing list The Practical Application Company PO Box 137 Blackpool Lancs FY2 9UN UK Tel: +44 (0)1253 358081 Fax: +44 (0)1253 353811 email: info@pap.com WWW: http://www.practical-applications.co.uk/TPAC
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