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



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Dec. 4 Morning Session:
Structured connectionism, rule representation
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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
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***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
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***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



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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: [log in to unmask]
http://osiris.sunderland.ac.uk/~cs0stw/
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