ECAI NNSK Workshop Program and Call for Participation
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ECAI'96 Workshop
on
NEURAL NETWORKS AND STRUCTURED KNOWLEDGE (NNSK)
August 12, 1996
during the
12th European Conference on Artificial Intelligence
August 12-16, 1996 in Budapest, Hungary
Call for Participation
-------------------------------------------------------------------------------
Latest information can be retrieved from the NNSK WWW-page
http://www.informatik.uni-ulm.de/fakultaet/abteilungen/ni/ECAI-96/NNSK.html
BACKGROUND
----------
Neural networks mostly are used for tasks dealing with information presented in
vector or matrix form, without a rich internal structure reflecting relations
between different entities. In some application areas, e.g. speech processing
or forecasting, types of networks have been investigated for their ability to
represent sequences of input data. Whereas approaches to use neural networks
for the representation and processing of structured knowledge have been around
for quite some time, especially in the area of connectionism, they frequently
suffer from problems with expressiveness, knowledge acquisition, adaptivity and
learning, or human interpretation. In the last years much progress has been
made in the theoretical understanding and the construction of neural systems
capable of representing and processing structured knowledge in an adequate way,
while maintaining essential capabilities of neural networks such as learning,
tolerance of noise, treatment of inconsistencies, and parallel operation. The
goal of this workshop is twofold: On one hand, existing mechanisms are
critically examined with respect to their suitability for the acquisition,
representation, processing and interpretation of structured knowledge. On the
other hand, new approaches, especially concerning the design of systems based
on such mechanisms, are presented, with particular emphasis on their
application to realistic problems.
PRELIMINARY WORKSHOP PROGRAM
----------------------------
8:30 - 8:50 INTRODUCTION (F. Kurfess)
8:50 - 10:10 SYMBOLIC INFERENCE IN CONNECTIONIST SYSTEMS
8:50 Semantic Knowledge in General Neural Units: Issues
of Representation (J. de L. Pereira Castro)
9:10 Implementation of a SHRUTI Knowledge Representation
and Reasoning System (R. Hayward, J. Diederich)
9:30 A Connectionist Representation of Symbolic Components,
Dynamic Bindings and Basic Inference Operations
(N. Seog Park, D. Robertson)
9:50 Logical Inference and Inductive Learning
(A.S. d'Avila Garcez, G. Zaverucha, L.A.V. de Carvalho)
10:10 - 10:20 Discussion
10:30 - 11:00 Break
11:00 - 11:40 EXPLOITING PROBLEM-INHERENT STRUCTURED META-KNOWLEDGE
11:00 Declarative Heuristics for Neural Network Design
(M. Vuilleumier, M. Hilario)
11:20 Sign Recognition as a Support to Robot Navigation
(G. Adorni, G. Destri, M. Gori, M. Mordonini)
11:40 - 12:00 Discussion
12:00 - 13:45 Break
13:45 - 14:45 SUPERVISED INDUCTIVE INFERENCE ON STRUCTURED DOMAINS
13:45 Inductive Inference from Noisy Examples: The Rule-Noise
Dilemma and the Hybrid Finite State Filter
(M. Gori, M. Maggini, G. Soda)
14:05 Inductive Learning in Symbolic Domains Using Structure-
Driven Recurrent Neural Networks
(A. Kuechler, C. Goller)
14:25 Neural Networks for the Classification of Structures
(A. Sperduti)
14:45 - 15:15 Discussion
15:15 - 15:45 Break
15:45 - 16:25 INFERRING HIERARCHIES
15:45 Inferring Hierarchical Categories with ART-Based
Modular Neural Networks (G. Bartfai)
16:05 A Tree-Structured Approach to Medical Diagnosis Tasks
(J. Rahmel, P. Hahn)
16:25 - 16:45 Discussion
16:45 - 17:30 General Discussion and Closing
DISCUSSION THEMES
-----------------
In addition to discussions centered around the presentations, we want to foster
an exchange of ideas and opinions about issues relevant for representing and
processing structured knowledge with neural networks.
1. Are symbols ultimately necessary for knowledge, or are they an artefact? Can
we provide symbol-less methods that achieve some kind of knowledge processing
facility? With respect to the limited discussion time at the workshop, we
would like to put the emphasis on the technical and practical aspects
(experiments, methods), not so much on the underlying philosophical thoughts.
2. Why do we need structured knowledge? Because the world is structured?
Because our cognition is systematic (Fodor & Pylyshyn's argument)? For
efficiency reasons? And should structure be then explicitly represented?
3. Should we try to use neural networks for the representation and processing of
structured knowledge, or are we simply wasting our time? After all, there are
well-founded methods and techniques in traditional, symbol-oriented AI.
If we should try, what are good reasons?
o knowledge acquisition
o learning, adaptability
o generalization
o performance
o robustness
o uncertainty
o inconsistency
o scalability
o learning times
o formal properties (correctness, completeness)
o understandability
o modularity
4. What are the characteristics of application domains/tasks where NN-models and
methods are more suitable than other approaches (e.g. Inductive Logic
Programming) when dealing with structured knowledge?
5. Learning and generalization on a structured domain -- what does this mean?
Are there different levels of generalization capabilities, what can be achieved
by NN models?
6. Are any of the approaches relevant for cognitive processes, e.g. memory,
reasoning, language?
7. Is there evidence for the use of symbols in biological neural networks? When
and where do symbols appear?
8. How difficult is it to build larger systems? They may consist of several NNSK
modules, or constitute hybrid systems together with symbo-oriented modules.
9. Should we try to establish formal relations between neural methods and symbolic
methods? An example might be Hoelldobler and Kalinke's or Pinkas' work.
o equivalence
o transformation
o complexity
10. Should we try to model basic functions known from symbolic methods, or develop
neural ones from scratch? Or is it like trying to build flying machines modeled
after birds, instead of what we know as airplanes? An example: unification;
is it necessary for reasoning system, or might a radically different approach
be better?
11. What are the relations between knowledge-based methods from the fields of neural
networks, machine learning, statistics?
PARTICIPATION AND REGISTRATION
------------------------------
A number of places are available for those who wish to attend the workshop
without doing an oral presentation. Potential attendees are requested to send
a statement of interest to the Workshop Chair (franz at cis.njit.edu).
Please note that attendees of workshops must register for the main ECAI
conference.
ORGANIZING COMMITTEE
--------------------
Franz Kurfess (chair) New Jersey Institute of Technology, Newark, USA
Daniel Memmi LEIBNIZ-IMAG, Grenoble, France
Andreas Kuechler University of Ulm, Germany
Arnaud Giacometti Universiti de Tours, France
CONTACT
-------
Prof. Franz Kurfess
Computer and Information Sciences Dept.
New Jersey Institute of Technology
Newark, NJ 07102, U.S.A.
Voice : +1/201-596-5767
Fax : +1/201-596-5767
E-mail: franz at cis.njit.edu
PROGRAM COMMITTEE
-----------------
Venkat Ajjanagadde - University of Minnesota, Minneapolis
Ethem Alpaydin - Bogazici University
Joan Cabestany - University of Catalunya
Joachim Diederich - Queensland University of Technology
Georg Dorffner - Universitaet Wien
C. Lee Giles - NEC Research Institute
Marco Gori - University of Florence
Melanie Hilario - University of Geneva (co-chair)
Steffen Hoelldobler - TU Dresden
Mirek Kubat - University of Ottawa
Wolfgang Maass - Technische Universitaet Graz
Ernst Niebur - John Hopkins University
Guenther Palm - University of Ulm
Lokendra Shastri - International Computer Science Institute, Berkeley
Hava Siegelman - Technion (Israeli Institue of Technology)
Alessandro Sperduti - University of Pisa (co-chair)
Chris J. Thornton - University of Sussex
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