NeSy'05 took place
at IJCAI-05, Edinburgh,
Scotland, 1st of August 2005.
NeSy'06 took place at ECAI2006, Riva del Garda, Italy, 29th of August 2006.
NeSy'07 took place at IJCAI-07, Hydarabad, India, 8th of January 2007.
|09.00 - 10.00||Keynote by Kai-Uwe Kühnberger: Modeling Reasoning Mechanisms by Neural-Symbolic Learning.|
|10:30 - 10:50||Ekaterina Komendantskaya: Unification by Error-Correction|
|10:55 - 11:10||Matthew Cook: The Reusable Symbol Problem (Position Paper)|
|11:15 - 11:35||Claudine Brucks, Michael Hilker, Christoph Schommer, Cynthia Wagner, Ralph Weires: Symbolic Computing with Incremental Mind-maps to Manage and Mine Data Streams - Some Applications|
|11:40 - 12:00||Sebastian Bader, Steffen Hölldobler, Nuno C. Marques: Guiding Backprop by Inserting Rules|
|12:05 - 12:25||Tsvi Achler, Eyal Amir: Hybrid Classification and Symbolic-Like Manipulation Using Self-Regulatory Feedback Networks|
Kai-Uwe Kühnberger, Osnabrück: Modeling Reasoning Mechanisms by Neural-Symbolic Learning
Currently, neural-symbolic integration covers - at least in theory - a whole bunch of types of reasoning: neural representations (and partially also neural-inspired learning approaches) exist for modeling propositional logic (programs), whole classes of manyvalued logics, modal logic, temporal logic, and epistemic logic, just to mention some important examples [2,4]. Besides these propositional variants of logical theories, also first proposals exist for approximating infinity with neural means, in particular, theories of first-order logic. An example is the core method intended to learn the semantics of the single-step operator TP for first-order logic (programs) with a neural network . Another example is the neural approximation of variable-free first-order logic by learning representations of arrow constructions (which represent logical expressions) in the Rn using Topos constructions .
Although these examples show a certain success of neural-symbolic learning and reasoning research, there are several non-trivial challenges. First, there exist a variety of frameworks that seem to have rather different and seemingly incompatible foundations. Second, potential application domains where the strengths of neural-symbolic integration could be documented and its potential be shown are not really known. Third, the conceptual understanding of the cognitive relevance and the cognitive plausibility of neural-symbolic learning and reasoning should be clarified. In this talk, I will address these questions and propose some ideas for answers. I will sketch general assumptions of solutions for the neuralsymbolic modeling of a variety of logical reasoning mechanisms. Then I will propose some application domains where neural-symbolic frameworks can be successfully applied. I will finish the talk with some speculations concerning cognitively relevant implications and the degree of cognitive plausibility of neuralsymbolic learning and reasoning in general.
 S. Bader, P. Hitzler, S. Hölldobler and A. Witzel: A Fully Connectionist Model Generator for Covered First-Order Logic Programs. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, 2007, pp. 666-671.
 A. d'Avila Garcez, L. Lamb and D. Gabbay: Neural-Symbolic Cognitive Reasoning, Cognitive Technologies, Springer, 2008.
 H. Gust, K.-U. Kühnberger and P. Geibel: Learning Models of Predicate Logical Theories with Neural Networks based on Topos Theory. In P. Hitzler and B. Hammer (eds.): Perspectives of Neural-Symbolic Integration, Studies in Computational Intelligence (SCI) 77, Springer, 2007, pp. 233-264.
 E. Komendantskaya, M. Lane and A. Seda: Conenctionist Representation of Multi-Valued Logic Programs. In P. Hitzler and B. Hammer (eds.): Perspectives of Neuro-Symbolic Integration, Studies in Computational Intelligence (SCI) 77, Springer, 2007, pp. 283-313.
Artificial Intelligence researchers continue to face huge challenges in their quest to develop truly intelligent systems. The recent developments in the field of neural-symbolic integration bring an opportunity to integrate well-founded symbolic artificial intelligence with robust neural computing machinery to help tackle some of these challenges.
The Workshop on Neural-Symbolic Learning and Reasoning is intended to create an atmosphere of exchange of ideas, providing a forum for the presentation and discussion of the key topics related to neural-symbolic integration. Topics of interest include:
Researchers and practitioners are invited to submit original papers that have not been submitted for review or published elsewhere. Submitted papers must be written in English and should not exceed 5 pages in the case of research and experience papers, and 2 pages in the case of position papers (including figures, bibliography and appendices) in ECAI2008 format as described in the ECAI2008 submissions and style guide. All submitted papers will be judged based on their quality, relevance, originality, significance, and soundness. Papers must be submitted directly by email in PDF format to email@example.com
Selected papers will have to be presented during the workshop. The workshop will include extra time for audience discussion of the presentation allowing the group to have a better understanding of the issues, challenges, and ideas being presented.
Accepted papers will be published electronically in the CEUR workshop proceedings (bearing an ISSN number). Hardcopies will be distributed during the workshop. Authors of the best papers will be invited to submit a revised and extended version of their papers to the Journal of Logic and Computation, OUP.
Deadline for submission: May 16th, 2008
Notification of acceptance: May 30th, 2008
Camera-ready paper due: June 6th, 2008
Workshop date: July 21st, 2008
ECAI2008 main conference dates: 21st to 25th of July, 2008
Artur d'Avila Garcez (City University London, UK)
Pascal Hitzler (University Karlsruhe, Germany)
Sebastian Bader, TU Dresden, Germany
Howard Blair, Syracuse University, U.S.A.
Luc de Raedt, KU Leuven, Belgium
Marco Gori, University of Siena, Italy
Barbara Hammer, TU Clausthal, Germany
Ioannis Hatzilygeroudis, University of Patras, Greece
Steffen Hölldobler, TU Dresden, Germany
Ekaterina Komendantskaya, Sophia Antipolis, France
Kai-Uwe Kühnberger, Osnabrück, Germany
Luis Lamb, Federal University of Rio Grande do Sul, Brazil
Roberto Prevete, University of Naples, Italy
Dan Roth, University of Illinois at Urbana-Champaign, U.S.A.
Anthony K. Seda, University College Cork, Ireland
Frank van der Velde, Leiden University, The Netherlands
Gerson Zaverucha, Federal University of Rio de Janeiro, Brazil
Kai-Uwe Kühnberger, Osnabrück
General questions concerning the workshop should be addressed to firstname.lastname@example.org.