IJCAI-07

NeSy'07
Third International Workshop on
Neural-Symbolic Learning and Reasoning

Workshop at IJCAI-07, Hyderabad, India
January 8th, 2007

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.

Proceedings available online

Artur S. d'Avila Garcez, Pascal Hitzler, Guglielmo Tamburrini (eds.), Proceedings of the IJCAI-07 Third International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'07, Hyderabad, India, January 2007. CEUR Workshop Proceedings, Vol. 230, 2007. ISSN 1613-0073.

Accepted Papers

Schedule (revised 21st of December 2006)

09:30 - 09:45 Opening
09.45 - 11.00 Keynote by Lokendra Shastri: A neural architecture for reasoning, decision-making, and episiodic memory: Taking a cue from the brain.
coffee break
11:30 - 12:00 Sebastian Bader, Steffen Hölldobler, Valentin Mayer-Eichberger: Extracting Propositional Rules from Feed-forward Neural Networks - A New Decompositional Approach
12:00 - 12:30 Rafael V. Borges, Luis C. Lamb, Artur S. d'Avila Garcez: Towards reasoning about the past in neural-symbolic systems
12:30 - 13:00 Orna Peleg, Zohar Eviatar, Larry Manevitz, Hananel Hazan: Using Neural Network Models to Model Cerebral Hemispheric Differences in Processing Ambiguous Words (slides (pdf))
lunch break
14:00 - 15:00 Keynote by Luc de Raedt: Statistical Relational Learning - A Logical Approach.
15:00 - 15:30 Florian Röhrbein, Julian Eggert, Edgar Körner: A Cortex-Inspired Neural-Symbolic Network for Knowledge Representation
coffee break
16:00 - 16:30 Sebastian Rudolph: Encoding Closure Operators into Neural Networks (slides (pdf))
16:30 - 17:00 Zhiwei Shi, Hong Hu, Zhongzhi Shi: A Bayesian Computational Cognitive Model
17:00 - 17:30 Discussion and Closing

Keynote talks

Lokendra Shastri, International Computer Science Institute, Berkeley, CA
A neural architecture for reasoning, decision-making, and episiodic memory: Taking a cue from the brain.
This talk will describe some recent results from, and the current state of, a long term research project on understanding the neural basis of knowledge representation, reasoning, decision-making, and memory. The talk will also discuss how the results of this work can be (and have been) mapped to AI systems and what I see as some of the key technical problems facing Neuro-Symbolic research.

Luc de Raedt, KU Leuven, Belgium
Statistical Relational Learning - A Logical Approach
In this talk I will briefly outline and survey some developments in the field of statistical relation learning, especially focussing on logical approaches. Statistical relational learning is a novel research stream within artificial intelligence that combines principles of relational logic, learning and probabilistic models. This endeavor is similar in spirit to the developments in Neural Symbolic Reasoning in that it attempts to integrate symbolic representation and reasoning methods with the advantages of subsymbolic representations. In the talk, I shall attempt to make this link more explicit and to present an overview of the state of the art in Statistical Relational Learning. This overview shall start by providing some background in logical approaches to learning (relational learning and inductive logic programming) and then extend it with probabilistic elements.

Call for Papers

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:

Submission

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 6 pages in the case of research and experience papers, and 2 pages in the case of position papers (including figures, bibliography and appendices) in IJCAI-07 format as described in the IJCAI-07 Call for Papers. 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 nesy@soi.city.ac.uk

Presentation

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. Please note that the number of participants will be strictly limited.

Publication

Accepted papers will be published in official workshop proceedings, which 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.

Important Dates

Deadline for submission: 22nd of September, 2006
Notification of acceptance: 23rd of October, 2006
Camera-ready paper due: 3rd of November, 2006
Workshop date: 8th of January, 2007
IJCAI-07 main conference dates: 6th of January 2007 to 12th of January, 2007.

Workshop Organisers

Artur d'Avila Garcez (City University London, UK)
Pascal Hitzler (University Karlsruhe, Germany)
Guglielmo Tamburrini (Università di Napoli, Italy)

Programme Committee

Artur d'Avila Garcez (City University London, UK)
Sebastian Bader (TU Dresden, Germany)
Howard Blair (Syracuse University, USA)
Dov Gabbay (Kings College London, UK)
Marco Gori (University of Siena, Italy)
Barbara Hammer (TU Clausthal, Germany)
Ioannis Hatzilygeroudis (University of Patras, Greece)
Pascal Hitzler (University of Karlsruhe, Germany)
Kai-Uwe Kühnberger (University of Osnabrück, Germany)
Luis Lamb (Federal University of Rio Grande do Sul, Brazil)
Vasile Palade (Oxford University, UK)
Anthony K. Seda (University College Cork, Ireland)
Lokendra Shastri (ICSI Berkeley, USA)
Jude W. Shavlik (University of Wisconsin-Madison, USA)
Ron Sun (Rensselaer Polytechnic Institute, USA)
Guglielmo Tamburrini (Università di Napoli Feredico II, Italy)
Stefan Wermter (University of Sunderland, UK)
Gerson Zaverucha (Federal University of Rio de Janeiro, Brazil)

Keynote speakers

Lokendra Shastri, International Computer Science Institute, Berkeley, CA
A neural architecture for reasoning, decision-making, and episiodic memory: Taking a cue from the brain.
This talk will describe some recent results from, and the current state of, a long term research project on understanding the neural basis of knowledge representation, reasoning, decision-making, and memory. The talk will also discuss how the results of this work can be (and have been) mapped to AI systems and what I see as some of the key technical problems facing Neuro-Symbolic research.

Luc de Raedt, KU Leuven, Belgium
Statistical Relational Learning - A Logical Approach
In this talk I will briefly outline and survey some developments in the field of statistical relation learning, especially focussing on logical approaches. Statistical relational learning is a novel research stream within artificial intelligence that combines principles of relational logic, learning and probabilistic models. This endeavor is similar in spirit to the developments in Neural Symbolic Reasoning in that it attempts to integrate symbolic representation and reasoning methods with the advantages of subsymbolic representations. In the talk, I shall attempt to make this link more explicit and to present an overview of the state of the art in Statistical Relational Learning. This overview shall start by providing some background in logical approaches to learning (relational learning and inductive logic programming) and then extend it with probabilistic elements.

Additional Information

General questions concerning the workshop should be addressed to nesy@soi.city.ac.uk.