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.
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.
| 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 |
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.
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 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
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.
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.
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.
Artur d'Avila Garcez (City University London, UK)
Pascal Hitzler (University Karlsruhe, Germany)
Guglielmo Tamburrini (Università di Napoli, Italy)
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)
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.
General questions concerning the workshop should be addressed to nesy@soi.city.ac.uk.