Portal:Neural Symbolic Learning and Reasoning

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Neural Symbolic Learning and Reasoning Project

Educational level: this is a research resource.

Attribution: User penninghlhd created this resource and is actively using it. Please coordinate future development with this user if possible.

Subject classification: this is an information technology resource.

Welcome to the Neural Symbolic Learning and Reasoning Project.

The project is hosted at the Artificial Intelligence Department of the School of Computer Science.

Project participants collaborate on research about integrating machine learning and symbolic reasoning using neural networks. Neural-symbolic computation is an interdisciplinary research area borrowing from computer science, artificial intelligence, neural computation, machine learning, computational logic, cognitive and neurosciences, psychology and philosophy.

The goal of this project is to come to a unified architecture that supports symbolic learning and reasoning using neural networks.

Topics of interest include: Network reasoning and inference, Reasoning about uncertainty, Learning from structured data, Statistical relational learning, Integrated reasoning and learning, Expressive reasoning with robust learning, Nonclassical models of computation, Computational theories of mind, Cognitive computation, abduction and analogy, Knowledge extraction from complex networks, Deep learning and reasoning, Combination of systems, Efficient implementations of integrated learning and reasoning.

Applications in large-scale data analysis problems including simulation and training, robotics, the web, multi-agent systems, fault diagnosis, bioinformatics, argumentation, normative systems, security, multi-modal learning, visual information processing, anomaly detection, fraud prevention.

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Quotations

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Research Topics

The project's main research topics are:

• Artificial Neural Networks

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Active Participants

Writing

The following people are active participants in this research project:

• Leo de Penning, MSc. - Research scientist at TNO, The Netherlands. Currently doing a PhD on Neural Symbolic Cognitive Agents
• "Artur d'Avila Garcez, PhD". - Reader at Department of Computing, City University London, United Kingdom. Co-organizer of the Neural Symbolic (NeSy) workshops.

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News and Events

News

General

• 14 July 2010 - Project started at Wikiversity!

Workshops on Neural-Symbolic Learning and Reasoning

• Sixth International Workshop on Neural-Symbolic Learning and Reasoning at AAAI-10
• Fifth International Workshop on Neural-Symbolic Learning and Reasoning at IJCAI-09
• Fourth International Workshop on Neural-Symbolic Learning and Reasoning at ECAI-08
• Third International Workshop on Neural-Symbolic Learning and Reasoning at IJCAI-07
• Second International Workshop on Neural-Symbolic Learning and Reasoning at ECAI-06
• First International Workshop on Neural-Symbolic Learning and Reasoning at IJCAI-05

Other Workshops

• Dagstuhl Seminar on Learning paradigms in dynamic environments, July 2010
• Dagstuhl Seminar on Perspectives of Recurrent Neural Networks - Models, Capacities, and Applications, January 2008

Tutorials and Courses at Summer Schools

• Integrating Logic Programs and Connectionist Systems at ESSLLI2008 Summer School
• Knowledge Technologies, Hybrid Approaches and Neural Networks at ICANN 2006 Conference
• Neural-symbolic learning and reasoning at IK2006 Summer School
• Connectionist Knowledge Representation and Reasoning at KI 2005 Conference
• Integrating Logic Programs and Connectionist Systems at ESSLLI2005 Summer School

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Things To Do...

Writing

The following topics are planned for the near future:

• Joint paper on perspectives of Neural Symbolic Computation
• Adding domain knowlegde to deep belief networks

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External Links and References

Links

• Neural Symbolic (NeSy) workshops
• Neural Symbolic (NeSy) mailinglist

Papers

Books

• Artur S. d'Avila Garcez, Luis C. Lamb and Dov M. Gabbay. Neural-Symbolic Cognitive Reasoning. Cognitive Technologies. Springer, 2008, ISBN 978-3-540-73245-7, 2008.
• Barbara Hammer, Pascal Hitzler (Eds). Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence, Vol. 77. Springer, 2007, ISBN 978-3-540-73953-1.

Journals

• Marco Gori, Barbara Hammer, Pascal Hitzler, Günter Palm, Perspectives and Challenges for Recurrent Neural Networks. Special Issue of the Elsevier Journal of Algorithms in Cognition, Informatics and Logic.
• A. S. d'Avila Garcez, D. M. Gabbay, S. Holldobler and J. G. Taylor (eds). Journal of Applied Logic, Volume 2(3), Special Volume on Neural-Symbolic Systems, Elsevier, September 2004.