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September 14 – 19 , 2014, Dagstuhl Seminar 14381

Neural-Symbolic Learning and Reasoning


Artur d'Avila Garcez (City University – London, GB)
Marco Gori (University of Siena, IT)
Pascal Hitzler (Wright State University – Dayton, US)
Luis C. Lamb (Federal University of Rio Grande do Sul, BR)

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Neural-symbolic computation aims at building rich computational models and systems through the integration of connectionist learning and sound symbolic reasoning [1,2]. Over the last three decades, neural networks were shown effective in the implementation of robust large-scale experimental learning applications. Logic-based, symbolic knowledge representation and reasoning have always been at the core of Artificial Intelligence (AI) research. More recently, the use of deep learning algorithms have led to notably efficient applications, with performance comparable to those of humans, in particular in computer image and vision understanding and natural language processing tasks [3,4,5]. Further, advances in fMRI allow scientists to grasp a better understanding of neural functions, leading to realistic neural-computational models. Therefore, the gathering of researchers from several communities seems fitting at this stage of the research in neural computation and machine learning, cognitive science, applied logic, and visual information processing. The seminar was an appropriate meeting for the discussion of relevant issues concerning the development of rich intelligent systems and models, which can, for instance integrate learning and reasoning or learning and vision. In addition to foundational methods, algorithms and methodologies for neural-symbolic integration, the seminar also showcase a number of applications of neural-symbolic computation.

The meeting also marked the 10th anniversary of the workshop series on neural-symbolic learning and reasoning (NeSy), held yearly since 2005 at IJCAI, AAAI or ECAI. The NeSy workshop typically took a day only at these major conferences, and it became then clear that given that the AI, cognitive science, machine learning, and applied logic communities share many common goals and aspirations it was necessary to provide an appropriately longer meeting, spanning over a week. The desire of many at NeSy to go deeper into the understanding of the main positions and issues, and to collaborate in a truly multidisciplinary way, using several applications (e.g. natural language processing, ontology reasoning, computer image and vision understanding, multimodal learning, knowledge representation and reasoning) towards achieving specific objectives, has prompted us to put together this Dagstuhl seminar marking the 10th anniversary of the workshop.

Further, neural-symbolic computation brings together an integrated methodological perspective, as it draws from both neuroscience and cognitive systems. In summary, neural-symbolic computation is a promising approach, both from a methodological and computational perspective to answer positively to the need for effective knowledge representation, reasoning and learning systems. The representational generality of neural-symbolic integration (the ability to represent, learn and reason about several symbolic systems) and its learning robustness provides interesting opportunities leading to adequate forms of knowledge representation, be they purely symbolic, or hybrid combinations involving probabilistic or numerical representations.

The seminar tackled diverse applications, in computer vision and image understanding, natural language processing, semantic web and big data. Novel approaches needed to tackle such problems, such as lifelong machine learning [6], connectionist applied logics [1,2], deep learning [4], relational learning [7] and cognitive computation techniques have also been extensively analyzed during the seminar. The abstracts, discussions and open problems listed below briefly summarize a week of intense scientific debate, which illustrate the profitable atmosphere provided by the Dagstuhl scenery. Finally, a forthcoming article describing relevant challenges and open problems will be published at the Symposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches at the AAAI Spring Symposium Series, to be held at Stanford in March 2015 [8]. This article also adds relevant content and a view of the area, illustrating its richness which may indeed lead to rich cognitive models integrating learning and reasoning effectively, as foreseen by Valiant [9].

Finally, we see neural-symbolic computation as a research area which reaches out to distinct communities: computer science, neuroscience, and cognitive science. By seeking to achieve the fusion of competing views it can benefit from interdisciplinary results. This contributes to novel ideas and collaboration, opening interesting research avenues which involve knowledge representation and reasoning, hybrid combinations of probabilistic and symbolic representations, and several topics in machine learning which can lead to both the construction of sound intelligent systems and to the understanding and modelling of cognitive and brain processes.


  1. Artur S. d'Avila Garcez, Luis C. Lamb, and Dov M. Gabbay, Neural-Symbolic Cognitive Reasoning. Cognitive Technologies, Springer, 2009.
  2. Barbara Hammer, Pascal Hitzler (Eds.): Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence 77, Springer 2007.
  3. D.C. Ciresan, U. Meier, J. Schmidhuber. Multi-column Deep Neural Networks for Image Classification. IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012.
  4. G.E. Hinton, S. Osindero, and Y. Teh, A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554, 2006.
  5. Abdel-rahman Mohamed, George Dahl & Geoffrey Hinton. Acoustic Modeling Using Deep Belief Networks. IEEE Transactions on Audio, Speech, and Language Processing. 20(1):14-22, 2012.
  6. D. Silver, Q. Yang, and L. Li, Lifelong machine learning systems: Beyond learning algorithms. Proceedings of the AAAI Spring Symposium on Lifelong Machine Learning, Stanford University, AAAI, March, 2013, pp. 4-55.
  7. Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan: ILP turns 20 - Biography and future challenges. Machine Learning, 86(1):3-23, 2012.
  8. Artur d'Avila Garcez, Tarek R. Besold, Luc de Raedt, Peter Foeldiak, Pascal Hitzler, Thomas Icard, Kai-Uwe Kuehnberger, Luis C. Lamb, Risto Miikkulainen, Daniel L. Silver. Neural-Symbolic Learning and Reasoning: Contributions and Challenges. Proceedings of the AAAI Spring Symposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Stanford, March 2015.
  9. L.G. Valiant, Knowledge Infusion: In Pursuit of Robustness in Artificial Intelligence. FSTTCS, pp. 415-422, 2008.
  Creative Commons BY 3.0 Unported license
  Artur d'Avila Garcez, Marco Gori, Pascal Hitzler, and Luis C. Lamb

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