NeSy'19 @ IJCAI

14th International Workshop on

Neural-Symbolic Learning and Reasoning

August 10, 11 or 12, 2019

***Submission Deadline: April 12 2019 (AOE)***

Submission Link: here

The NeSy workshop series celebrates the integration of connectionist and symbolic thinking, technologies, theories, and techniques in Artificial Intelligence systems.

NeSy is the annual workshop of the Neural-Symbolic Learning and Reasoning Association.

Statistical machine learning and connectionist systems have achieved industrial relevance in a number of areas, from retail to health, by their state-of-the-art performance in language modelling, speech recognition, graph analytics, image, video and sensor data analysis. Symbolic systems are challenged by unstructured data, but they are recognized as being in principle transparent, in that reasoned facts from knowledge bases can be inspected to interpret how decisions follow from input. Connectionist and symbolic methods also contrast in the problems they excel at: scene recognition from images appears to be a problem still outside the capabilities of symbolic systems for example, while neural nets are not yet sufficient for industrial-strength complex planning scenarios and deductive reasoning tasks.

Neural-symbolic computation aims to build rich computataional models and systems by combining both connectionist and symbolic learning paradigms. This combination hopes to form synergies between their strengths while overcoming their complementary weaknesses.

The NeSy workshop series is seen as a major venue for the presentation and discussion of key topics related to neural-symbolic computing. NeSy has provided an atmosphere for the free exchange of ideas and networking within the community of scientists that straddle the line between statistical and symbolic methods in AI.

Call For Papers

NeSy invites theoretical and applied submissions of all types that span both connectionist and symbolic learning paradigms. We further invite papers detailing experimental and in-the-wild neural-symbolic systems and papers on topics where neural-symbolic learning has a strong use case. Topics of interest include, but are not limited to, the following:

  • Knowledge representation and reasoning in (deep) neural networks
  • Symbolic knowledge extraction from neural and statistical learning models
  • Explainable AI models, systems, and techniques that integrate connectionist and symbolic paradigms
  • Neural-symbolic cognitive models
  • Biologically-inspired neural-symbolic integration
  • Continual learning, integration of logic and probabilities with neural networks
  • Neural-symbolic methods for structured learning tasks, including transfer, meta, and relational learning
  • Novel connectionist systems able to perform traditionally symbolic AI tasks (e.g., logical deduction)
  • Novel symbolic systems able to perform traditionally connectionist tasks (e.g., unstructured data analysis)
  • Applications in simulation, finance, robotics, the semantic web, software engineering, systems engineering, bioinformatics, and visual intelligence.

NeSy also invites short, clear, and well-written position papers for presentation and discussion during the workshop.

Submission

Researchers and practitioners are invited to submit original papers using the IJCAI paper format 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, or 3 pages in the case of position papers (including figures, bibliography and appendices). All submitted papers will be judged based on their quality, relevance, originality, significance, and soundness. Submissions need not be double-blind.

Submit your paper via: EasyChair Submission Link

Presentation

Selected papers will 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. A presentation may selected for oral or poster presentation depending on the number of accepted papers and on the ideal presentation style for the submitted work.

Publication

Accepted papers regardless of presentation format will be published in official workshop proceedings, which will be distributed during the workshop. Authors of papers that are well-reviewed or incites interest during the workshop will be invited to submit a revised and extended version of their papers to the Journal of Applied Logics, College Publications.

NeSy'19 Keynote Speakers

Dorothy and Walter Gramm Professor of Engineering, Dartmouth

Dr. George Cybenko has made key research contributions in machine learning, information security, parallel processing and computational behavioral analysis, and discovered the universal approximation theorem for neural networks. He was the Founding Editor-in-Chief of IEEE Security & Privacy, which is currently the largest professional society publication focused on security. Professor Cybenko is a Fellow of the IEEE, has served on the Defense Science Board, the US Air Force Science Advisory Board and the US Army Cyber Institute Advisory Board. He was also Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign before joining Dartmouth. Cybenko received his BS (University of Toronto) and PhD (Princeton) degrees in Mathematics.

Important Dates

Submission deadline: Apr 12, 2019

Notification: May 10, 2019

Camera-ready paper due: May 24, 2019

Workshop: TBA [Aug 10, 11 or 12]

IJCAI-19 conference: Aug 10-16, 2019

2019 Workshop Organizers

  • Artur d'Avila Garcez, City, University of London, UK
  • Freddy Lecue, INRIA and Thales, Montreal, Canada
  • Derek Doran, Wright State University, USA

NeSy History and Past Proceedings

Program Committee

  • Tarek Besold, Alpha Health AI Lab, Telefonia
  • George Cybenko, Dartmouth
  • Derek Doran, Wright State University
  • Artur d'Avila Garcez, City University of London
  • Fran Van Harmelen, Vrije Universiteit Amsterdam
  • Irina Higgins, Google
  • Pascal Hitzler, Wright State University
  • Kristan Kersting, TU Darmstadt
  • Luis Lamb, Federal University of Rio Grande do Sul
  • Freddy Lecue, INRIA and Thales
  • Thomas Lukasiewicz, University of Oxford
  • Pasquale Minervini, University College London
  • Amina Shabbeer, Amazon
  • Tran Son, University of Tasmania
  • Michael Spranger, Sony Computer Science Labs