= WEBPAGE =
https://sites.google.com/site/ijcnn2015nsncc/
= SCOPE =
Researchers in artificial intelligence and cognitive systems modelling continue to face foundational challenges in their quest to develop plausible models and implementations of cognitive capacities and intelligence in artificial systems. One of the methodological core issues is the question of the integration between sub-symbolic and symbolic approaches to knowledge representation, learning and reasoning in cognitively-inspired models.
Network-based approaches very often enable flexible tools which can discover and process the internal structure of (possibly large) data sets. They promise to give rise to efficient signal-processing models which are biologically plausible and optimally suited for a wide range of applications, whilst possibly also offering an explanation of cognitive phenomena of the human brain.
Still, the extraction of high-level explicit (i.e. symbolic) knowledge from distributed low-level representations thus far has to be considered a mostly unsolved problem.
In recent years, network-based models have seen significant advancement in the wake of the development of the new "deep learning" family of approaches to machine learning. Due to the hierarchically structured nature of the underlying models, these developments have also reinvigorated efforts in overcoming the neural-symbolic divide.
The aim of the special session is to bring together recent work developed in the field of network-based information processing in a cognition-related context, which bridges the gap between different levels of description and paradigms and which sheds light onto canonical solutions or principled approaches occurring in the context of neural-symbolic integration to modelling or implementing cognitively-inspired capacities in artificial systems.
Besides classical research work applying computational modelling methods to problems from cognitive psychology, computational neuroscience, artificial intelligence, and cognitive science, this session also explicitly addresses cognitively-inspired neural-symbolic approaches in more application-driven research as, e.g., technical cognitive systems, cognitive robotics, large knowledge bases and big data, etc.
= TOPICS =
We particularly encourage submissions related to the following non-exhaustive list of topics:
- new learning paradigms of network-based models addressing different knowledge levels
- biologically plausible methods and models
- integration of network models and symbolic reasoning
- cognitive systems using neural-symbolic paradigms
- extraction of symbolic knowledge from network-based representations
- applications and implementations of cognitively-inspired neural-symbolic approaches in technical systems and industry
- cognitively-inspired neural-symbolic techniques for large knowledge bases and big data
- challenging applications which have the potential to become benchmark problems
- visionary papers concerning the future of network approaches to cognitive modelling or the future role of neural-symbolic systems in applications
= DATES & SUBMISSIONS =
The deadlines for submissions, author feedback, etc. are bound to the normal IJCNN 2015 deadlines (and, thus, are also subject to the same changes and extensions).
The current schedule is:
- Paper submission due: January 15, 2015
- Paper review feedback: March 15, 2015
- Final papers due: April 15, 2015
For details on the submission process, formats, etc., please refer to the IJCNN 2015 Call for Papers ( http://www.ijcnn.org/call-for-papers ) and the IJCNN 2015 submission guidelines ( http://www.ijcnn.org/paper-submission ).
When submitting to the special session, please make sure to select the corresponding session topic during the submission process.
= SESSION CO-CHAIRS =
- Tarek R. Besold, Institute of Cognitive Science, University of Osnabrück, Germany
- Artur D'Avila Garcez, Department of Computer Science, City University London, UK
- Kai-Uwe Kühnberger, Institute of Cognitive Science, University of Osnabrück, Germany
- Terrence C. Stewart, Centre for Theoretical Neuroscience, University of Waterloo, Canada