[DL] 1st CfP: NIPS-Workshop "Cognitive Computation: Integrating Neural and Symbolic Approaches" (CoCo @ NIPS 2016)

Tarek R. Besold tarek.besold at googlemail.com
Sat Sep 3 15:16:00 CEST 2016


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Cognitive Computation:
 Integrating Neural and Symbolic Approaches
(CoCo @ NIPS 2016)
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Workshop at NIPS 2016, Barcelona, Spain
December 09, 2016



== WORKSHOP WEBPAGE ==

http://www.neural-symbolic.org/CoCo2016/



== MISSION STATEMENT ==

While early work on knowledge representation and inference was primarily
symbolic, the corresponding approaches subsequently fell out of favor, and
were largely supplanted by connectionist methods. In this workshop, we will
work to close the gap between the two paradigms, and aim to formulate a new
unified approach that is inspired by our current understanding of human
cognitive processing. This is important to help improve our understanding
of Neural Information Processing and build better Machine Learning systems,
including the integration of learning and reasoning in dynamic
knowledge-bases, and reuse of knowledge learned in one application domain
in analogous domains.

The workshop brings together established leaders and promising young
scientists in the fields of neural computation, logic and artificial
intelligence, knowledge representation, natural language understanding,
machine learning, cognitive science and computational neuroscience. Invited
lectures by senior researchers will be complemented with presentations
based on contributed papers reporting recent work (following an open call
for papers) and a poster session, giving ample opportunity for participants
to interact and discuss the complementary perspectives and emerging
approaches.

The workshop targets a single broad theme of general interest to the vast
majority of the NIPS community, namely translations between connectionist
models and symbolic knowledge representation and reasoning for the purpose
of achieving an effective integration of neural learning and cognitive
reasoning, called neural-symbolic computing. The study of neural-symbolic
computing is now an established topic of wider interest to NIPS with topics
that are relevant to almost everyone studying neural information processing.



== KEYWORDS ==

The following list gives some (but by far not all) relevant keywords for
the CoCo @ NIPS 2016 workshop:

- neural-symbolic computing;
- language processing and reasoning;
- cognitive agents;
- multimodal learning;
- deep networks;
- knowledge extraction;
- symbol manipulation;
- variable binding;
- memory-based networks;
- dynamic knowledge-bases;
- integration of learning and reasoning;
- explainable AI.



== CALL FOR PAPERS ==

We invite submission of papers dealing with topics related to the research
questions discussed in the workshop. The reported work can range from
theoretical/foundational research to reports on applications and/or
implemented systems.

We explicitly also encourage the submission of more controversial papers
which can serve as basis for open discussions during the event.

Possible topics of interest include but are (by far!) not limited to:
- The representation of symbolic knowledge by connectionist systems;
- Neural Learning theory;
- Integration of logic and probabilities, e.g., in neural networks, but
also more generally;
- Structured learning and relational learning in neural networks;
- Logical reasoning carried out by neural networks;
- Integrated neural-symbolic approaches;
- Extraction of symbolic knowledge from trained neural networks;
- Integrated neural-symbolic reasoning;
- Neural-symbolic cognitive models;
- Biologically-inspired neural-symbolic integration;
- Applications in robotics, simulation, fraud prevention, natural language
processing, semantic web, software engineering, fault diagnosis,
bioinformatics, visual intelligence, etc.
- Approaches/techniques making AI and/or Machine Learning
systems/algorithms better explainable or increasing human comprehensibility.


= Submission instructions =

- Submissions have to be made via EasyChair (
https://easychair.org/conferences/?conf=coconips2016) before the paper
submission deadline indicated below.
- Submissions are limited to at most eight pages, an additional ninth page
containing only cited references is allowed. Still, also shorter papers are
expressly welcomed.
- Submissions have to use the NIPS 2016 submission format (see
http://nips.cc/Conferences/2016/PaperInformation/StyleFiles).
- Reviewing will be single-blind, i.e., you are free to indicate your name
etc. on the paper. (Still, this is not an obligation.)

Please note that at least one author of each accepted paper must register
for the event and be available to present the paper at the workshop.


=Publication=

Accepted papers will be published in official workshop proceedings
submitted to CEUR-WS.org.

Authors of selected papers will be invited to submit a revised and extended
version of their papers to a journal special issue after the workshop.



== IMPORTANT DATES ==

- Deadline for paper submission: October 10, 2016
- Notification of paper acceptance: October 30, 2016
- Camera-ready paper due: November 14, 2016
- Workshop date: December 09, 2016
- NIPS 2015 main conference: December 5-8, 2016



== ADMISSION ==

The workshop is open to anybody, please register via NIPS 2016 (
http://nips.cc).



== WORKSHOP ORGANIZERS ==

- Tarek R. Besold (University of Bremen, Germany)
- Antoine Bordes (Facebook AI Research, USA)
- Artur d'Avila Garcez (City University London, UK)
- Greg Wayne (Google DeepMind, UK)



== ADDITIONAL INFORMATION ==

- General questions concerning the workshop should be addressed to Tarek R.
Besold at Tarek(dot)Besold(at)uni(hyphen)bremen(dot)de.
- This workshop is conceptually related to the series of International
Workshops on Neural-Symbolic Learning and Reasoning (NeSy). If interested,
have a look at http://www.neural-symbolic.org
- Please also feel free to join the neural-symbolic integration mailing
list for announcements and discussions - it's a low traffic mailing list.
If interested, register at http://maillists.city.ac.uk/mailman/listinfo/nesy
.
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