13th International Workshop on Neural-Symbolic Learning and Reasoning NeSy'18

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活动简介

Artificial Intelligence researchers continue to face huge challenges in their quest to develop truly intelligent systems. The recent developments in the field of neural-symbolic integration bring an opportunity to integrate well-founded symbolic artificial intelligence with robust neural computing machinery to help tackle some of these challenges.

The Workshop on Neural-Symbolic Learning and Reasoning is intended to create an atmosphere of exchange of ideas, providing a forum for the presentation and discussion of the key topics related to neural-symbolic integration.

组委会

Program Committee

Derek Doran, Wright State University, USA
Richard Evans, DeepMind, UK
Steffen Hoelldobler, TU Dresden, Germany
Kristian Kersting, TU Darmstadt, Germany
Luis Lamb, Universidade Federal do Rio Grande do Sul, Brazil
Edjard Mota, Federal University of Amazonas, Brazil
Steven Schockaert, Cardiff University, UK
Luciano Serafini, Fondazione Bruno Kessler, Italy
Daniel L. Silver, Acadia University, Canada
Michael Spranger, SONY CSL, Japan
Frank van der Velde, University of Twente, The Netherlands
Frank van Harmelen, VU Amsterdam, The Netherlands
Michael Witbrock, IBM Research AI, USA

Organizing committee

Tarek R. Besold (City, University of London, UK)
Artur d'Avila Garcez (City, University of London, UK)

征稿信息

重要日期

2018-08-01

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Researchers and practitioners are invited to submit original papers that have not been submitted for review or published elsewhere:

Authors of contributed papers are encouraged to use the LaTex article style, a 12pt font, and to submit a paper with no more than 12 pages plus references.

征稿范围

Topics of interest include but are 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.