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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

征稿信息

重要日期

2018-08-01
初稿截稿日期

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.
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重要日期
  • 会议日期

    08月23日

    2018

    08月24日

    2018

  • 08月01日 2018

    初稿截稿日期

  • 08月24日 2018

    注册截止日期

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