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Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning

Citation

Garcez, AD and Gori, M and Lamb, LC and Serafini, L and Spranger, M and Tran, SN, Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning, Journal of Applied Logics, 6, (4) pp. 611-631. ISSN 2631-9810 (2019) [Refereed Article]


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Copyright 2019 The Authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/

Official URL: https://www.collegepublications.co.uk/ifcolog/?000...

Abstract

Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels. However, concerns about interpretability and accountability of AI have been raised by influential thinkers. In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems. Neural-symbolic computing aims at integrating, as foreseen by Valiant, two most fundamental cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned. Neural-symbolic computing has been an active topic of research for many years, reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. In this paper, we survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning. We illustrate the effectiveness of the approach by outlining the main characteristics of the methodology: principled integration of neural learning with symbolic knowledge representation and reasoning allowing for the construction of explainable AI systems. The insights provided by neural-symbolic computing shed new light on the increasingly prominent need for interpretable and accountable AI systems.

Item Details

Item Type:Refereed Article
Keywords:neural-symbolic, neural networks, artificial intelligence, knowledge representation
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Adaptive Agents and Intelligent Robotics
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
UTAS Author:Tran, SN (Dr Son Tran)
ID Code:138157
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-26
Last Modified:2020-08-10
Downloads:3 View Download Statistics

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