Optics and symbolic computing

Brian Harvey's Computer Science Logo Style (Volume 1: Symbolic Computing, Volume 2: Advanced Techniques, Volume 3: Beyond Programming) begins with the words: "This book isn't for everyone." There follows a brief account of the fact that not everyone needs to program computers, based on an economic (Marxist-flavored) tirade (that I mostly agree with). The closing of the introductory paragraphs is the part that matters, though: "This book is for people who are interested in computer programming because it's fun."

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MASA: a multithreaded processor architecture for parallel symbolic computing

[1988] The 15th Annual International Symposium on Computer Architecture. Conference Proceedings ◽

10.1109/isca.1988.5255 ◽

2003 ◽

Cited By ~ 46

Author(s):

R.H. Halstead ◽

T. Fujita

Keyword(s):

Processor Architecture ◽

Symbolic Computing ◽

Multithreaded Processor

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Problem Solving Environments and Symbolic Computing

Enabling Technologies for Computational Science ◽

10.1007/978-1-4615-4541-5_8 ◽

2000 ◽

pp. 91-102

Author(s):

Richard J. Fateman

Keyword(s):

Problem Solving ◽

Symbolic Computing ◽

Problem Solving Environments

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Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/679 ◽

2020 ◽

Cited By ~ 2

Author(s):

Luís C. Lamb ◽

Artur d’Avila Garcez ◽

Marco Gori ◽

Marcelo O.R. Prates ◽

Pedro H.C. Avelar ◽

...

Keyword(s):

Neural Networks ◽

Constraint Satisfaction ◽

State Of The Art ◽

Relational Reasoning ◽

Widespread Application ◽

Symbolic Computing ◽

Industry Research ◽

Optimization Constraint ◽

The Subject ◽

Graph Neural Networks

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNNs) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as their relationship to current developments in neural-symbolic computing.

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