A Computational Perspective on Neural-Symbolic Integration

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00375262" target="_blank" >RIV/68407700:21230/24:00375262 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3233/NAI-240672" target="_blank" >https://doi.org/10.3233/NAI-240672</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/NAI-240672" target="_blank" >10.3233/NAI-240672</a>

Result language

angličtina

Original language name

A Computational Perspective on Neural-Symbolic Integration

Original language description

Neural-Symbolic Integration (NSI) aims to marry the principles of symbolic AI techniques, such as logical reasoning, with the learning capabilities of neural networks. In recent years, many systems have been proposed to address this integration in a seemingly {{efficient}} manner. However, from the computational perspective, this is in principle impossible to do. Specifically, some of the core symbolic problems are provably hard, hence a general NSI system necessarily needs to adopt this computational complexity, too. Many NSI methods try to circumvent this downside by inconspicuously dropping parts of the symbolic capabilities while mapping the problems into static tensor representations in exchange for efficient deep learning acceleration. In this paper, we argue that the aim for a general NSI system, {properly} covering both the neural and symbolic paradigms, has important computational implications on the learning representations, the structure of the resulting computation graphs, and the underlying hardware and software stacks. Particularly, we explain how the currently prominent, tensor-based deep learning with static computation graphs is conceptually insufficient as a foundation for such general NSI, which we discuss in a wider context of established (statistical) relational and structured deep learning methods. Finally, we delve into the underlying hardware acceleration aspects and outline some promising computational directions toward fully expressive and efficient NSI.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA24-11664S" target="_blank" >GA24-11664S: Relational Reinforcement Learning for Science Acceleration</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neurosymbolic Artificial Intelligence

ISSN

2949-8732

e-ISSN

2949-8732

Volume of the periodical

1

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

1-12

UT code for WoS article

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EID of the result in the Scopus database

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