Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture

Paulo Shakarian, Gerardo I. Simari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While deep neural networks have led to major advances in image recognition, language translation, data mining, and game playing, there are well-known limits to the paradigm such as lack of explainability, difficulty of incorporating prior knowledge, and modularity. Neuro symbolic hybrid systems have recently emerged as a straightforward way to extend deep neural networks by incorporating ideas from symbolic reasoning such as computational logic. In this paper, we propose a list desirable criteria for neuro symbolic systems and examine how some of the existing approaches address these criteria. We then propose an extension to generalized annotated logic that allows for the creation of an equivalent neural architecture comprising an alternate neuro symbolic hybrid. However, unlike previous approaches that rely on continuous optimization for the training process, our framework is designed as a binarized neural network that uses discrete optimization. We provide proofs of correctness and discuss several of the challenges that must be overcome to realize this framework in an implemented system.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 4th International Conference on Transdisciplinary AI, TransAI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages63-70
Number of pages8
ISBN (Electronic)9781665471848
DOIs
StatePublished - 2022
Event4th International Conference on Transdisciplinary AI, TransAI 2022 - Laguna Hills, United States
Duration: Sep 20 2022Sep 22 2022

Publication series

NameProceedings - 2022 4th International Conference on Transdisciplinary AI, TransAI 2022

Conference

Conference4th International Conference on Transdisciplinary AI, TransAI 2022
Country/TerritoryUnited States
CityLaguna Hills
Period9/20/229/22/22

Keywords

  • Logic programming
  • Machine learning
  • Neural networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Modeling and Simulation

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