Review and assessment of prior work on and future directions for gradient descent-trained expert systems

Jeremy Straub

doi:10.1117/12.2670838

15 June 2023 Review and assessment of prior work on and future directions for gradient descent-trained expert systems

Jeremy Straub

Proceedings Volume 12542, Disruptive Technologies in Information Sciences VII; 125420E (2023) https://doi.org/10.1117/12.2670838
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

This paper reviews prior work demonstrating the efficacy of a new artificial intelligence technique which is based on optimizing expert systems’ rule-fact networks. Systems of this type can learn from presented data and operations; however, they cannot learn any changes that ‘jump out of’ the human-created or validated pathways, ensuring that they don’t learn invalid or non-causal associations. This paper presents a review and assessment of the functionality provided by the base gradient descent-trained expert system, the functionality provided by an enhancement that facilitates automated network development, and several other enhancements. The benefits of each system variant are discussed.

Conference Presentation

Citation Download Citation

Jeremy Straub "Review and assessment of prior work on and future directions for gradient descent-trained expert systems", Proc. SPIE 12542, Disruptive Technologies in Information Sciences VII, 125420E (15 June 2023); https://doi.org/10.1117/12.2670838

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