Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems

Ritter, Noah; Straub, Jeremy

doi:10.3390/machines9120361

Cited by 3 publications

(1 citation statement)

References 66 publications

(84 reference statements)

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“…In [48], a hardware-based GDES (HGDES) implementation was introduced. This work built on prior work in developing hardware-based expert systems [49] and uses software training (using the GDES software that has been previously developed [50]) with hardware-based operations processing. HGDES is designed to be used as a decision-making system via interfacing using its input and output ports.…”

Section: Gdtes Hardware Implementationmentioning

confidence: 99%

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

Straub

2023

Disruptive Technologies in Information Sciences VII

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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.

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Section: Gdtes Hardware Implementationmentioning

confidence: 99%

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

Straub

2023

Disruptive Technologies in Information Sciences VII

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AI-Based Computer Vision Techniques and Expert Systems

Matsuzaka

Yashiro

2023

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Computer vision is a branch of computer science that studies how computers can ‘see’. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise ‘vision’ among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts’ brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to ‘acquire the tacit knowledge of experts’, which was not previously achievable with conventional expert systems. Machine learning ‘systematises tacit knowledge’ based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning.

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Initial Work on the Development of a Hardware-Based Gradient Descent Trained Expert System

Ahmed

Straub

2022

Systems

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Prior work has introduced a form of explainable artificial intelligence that is able to precisely explain, in a human-understandable form, why it makes decisions. It is also able to learn to make better decisions without potentially learning illegal or invalid considerations. This defensible system is based on fractional value rule-fact expert systems and the use of gradient descent training to optimize rule weightings. This software system has demonstrated efficacy for many applications; however, it utilizes iterative processing and thus does not have a deterministic completion time. It also requires comparatively expensive general-purpose computing hardware to run on. This paper builds on prior work in the development of hardware-based expert systems and presents and assesses the efficacy of a hardware implementation of this system. It characterizes its performance and discusses its utility and trade-offs for several application domains.

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Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems

Cited by 3 publications

References 66 publications

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

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

AI-Based Computer Vision Techniques and Expert Systems

Initial Work on the Development of a Hardware-Based Gradient Descent Trained Expert System

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