Gradient descent training expert system

Disruptive Technologies in Information Sciences VII

2023

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.

Section: Gdtes Hardware Implementationmentioning

confidence: 99%

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

Disruptive Technologies in Information Sciences VII

2023

“…Once the values are converted, they are supplied to the system. Procedurally, this is completed using a set fact (SF) command (see [ 45 ]). A 32-character globally unique identifier (GUID) is assigned to each fact and rule which is used for identifying nodes within the network when issuing commands.…”

Section: System Designmentioning

confidence: 99%

Assessment of the Use of Patient Vital Sign Data for Preventing Misidentification and Medical Errors

Maul

2022

Healthcare

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Patient misidentification is a preventable issue that contributes to medical errors. When patients are confused with each other, they can be given the wrong medication or unneeded surgeries. Unconscious, juvenile, and mentally impaired patients represent particular areas of concern, due to their potential inability to confirm their identity or the possibility that they may inadvertently respond to an incorrect patient name (in the case of juveniles and the mentally impaired). This paper evaluates the use of patient vital sign data, within an enabling artificial intelligence (AI) framework, for the purposes of patient identification. The AI technique utilized is both explainable (meaning that its decision-making process is human understandable) and defensible (meaning that its decision-making pathways cannot be altered, just optimized). It is used to identify patients based on standard vital sign data. Analysis is presented on the efficacy of doing this, for the purposes of catching misidentification and preventing error.

“…This article, thus, presents a easy-to-use Blackboard Architecture implementation, which is based on the versions developed and upgraded for [21][22][23][24][25]. The version presented herein includes an interface that has been specifically developed for facilitating the ease of use of the system (using command syntax that is similar to the command language developed for the gradient descent trained expert system presented in [26]). It also includes a generalized actuation capability which allows the system to make system calls (including command parameters).…”

Section: Introductionmentioning

confidence: 99%

A modern Blackboard Architecture implementation with external command execution capability

2022

Software Impacts

Self Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.