A knowledge systems design is a layer of learning modeling approach that focuses on applying generic Artificial Intelligence (AI) application approaches to a specific category of problem-solving activities. The consequences of 3 major approaches of the architectural dimension for the development of Knowledge Engineering (KE) tools are discussed in this study. The methodology is shown by a hierarchy of KE tools to enable systems management and knowledge development at the architectural design dimension, as well as architecture for controlling uncertainty through reasoning about actions. This article discusses architecture-dimension tools for KE. Knowledge architecture is a way of tailoring conventional AI problem-solving strategies to a specific set of activities. Architecture describes a certain kind of issue solution (e.g., diagnostic or reconfiguration) at a theoretical dimension above the application, indicating which parts of a problem class are fundamental to the issue and which are integration artifacts. An information system's design is a partial model in which certain choices are taken ahead of time to accommodate specific job characteristics. Many medical diagnostic systems, for example, evaluate data from the bottom up to identify "triggered" illness hypotheses, and then create top-down tasks to gather evidence for and against the hypotheses. Although it may be executed in a number of ways, the "trigger and acquire proof" cycle is an essential aspect of any design for the domain of clinical imaging activities.
Keywords
Artificial Intelligence (AI), Knowledge Engineering (KE), Personal Construct Psychology (PCP)
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Aravindhan K
Aravindhan K
Department of Computer Science and Engineering, SNS College of Engineering, TamilNadu, India.
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Aravindhan K, "Knowledge and Information Management Tools in Architectural Dimensions", vol.1, no.1, pp. 058-066, January 2021. doi: 10.53759/0088/JBSHA202101008.
