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A Study on Big Data and Artificial Intelligence Techniques in Agricultural Sector
Published in R. Sujatha, S. L. Aarthy, R. Vettriselvan, Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics, 2021
The Expert System works as follows:Analyze the characteristics of the problem.Knowledge engineers and domain experts should work in unity to define the problem.Knowledge acquisition is an essential part of the expert system. The main aim is to extract the required knowledge from the human expert and to convert the acquired knowledge into rules. The developed rules are stored in the knowledge base.The knowledge is converted into computer-understandable knowledge from the knowledge engineer. The interface engine and reasoning structure are designed by the knowledge engineer to access knowledge whenever needed.The knowledge expert integrates uncertain knowledge into the reasoning process to give useful explanations (Figure 1.6).
Development of a DSS: Main Considerations and Framework
Published in Nebojša Kukurić, Development of a Decision Support System for Groundwater Pollution Assessment, 2020
Problem definition is crucial, because it determines not only the scope of the DSS knowledge component, but also the purpose and content of the DSS as a whole. Therefore it asks for teamwork between managers and specialists from various fields because of the interdisciplinary character of groundwater problems. The same holds for knowledge acquisition and systematisation. These two steps are partially overlapping, because some (at least preliminary) structuring is made during the acquisition, and that is used as a basis for the further acquisition (see Chapter 6). It seems that available AI knowledge acquisition techniques and procedures are suitable primarily for acquisition of knowledge from narrow, well-defined knowledge domains. Subsequent systematisation and formalisation of knowledge coming from these domains lead to application of standard knowledge representation forms. Contemporary DSSs can, however, accommodate knowledge encapsulated in various ways (e.g. hypertext-based software – see below). Besides, acquisition of large quantities of semi-structured, interdisciplinary knowledge asks not for ‘classical’ interviews with experts (classical acquisition), but for their intensive and interactive cooperation. Probably the most important postulate of that cooperation is ‘consensus’; unfortunately, it often has to be reached about issues that are considered as a ‘common knowledge’ (thus not about ‘frontiers of the known’ i.e. current research issues).
Expert systems
Published in Janet Finlay, Alan Dix, An Introduction to Artificial Intelligence, 2020
So we have examined our candidate problem and decided that an expert system would be an appropriate solution; what next? Assuming that we have considered our domain of interest carefully and defined the boundaries of the expert system, our first and most crucial stage is knowledge acquisition. Knowledge acquisition is the process of getting information out of the head of the expert or from the chosen source and into the form required by the expert system. We can identify two phases of this process: knowledge elicitation, where the knowledge is extracted from the expert, and knowledge representation, where the knowledge is put into the expert system. We considered the latter in Chapter 1. Here we will look briefly at knowledge elicitation.
(AIAM2019) Artificial Intelligence in Software Engineering and inverse: Review
Published in International Journal of Computer Integrated Manufacturing, 2020
Mohammad Shehab, Laith Abualigah, Muath Ibrahim Jarrah, Osama Ahmad Alomari, Mohammad Sh. Daoud
Second, new versions of the software are generated by integrating AI and SE, which leads to many possibilities. Figure 5 shows an overview of each field with the interaction area between them. AI includes knowledge acquisition, domain modeling, and data analysis techniques. SE contains project management methods, requirements engineering, and code engineering. The intersection area between AI and SE includes KBS, AOSE, CI, and ambient intelligence.
Development of an expert system for demand management process
Published in International Journal of Computer Integrated Manufacturing, 2018
ESs or knowledge-based systems are programmes that emulate this decision-making process instead of a human expert. They are computer programs embodying knowledge about a narrow domain for solving problems related to that domain. Figure 1 illustrates the basic concept of a knowledge-based ES. Knowledge acquisition involves the acquisition of knowledge from human experts, books, documents, sensors or computer files (Turban, Aronson, and Liang 2005).
Developing a knowledge-based system for diagnosis and treatment recommendation of neonatal diseases
Published in Cogent Engineering, 2023
Desalegn Wendimu, Kindie Biredagn
Knowledge Acquisition: In the artificial intelligence field, knowledge acquisition and representation are important activities in knowledge-based systems development. The knowledge gained during the first stages of the development of knowledge-based systems has determined the success of the intelligent system (Mohammad & Al Saiyd, 2010).