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AI-Informed Analytics Cycle: Reinforcing Concepts
Published in Jay Liebowitz, Data Analytics and AI, 2020
Rosina O. Weber, Maureen P. Kinkela
The first wave AI methods can roughly be categorized as data-oriented, knowledge-oriented, natural language, and evolutionary, as shown in Figure 12.4. Data-oriented methods typically learn from data and characterize the subfield of AI called machine learning. Machine learning methods (Russell and Norvig, 2014) include neural networks, decision trees, naive Bayes and support vector machines, and so on. These methods require large data sets to work well. Knowledge-based methods include rule-based reasoning, constraint satisfaction, and ontologies. Case-based reasoning combines both knowledge-based and machine learning aspects. Knowledge-based methods require some type of knowledge engineering and maintenance. Natural language processing combines techniques to treat sentences and grammar (ibid.), whereas natural language understanding theorizes the existence of a language model that can be used to process natural language segments (Allen, 1995). An example of an evolutionary method is genetic algorithms (Russell and Norvig, 2014), which evolve solution representations. This type of method is usually combined with others for best results. It requires a reference of quality as a fitness function to guide the evolution.
Knowledge mapping and knowledge acquisition
Published in Jay Liebowitz, Knowledge Management: Learning from Knowledge Engineering, 2001
Knowledge acquisition is a critical part of knowledge engineering and knowledge management processes. The main focus is applying techniques to capture expertise and knowledge. Eliciting the set of facts and rules of thumb from experiential learning is one of the key bottlenecks in the knowledge engineering process. Some reasons for this difficulty include: What the expert assumes to be common sense may not be common sense to others.The knowledge engineering paradox — the more expert an individual, the more compiled the knowledge, and the harder it is to extract or elicit this knowledge.The knowledge engineer may misinterpret what the expert is saying.Human biases in judgment on the part of the expert and knowledge engineer may interfere with the knowledge being acquired for the knowledge base.A single knowledge elicitation session could result in many pages of knowledge elicitation transcripts, resulting in difficulty in organizing the knowledge acquired.
A Knowledge Elicitation Approach to the Measurement of Team Situation Awareness
Published in Eduardo Salas, Aaron S Dietz, Situational Awareness, 2017
Nancy J Cooke, Renée J Stout, Eduardo Salas
Knowledge engineering is the process of building a knowledge base required for intelligent software such as expert systems, intelligent tutors, and decision aids. One approach to knowledge-base creation has involved eliciting knowledge from a human domain expert. In this context, the goal has been to reveal the domain facts and rules (i.e., content) relevant to a particular task, a goal that has become more central for cognitive engineering in general and that echoes some of our objectives for the measurement of team SA. More detailed definitions of knowledge elicitation, along with historical development of the enterprise and associated issues, can be found in Cooke (1994, 1999) and Hoffman, Shadbolt, Burton, and Klein (1995).
Smart Innovation Engineering: Toward Intelligent Industries of the Future
Published in Cybernetics and Systems, 2018
Mohammad Maqbool Waris, Cesar Sanin, Edward Szczerbicki
Knowledge engineering is an engineering discipline that aims to solve complex problems, normally requiring a high level of human expertise, by integrating knowledge into computer systems (Feigenbaum and McCorduck 1983). It involves the use and application of several computer science domains such as artificial intelligence (AI), knowledge representation (KR), databases, and decision support systems. KE is primarily concerned with constructing a KBS. Knowledge engineers are interested in what technologies are needed to meet the enterprise’s KM needs. In developing KBS, the knowledge engineer must apply quality control and standards, plan and manage projects, and take into account technological, human, financial, and environmental constraints.
Knowledge reuse for decision aid in additive manufacturing: application on cost quotation support
Published in International Journal of Production Research, 2023
Qussay Jarrar, Farouk Belkadi, Remy Blanc, Kenan Kestaneci, Alain Bernard
Knowledge base system (KBS) is a kind of computer system that mimic human problem-solving methods by combining Knowledge engineering methods and tools. This system comprises three major components: an inference engine, a graphical user interface (GUI), and a knowledge base (KB) (Ghazy 2012). KB helps structure knowledge fragments to connect consistently the problem, context, and solutions descriptive elements. The inference engine generates new knowledge from an existing one, taking into consideration the user's needs. The GUI allows various users to, describe the knowledge, input their queries, and display the results (Lai, Huang, and Wang 2011).