{"title":"The epsilon-knowledge: an emerging complement of Machlup's types of disciplinary knowledge","authors":"I. Horváth","doi":"10.1017/S089006042200004X","DOIUrl":null,"url":null,"abstract":"Abstract Machlup used the words alpha, beta, and gamma to identify humanities, science, and social science as three distinct fields of academic learning and knowing, in addition to general knowledge. Gilles and Paquet identified a fourth type of disciplinary knowledge and labeled it as delta. This includes the knowledge of creative disciplines such as design, law, and economy. Since the time of these road-paving works, a lot has changed. In the last two decades, various concepts and manifestations of intellectualized engineered systems have appeared. A paradigmatic feature of these systems, exemplified by smart cyber-physical systems, is that they collect, infer, or extract massive amount of synthetic system knowledge (M-SSK) based on some pre-programmed human knowledge. The amount of this type of knowledge grows continuously. It can be aggregated on system level and on system of systems level. This paper argues that this aggregated M-SSK is not covered by the abovementioned four genres of knowledge. In fact, it represents a new genre. The conducted literature study underpins this claim. Therefore, the paper suggests dealing with it as a new genre, called epsilon-knowledge. Artificial intelligence, system engineering, cyber-physical systems, and knowledge engineering are the disciplines dealing with epsilon-knowledge. The paper refers to sympérasmology as the proper conceptual framework of studying this genre of knowledge.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1017/S089006042200004X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 2
Abstract
Abstract Machlup used the words alpha, beta, and gamma to identify humanities, science, and social science as three distinct fields of academic learning and knowing, in addition to general knowledge. Gilles and Paquet identified a fourth type of disciplinary knowledge and labeled it as delta. This includes the knowledge of creative disciplines such as design, law, and economy. Since the time of these road-paving works, a lot has changed. In the last two decades, various concepts and manifestations of intellectualized engineered systems have appeared. A paradigmatic feature of these systems, exemplified by smart cyber-physical systems, is that they collect, infer, or extract massive amount of synthetic system knowledge (M-SSK) based on some pre-programmed human knowledge. The amount of this type of knowledge grows continuously. It can be aggregated on system level and on system of systems level. This paper argues that this aggregated M-SSK is not covered by the abovementioned four genres of knowledge. In fact, it represents a new genre. The conducted literature study underpins this claim. Therefore, the paper suggests dealing with it as a new genre, called epsilon-knowledge. Artificial intelligence, system engineering, cyber-physical systems, and knowledge engineering are the disciplines dealing with epsilon-knowledge. The paper refers to sympérasmology as the proper conceptual framework of studying this genre of knowledge.
期刊介绍:
The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.