A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents

Leu, George; Abbass, Hussein A.

doi:10.1016/j.knosys.2016.02.012

Cited by 28 publications

(25 citation statements)

References 181 publications

(337 reference statements)

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“…Initial knowledge in the form of information about the FCM is generated in the “FCM mapping.” The tools used for FCM generation include the KA methods (questionnaire, worksheet, etc.) that were presented in Leu and Abbass (). In our study, we do not aim to develop methods for the FCM generation.…”

Section: The Proposed Esmentioning

confidence: 99%

“…A major multidisciplinary research effort undertaken by Leu and Abbass () classifies the methods of KA based on the degree of involvement of the human factor and the type of interaction between knowledge engineer (s) and domain expert (s) in human activity systems. The possible options for the automation of these interactions (up to complete replacement) form three main categories of methods: human agents, human‐inspired agents, and machine agents.…”

Section: Introductionmentioning

confidence: 99%

“…A major multidisciplinary research effort undertaken by Leu and Abbass (2016) classifies the methods of KA based on the degree of involvement of the human factor and the type of interaction between knowledge engineer (s) and domain expert (s) in human activity systems.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge discovery using maximization of the spread of influence in an expert system

Tselykh

Vasilev

et al. 2018

Expert Systems

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The problem of knowledge acquisition (KA) and the automation of this process is a bottleneck of expert systems (ESs) and remains a topical issue. The present study is an attempt to develop an ES in which inference attributes are formed on the basis of automated discovery of tacit knowledge. The structure of the KA module provides an opportunity to discover new knowledge from the cognitive representations of domain models. The algorithmic processing mechanism consists of three blocks: cluster analysis, influence analysis, and modelling of the equivalent graph. In the proposed expert system based on effective controls, a method of finding influences that highlights the main characteristics of the system is presented. These parameters are used as attributes for the further development of the rule base, expertise, or independent use in decision‐making. A qualitative decrease in user subjectivity is achieved using this algorithm and through elimination of the knowledge engineer from the cycle of KA, which transforms it into a discovery knowledge cycle. Quality assurance of the knowledge base is achieved using three approaches: system presentation, mathematical support, and automation.

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Section: The Proposed Esmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Knowledge discovery using maximization of the spread of influence in an expert system

Tselykh

Vasilev

et al. 2018

Expert Systems

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“…Prodan (2005) [17]; T. Sasaki (2016) [19]; P. J. Sher and P. Z. Yang (2005) [20]; A. Morozumi and F. J. Veiga (2016) [16]; G. Leu and H. Abbass (2016) [13]; F. Langot and M. Lemoine (2016) [12].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Furthermore, we noted that it is also supported by other factors -the use of information technology and constantly increasing rate of technological change. The pace of change in human activity significantly affects the acquisition of knowledge and the creation of a new category of the acquiring knowledge-based cognitive task analysis via exercise and as an autonomous knowledge-discovery as demonstrated in research of Leu and Abbass (2016) [13] in their paper concluding with the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Changes in research and development after crisis in selected countries

Hvizdová¹,

Mokrisova²,

Polačko³

2016

EA-XXI

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Abstract. This article deals with the value of research and development (R&D) indicators before, during and after the economic crisis of [2008][2009]. Higher R&D intensity and higher R&D manpower are found to be predictors of improved firm performance. On the example of four countries with various level of R&D, we try to show if crisis influences this area of economy in the selected countries, namely, Germany, Finland, Slovakia and the Czech Republic. We analysed the period of 2006-2014, as the indicators of the year 2015 are still not available, paying particular attention to Slovakia. For the analysis, such indicators were chosen: expenditures on research and development per inhabitant and as a per cent of GDP; number of university graduates; number of companies in high-technology sector and total high-tech trade (export and import) as a per cent of total trade. According to analysed indicators, the leading countries in research and development were Finland and Germany. Slovakia reached the worst results in expenditures to R&D. Another conclusion of our research was that the crisis does not cause significant changes in research and development area. Despite the fact that in the years 2008 and 2009 there were lower values of some R&D indicators compared to the other years, the crisis did not make a serious impact on analysed sphere. We agree with the conclusion of M. Mazzucato (2015) that there should be more debate about actual composition of investment; how to invest strategically in key areas, such as research and development, education and human capital formation that will increase gross domestic product (GDP) in the future (bringing the debt/GDP ratio down as a consequence); and how to engage in a debate about the direction of change so that such investments will result in growth which is not only smarter (innovation-led) but also inclusive and sustainable [15].The interest in the relation between technology and competitiveness dates back to the so-called neo-technological trade theories of the 1960s concerning technology gap, product cycle etc. (Dosi, Soete, 1988) [3]. Since these issues were first introduced by Posner (1961), Vernon (1966) (In: Fagerberg, 1996 [5] and others, economic theory has changed considerably. Trade theorists started to apply the insights from models of imperfectly competitive markets to the analysis of international trade and world-wide competitiveness. Differences across countries in the efficiency of R&D and other technological activities have also been emphasised by the recent literature on national systems of innovation (Fagerberg, 1996) [5], competition and competitiveness growth through the creation of innovative products and technologies [11], human capital quality, and the stability of the economic environment and the quality of businesses management. For example, Morozumi and Veiga (2016) [16] state that empirical results based on a newly assembled dataset of 80 countries over the 1970-2010 period of time suggest that particularly when institutions prompt governments to...

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2019

Simulation and Computational Red Teaming for Problem Solving

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A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents

Cited by 28 publications

References 181 publications

Knowledge discovery using maximization of the spread of influence in an expert system

Knowledge discovery using maximization of the spread of influence in an expert system

Changes in research and development after crisis in selected countries

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