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Component Selection with Fuzzy Decision Making
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-0396-1993
2018 (English)In: Procedia Computer Science, Elsevier B.V. , 2018, Vol. 126, p. 1378-1386Conference paper, Published paper (Refereed)
Abstract [en]

In many situations a decision maker (DM) would like to grade a component, or rank several components of the same type. Often a component type has many features, which are deemed as valuable by the DM. Other vital features are not known by the DM but are needed for the component to function. However, it should be possible to guide the DM to find the desired business solution, without putting a requirement of detailed knowledge of the component type on the DM. We propose a framework for component selection with the help of fuzzy decision making. The work is based on algorithms from fuzzy decision making, which we have adapted or extended. The framework was validated by practitioners, which found the framework useful. © 2018 The Author(s).

Place, publisher, year, edition, pages
Elsevier B.V. , 2018. Vol. 126, p. 1378-1386
Series
Procedia Computer Science, ISSN 1877-0509
Keywords [en]
component selection, fuzzy decision making, max-prod composition, minimization of regret, Knowledge based systems, Business solutions, Decision makers, Max-prod compositions, Decision making
National Category
Computer Sciences Software Engineering
Identifiers
URN: urn:nbn:se:bth-17357DOI: 10.1016/j.procS.2018.08.089ISI: 000525954400146Scopus ID: 2-s2.0-85056626463OAI: oai:DiVA.org:bth-17357DiVA, id: diva2:1266877
Conference
22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2018, 3 September 2018 through 5 September 2018
Note

open access

Available from: 2018-11-29 Created: 2018-11-29 Last updated: 2021-12-22Bibliographically approved
In thesis
1. Towards Intent-Driven Systems Based on Context Frames
Open this publication in new window or tab >>Towards Intent-Driven Systems Based on Context Frames
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this research project we investigate how machine actors can support the business intents (desired outcomes) of an enterprise via predictive execution flows, prescriptive execution flows, and bidirectional knowledge creation between human actors and machine actors. A context frame supports bidirectional knowledge creation via interventions and counterfactual analysis. An intent-driven system combines execution flows to obtain business intents, and a context frame is a component in these flows. 

Our aim is to develop theoretical frameworks supporting intent-driven systems and context frames, and to validate the components needed to realize such frame- works. 

We are using the design science framework as our research framework. During our design science study we have used the following research methods: systematic literature review, case study, quasi experiment, action research, and evaluation research. 

We have created theoretical frameworks supporting intent-driven systems, and context frames, and implemented needed functionality in the involved components. The framework supports knowledge creation and knowledge validation. The possibility of using the knowledge for predictive analysis, prescriptive analysis, and counterfactual analysis, makes it possible to obtain bidirectional knowledge creation between a human actor and a machine actor. This enables a context frame to be part of an intent-driven system which supports predictive, and prescriptive, executions flows. 

The produced artifacts provide answers to our research questions. These answers are a base for theoretical frameworks supporting intent-driven systems and context frames, and provide knowledge of how to construct the components needed to realize these frameworks. 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2021. p. 50
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2021:08
Keywords
business intent, knowledge creation, decision making, rule adherence, OODA-loop, causal models
National Category
Software Engineering
Research subject
Software Engineering
Identifiers
urn:nbn:se:bth-22178 (URN)978-91-7295-430-4 (ISBN)
Public defence
2021-12-10, J1630 + Zoom, Campus Gräsvik, Karlskrona, 09:30 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2021-10-08 Created: 2021-10-07 Last updated: 2021-11-23Bibliographically approved

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Silvander, Johan

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