Intelligence engineering framework

Juhani Hirvonen, Eija Kaasinen, Ville Kotovirta, Jussi Lahtinen, Leena Norros, Leena Salo, Mika Timonen, Teemu Tommila, Janne Valkonen, Mark van Gils, Olli Ventä

Research output: Book/ReportReportProfessional

Abstract

A number of advanced algorithms and mostly software-based technologies have been developed in recent decades in order to solve problems in complex technical systems. Examples that have been actively studied include machine learning, artificial intelligence, pattern recognition, neural networks, fuzzy logic, statistical methods, operation analysis and, most recently, sensor networks. The problem is that these techniques require considerable knowledge to be applied correctly, necessitating participation by skilled professionals. This makes applications expensive to design and maintain. There is therefore a common need for better engineering methods and tools. This paper describes start of the development of a systematic engineering discipline for algorithmic and knowledge-intensive intelligent systems and services. The rationale behind this idea is that advanced technologies and algorithms cannot be economically feasible unless standardised design practices, tools and system components are available. The focus of the research on the early stages of design gave rise to two issues of design reuse: 1) how to model the application's needs for intelligence and the features of potential solutions stored in solution libraries, and 2) how to help the designer search the libraries for solutions that provide the best match with the application needs.
Original languageEnglish
Place of PublicationEspoo
PublisherVTT Technical Research Centre of Finland
Number of pages48
ISBN (Electronic)978-951-38-7480-3
Publication statusPublished - 2010
MoE publication typeNot Eligible

Publication series

NameVTT Working Papers
PublisherVTT
No.140
ISSN (Electronic)1459-7683

Fingerprint

Intelligent systems
Fuzzy logic
Sensor networks
Pattern recognition
Artificial intelligence
Learning systems
Statistical methods
Neural networks

Keywords

  • semantics
  • modelling
  • design
  • intelligent systems
  • ontology
  • design pattern

Cite this

Hirvonen, J., Kaasinen, E., Kotovirta, V., Lahtinen, J., Norros, L., Salo, L., ... Ventä, O. (2010). Intelligence engineering framework. Espoo: VTT Technical Research Centre of Finland. VTT Working Papers, No. 140
Hirvonen, Juhani ; Kaasinen, Eija ; Kotovirta, Ville ; Lahtinen, Jussi ; Norros, Leena ; Salo, Leena ; Timonen, Mika ; Tommila, Teemu ; Valkonen, Janne ; van Gils, Mark ; Ventä, Olli. / Intelligence engineering framework. Espoo : VTT Technical Research Centre of Finland, 2010. 48 p. (VTT Working Papers; No. 140).
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Hirvonen, J, Kaasinen, E, Kotovirta, V, Lahtinen, J, Norros, L, Salo, L, Timonen, M, Tommila, T, Valkonen, J, van Gils, M & Ventä, O 2010, Intelligence engineering framework. VTT Working Papers, no. 140, VTT Technical Research Centre of Finland, Espoo.

Intelligence engineering framework. / Hirvonen, Juhani; Kaasinen, Eija; Kotovirta, Ville; Lahtinen, Jussi; Norros, Leena; Salo, Leena; Timonen, Mika; Tommila, Teemu; Valkonen, Janne; van Gils, Mark; Ventä, Olli.

Espoo : VTT Technical Research Centre of Finland, 2010. 48 p. (VTT Working Papers; No. 140).

Research output: Book/ReportReportProfessional

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T1 - Intelligence engineering framework

AU - Hirvonen, Juhani

AU - Kaasinen, Eija

AU - Kotovirta, Ville

AU - Lahtinen, Jussi

AU - Norros, Leena

AU - Salo, Leena

AU - Timonen, Mika

AU - Tommila, Teemu

AU - Valkonen, Janne

AU - van Gils, Mark

AU - Ventä, Olli

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AB - A number of advanced algorithms and mostly software-based technologies have been developed in recent decades in order to solve problems in complex technical systems. Examples that have been actively studied include machine learning, artificial intelligence, pattern recognition, neural networks, fuzzy logic, statistical methods, operation analysis and, most recently, sensor networks. The problem is that these techniques require considerable knowledge to be applied correctly, necessitating participation by skilled professionals. This makes applications expensive to design and maintain. There is therefore a common need for better engineering methods and tools. This paper describes start of the development of a systematic engineering discipline for algorithmic and knowledge-intensive intelligent systems and services. The rationale behind this idea is that advanced technologies and algorithms cannot be economically feasible unless standardised design practices, tools and system components are available. The focus of the research on the early stages of design gave rise to two issues of design reuse: 1) how to model the application's needs for intelligence and the features of potential solutions stored in solution libraries, and 2) how to help the designer search the libraries for solutions that provide the best match with the application needs.

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Hirvonen J, Kaasinen E, Kotovirta V, Lahtinen J, Norros L, Salo L et al. Intelligence engineering framework. Espoo: VTT Technical Research Centre of Finland, 2010. 48 p. (VTT Working Papers; No. 140).