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    Uthayakumar, C. and Sarukesi, K. 2011. 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011). p. 70.

    Keravnou, E.T. Dams, F. Washbrook, J. Hall, C.M. Dawood, R.M. and Shaw, D. 1994. Modelling diagnostic skills in the domain of skeletal dysplasias. Computer Methods and Programs in Biomedicine, Vol. 45, Issue. 4, p. 239.


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    KRAUSE, PAUL and GLOWINSKI, ANDRZEJ 1993. FORMAL SPECIFICATIONS AND MEDICAL DECISION SUPPORT SYSTEMS. Applied Artificial Intelligence, Vol. 7, Issue. 3, p. 237.


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  • The Knowledge Engineering Review, Volume 4, Issue 3
  • September 1989, pp. 189-204

Life cycles in software and knowledge engineering: a comparative review

  • Michael Wilson (a1), David Duce (a1) and Dan Simpson (a2)
  • DOI: http://dx.doi.org/10.1017/S0269888900005026
  • Published online: 01 July 2009
Abstract
Abstract

Progress in software engineering has led to system development following models of the system life cycle. These models incorporate the use of prototyping and formal methods of program verification. They are becoming supported by integrated project support environments and permit the planning and monitoring of software development projects.

In contrast, knowledge based systems (KBS) are developed using informal views of the system life cycle. Tools have been developed to support some stages of the life cycle in an undisciplined manner. The commercial use of KBS needs development projects to be planned and monitored. This requires methods and tools based on systematic life cycle models to be established for KBS.

This paper reviews the current state of life cycle approaches to software engineering and KBS development projects in order to provide a direction for the development of methodical KBS life cycle models.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

N Cercone and G McCalla , 1987. “What is knowledge representation” In: The Knowledge Frontier: Essays in the Representation of Knowledge N Cercone and G McCalla (Eds.) New York: Springer-Verlag. A review of knowledge representation techniques.

K L McGraw and M R Seale , 1988. “Knowledge elicitation with multiple experts: considerations and techniquesArtificial Intelligence Review 2(1) 3144.

L J Peters , 1980. “Software representation and composition techniquesProceedings of the IEEE 68(9) 1085–93.

J Wyatt , 1987. “The evaluation of clinical decision support systems: a discussion of the methodology in the ACORN project”. In: Proceedings of AIME '87, J Fox , M Fieschi and R Engelbrecht (Eds.), Berlin: Springer-Verlag.

C M Ashworth , 1988. “Structured systems analysis and design method (SSADM)Information and Software Technology 30(3) 153163. This paper outlines the stages of the standard software development method used for UK government projects.

B W Boehm , 1981. Software Engineering Economics Englewood Cliffs, NJ, USA: Prentice Hall. This is the standard text on the prediction of the time, effort and resources required for a software development project.

I Cambell , 1986. “PCTE proposal for a common tool interface”. In: Software Engineering Environments I Sommerville (Ed.), London: Peter Peregrinus on behalf of the Institution of Electrical Engineers. This is a description of the EEC sponsored interface for software development tools.

L Ford , 1987. “Artificial intelligence and software engineering: a tutorial introduction to their relationshipArtificial Intelligence Review 1(4) 255273. An introduction to the role of software engineering in KBS development.

D Partridge and Y Wilks , 1987. “Does AI have a methodology which is different from software engineering?Artificial Intelligence Review 1(2) 111121. A controversial defence of the differences between AI and software engineering methods.

J Breuker and B Wielinga , 1987. “Use of models in the interpretation of verbal data”. In: Knowledge Aquisition for Expert Systems: a Practical Handbook A L Kidd (Ed.), New York: Plenum Press. A description of the KADS knowledge acquisition phase.

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The Knowledge Engineering Review
  • ISSN: 0269-8889
  • EISSN: 1469-8005
  • URL: /core/journals/knowledge-engineering-review
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