From belief revision to design revision: Applying theory change to changing requirements

  • C. K. MacNish
  • M. A. Williams
Reasoning with Changing and Incomplete Information
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1359)


The ability to correctly analyse the impact of changes to system designs is an important goal in software engineering. A framework for addressing this problem has been proposed in which logical descriptions are developed alongside traditional representations. While changes to the resulting design have been considered, no formal framework for design change has been offered.

This paper proposes such a framework using techniques from the field of belief revision. It is shown that under a particular strategy for belief revision, called a maxi-adjustment, design revisions can be modelled using standard revision operators.

As such, the paper also offers a new area of application for belief revision. Previous attempts to apply belief revision theory have suffered from the criticism that deduced information is held on to more strongly than the facts from which it is derived. This criticism does not apply to the present application because we are concerned with goal decomposition rather than reasoning from facts, and it makes sense that goals should be held onto more strongly than the decompositions designed to achieve them.


Partial Order Belief Revision Local Support Goal Structure Global Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    C. Alchourron, P. Gärdenfors, and D. Makinson. Partial meet contraction and revision functions. J. Symbolic Logic, 50:510–530, 1985.zbMATHMathSciNetCrossRefGoogle Scholar
  2. [2]
    J. Doyle. A truth maintenance system. Artificial Intelligence, 12:231–272, 1979.MathSciNetCrossRefGoogle Scholar
  3. [3]
    D. Duffy, C. MacNish, J. McDermid, and P. Morris. A framework for requirements analysis using automated reasoning. In J. Evari, K. Lyytinen, and M. Rossi, editors, Advanced Information Systems Engineering: Proc. Seventh International Conference, volume LNCS-932, pages 61–81. Springer-Verlag, 1995.Google Scholar
  4. [4]
    P. Gärdenfors and D. Makinson. Revisions of knowledge systems using epistemic entrenchment. In Second Conference on Theoretical Aspects of Reasoning about Knowledge, pages 822–830, 1988.Google Scholar
  5. [5]
    A. Grove. Two modellings for theory change. Journal of Philosophical Logic, 17:157–170, 1988.zbMATHMathSciNetCrossRefGoogle Scholar
  6. [6]
    H. Katsuno and A. Mendelzon. On the difference between updating a knowledge base and revising it. In P. Gärdenfors, editor, Belief Revision, pages 183–203. Cambridge University Press, 1992.Google Scholar
  7. [7]
    M. Osborne and C. K. MacNish. Processing natural language software requirement specifications. In Proc. ICRE'96: 2nd IEEE International Conference on Requirements Engineering, pages 229–236. IEEE Press, 1996.Google Scholar
  8. [8]
    P. Peppas and M.-A. Williams. Constructive modelings for theory change. Notre Dame Journal of Formal Logic, 36(1):120–133, 1995.zbMATHMathSciNetCrossRefGoogle Scholar
  9. [9]
    E. Williams. 1st DTI/SERC Proteus Project Workshop: Understanding Changing Requirements. Address from industrial participants, 1993.Google Scholar
  10. [10]
    M.-A. Williams. Two operators for theory bases. In Proc. Australian Joint Artificial Intelligence Conference, pages 259–265. World Scientific, 1992.Google Scholar
  11. [11]
    M.-A. Williams. Iterated theory base change: A computational model. In Proc. Fourteenth International Joint Conference on Artificial Intelligence, pages 1541–1550. Morgan Kaufmann, 1995.Google Scholar
  12. [12]
    M.-A. Williams. Towards a practical approach to belief revision: Reason-based change. In Proc. Fifth International Conference on Principles of Knowledge Representation and Reasoning. Morgan Kaufmann, 1996.Google Scholar
  13. [13]
    M.-A. Williams. Anytime belief revision. In Proc. Fifteenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1997 (in press).Google Scholar
  14. [14]
    M.-A. Williams, M. Pagnucco, N. Foo, and B. Sims. Determining explanations using transmutations. In Proc. Fourteenth International Joint Conference on Artificial Intelligence, pages 822–830. Morgan Kaufmann, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • C. K. MacNish
    • 1
  • M. A. Williams
    • 2
  1. 1.Department of Computer ScienceThe University of Western AustraliaNedlandsAustralia
  2. 2.Department of ManagementThe University of NewcastleNewcastleAustralia

Personalised recommendations