Advertisement

Reasoning about Interactive Systems with Stochastic Models

  • G. Doherty
  • M. Massink
  • G. Faconti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2220)

Abstract

Several techniques for specification exist to capture certain aspects of user behaviour, with the goal of reasoning about the usability of the system and other human-factors related issues. One such approach is to encode a set of assumptions about user behaviour in a user model. A difficulty with this approach is that human behaviour is inherently nondeterministic; humans make errors, perform unexpected actions, and, taken individually, both the occurrence of errors and response times can be unpredictable. Such factors, however can be expected to follow probability distributions, and so an interesting possibility is to apply stochasticor probabilistictec hniques that allow the modelling of uncertainty in user models. Recently, a number of process algebra based approaches to specifying stochastic systems have been proposed and in this paper we examine the possibility of applying these stochastic modelling techniques to reasoning about performance aspects of interactive systems.

Keywords

Stochastic Model Model Check Movement Time Interactive System User Behaviour 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    H. Alexander. Structuring dialogues using CSP. In M. D. Harrison and H. W. Thimbleby, editors, Formal methods in Human Computer Interaction, pages 273–295. Cambridge University Press, 1990.Google Scholar
  2. 2.
    R. Alur, C. Courcoubetis, and D.L. Dill. Model-checking for probabilistic realtimesystems. In Automata, Languages and Programming: Proceedings of the18th ICALP, volume 510 of Lecture Notes in Computer Science, pages 115–136.Springer-Verlag, 1991.CrossRefGoogle Scholar
  3. 3.
    François Bérard. Vision par ordinateur pour l’interaction fortement couplée. PhD thesis, L’Université Joseph Fourier Grenoble I, 1999.Google Scholar
  4. 4.
    H. Bowman, J. W. Bryans, and J. Derrick. Analysis of a multimedia stream usingstochastic process algebra. InC. Priami, editor, Proceedings of 6th InternationalWorkshop on Process Algebras and Performance Modelling, Nice, France, pages51–69, September 1998.Google Scholar
  5. 5.
    H. Bowman, G. P. Faconti, and M. Massink. Specification and verification of mediaconstraints using UPPAAL. In P. Markopoulos and P. Johnson, editors, Proceedingsof the 5th Eurographics Workshop on Design, Specification, and Verification ofInteractive Systems, pages 261–277. Springer Wien, 1998.Google Scholar
  6. 6.
    J. Bryans, H. Bowman, and J. Derrick. A model checking algorithm for stochasticsystems. Technical Report 4-00, University of Kent at Canterbury, January 2000.Google Scholar
  7. 7.
    P. Cairns, M. Jones, and H. Thimbleby. Reusable usability analysis with markovmodels. ACM Transactions on Human-Computer Interaction, (in press), 2001.Google Scholar
  8. 8.
    S. Card, T. Moran, and A. Newell. The psychology of human computer interaction.Lawrence Erlbaum Associates, 1983.Google Scholar
  9. 9.
    P. R. D’Argenio, J-P. Katoen, and E. Brinksma. A stochastic automaton model andits algebraicapproac h. In Proceedings of 5th International Workshop on ProcessAlgebra and Performance Modelling, pages 1–17, 1997. CTIT Technical Report97-09.Google Scholar
  10. 10.
    A.M. Dearden and M.D. Harrison. Using executable interactor specifications to explore the impact of operator interaction error. InP. Daniel, editor, SAFECOMP97: Proceedings of the 16th International Conference Computer Safety, Reliabilityand Security, pages 138–147. Springer, 1997.Google Scholar
  11. 11.
    G. Doherty and M. Massink. Stochastic modelling of interactive systems withUML. TUPIS Workshop, UML 2000.Google Scholar
  12. 12.
    G. Doherty, M. Massink, and G. Faconti. Using hybrid automata to support humanfactors analysis in a critical system. Formal Methods in System Design, 19(2),September 2001.Google Scholar
  13. 13.
    M. Du and D. England. Temporal patterns for complex interaction design. In Johnson [25].Google Scholar
  14. 14.
    S. Gilmore and J. Hillston. The PEPA workbench: A tool to support a processalgebra-based approach to performance modelling. In Proceedings of the SeventhInternational Conference on Modelling Techniques and Tools for Computer PerformanceEvaluation, volume 794 of Lecture Notes in Computer Science, pages353–368. Springer-Verlag, 1994.CrossRefGoogle Scholar
  15. 15.
    S. Gnesi, D. Latella, and M. Massink. A stochastic extension of a behaviouralsubset of UML statechart diagrams. In L. Palagi and R. Bilof, editors, Fifth IEEEInternational High-Assurance Systems Engineering Symposium, pages 55–64. IEEE Computer Society Press, 2000.Google Scholar
  16. 16.
    M.D. Harrison, A.E. Blandford, and P.J. Barnard. The requirements engineeringof user freedom. InF. Paternó, editor, Proceedings of Eurographics Workshop on Design Specification and Verification of Interactive Systems, Italy, pages 181–194.Springer-Verlag, 1995.Google Scholar
  17. 17.
    F. Hartleb. Stochastic graph models for performance evaluation of parallel programsand the evaluation tool PEPP. In Proceedings of the QMIPS Workshop onFormalisms, Principles and State-of-the-art, Erlangen/Pommersfelden, Germany,number 14 in Arbeitsbericht Band 26, pages 207–224, March 1993.Google Scholar
  18. 18.
    H. Hermanns, J.P. Katoen, J. Meyer-Kayser, and M. Siegle. Tools and algorithmsfor the construction and analysis of systems. In Proceedings of TACAS 2000,volume 1785 of LNCS, pages 347–362. Springer-Verlag, 2000.Google Scholar
  19. 19.
    H. Hermanns, V. Mertsiotakis, and M. Siegle. TIPPtool: Compositional speci.-cation and analysis of markovian performance models. In Proceedings of CompterAided Verification (CAV) 99, volume 1633 of Lecture Notes in Computer Science,pages 487–490. Springer-Verlag, 1999.Google Scholar
  20. 20.
    J. Hillston. A Compositional Approach to Performance Modelling. DistinguishedDissertations in Computer Science. Cambridge University Press, 1996.Google Scholar
  21. 21.
    C.A.R. Hoare. Communicating Sequential Processes. Prentice-Hall International,1985.Google Scholar
  22. 22.
    E. Hollnagel. The phenotype of erroneous actions. International Journal of Man-Machine Studies, 39(1):1–32, July 1993.Google Scholar
  23. 23.
    R. Jagacinski, R.D. Repperger, M. Moran, S. Ward, and B. Glass. Fitts’ law and themicrostructure of rapid discrete movements. Journal of Experimental Psychology:Human Perception and Performance, 6(2):309–320, 1980.Google Scholar
  24. 24.
    R. Jain. The art of computer systems performance analysis: techniques for experimentaldesign, measurement, simulation, and modeling. Wiley, New York, 1991.Google Scholar
  25. 25.
    C. Johnson, editor. Proceedings of Design Specification and Verification of InteractiveSystems, Glasgow. Springer-Verlag, 2001.Google Scholar
  26. 26.
    G. Langolf, D. Chaffin, and J. Foulke. An investigation of Fitts’ law. Journal of Motor Behaviour, 8:113–128, 1976.CrossRefGoogle Scholar
  27. 27.
    I. Scott MacKenzie. Fitts’ law as a research and design tool in human-computerinteraction. Human Computer Interaction, 7:91–139, 1992.CrossRefGoogle Scholar
  28. 28.
    D. Navarre, P. Palanque, F. Paterno, C. Santoro, and R. Bastide. Tool suite forintegrating task and system models through scenarios. In Johnson [25].Google Scholar
  29. 29.
    P. Palanque and R. Bastide. Synergisticmo delling of tasks, users and systems usingformal specification techniques. Interacting with Computers, 9:129–153, 1997.CrossRefGoogle Scholar
  30. 30.
    J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of PlausibleInference. San Mateo, CA: Morgan Kaufmann, 1988.Google Scholar
  31. 31.
    G. Salvendy, editor. Handbook of Human Factors and Ergonomics. Wiley-Interscience, 2nd edition, 1997.Google Scholar
  32. 32.
    R. Schäfer and Thomas Weyrath. Assessing temporally variable user properties with dynamicba yesian networks. InA. Jameson, C. Paris, and C. Tasso, editors,User Modelling: Proceedings of the Sixth Internation Conference. Springer Wien New York, 1997.Google Scholar
  33. 33.
    A.D. Swain and H.E. Guttmann. Handbook of human reliability analysis withemphasis on nuclear power plant applications-final report. Technical Report NRC FIN A 1188 NUREG/CR-1278 SAND80-0200, Prepared for Division of FacilityOperations; Office of Nuclear Regulatory Research; US Nuclear RegulatoryCommission; Washington, D.C. 20555, August 1983.Google Scholar
  34. 34.
    M.Q.V. Turnell, A. Scaico, M.R.F. de Sousa, and A. Perkusich. Industrial user interfaceevaluation based on coloured petri nets modelling and analysis. In Johnson[25].Google Scholar
  35. 35.
    C. Ware and R. Balakrishnan. Reaching for objects in VR displays: Lag andframe rate. ACM Transactions on Human Computer Interaction, 1(4):331–356,December 1994.CrossRefGoogle Scholar
  36. 36.
    C.D. Wickens. Engineering Psychology and Human Performance. Charles E. Merrill Publishing Company, 1984.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • G. Doherty
    • 1
  • M. Massink
    • 2
  • G. Faconti
    • 2
  1. 1.Rutherford Appleton LaboratoryOxfordshireUK
  2. 2.Istituto CNUCEPisaItaly

Personalised recommendations