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2011 | nr 27 Informatyka, technologie-społeczeństwo-zastosowania | 71-84
Tytuł artykułu

Metoda przekształcania planów reakcji na ryzyko w sieci Bayesa

Treść / Zawartość
Warianty tytułu
A Method for Transforming Risk Response Plans to Bayesian Networks
Języki publikacji
PL
Abstrakty
Celem niniejszej pracy jest opracowanie RRP2BN - metody przekształcania wiedzy zawartej w planach reakcji na ryzyko do postaci sieci Bayesa. Dzięki temu możliwe będzie przeprowadzanie analiz symulacyjnych, których nie da się przeprowadzić, korzystając z samych planów zarządzania ryzykiem. Metoda RRP2BN generuje sieci Bayesa zgodne ze szkieletem przyczynowo-skutkowym. Została ona zaimplementowana w postaci prototypu narzędzia informatycznego. Wstępne wyniki badań potwierdzają, że metoda może ułatwić i przyspieszyć początek procesu budowy sieci Bayesa, szczególnie ekspertom dziedzinowym bez doświadczenia w korzystaniu z sieci Bayesa czy podobnych metod.(fragment tekstu)
EN
Bayesian networks (BNs) have been successfully used in various fields, including software engineering and project management. One of the main obstacles preventing them from a wider use is a difficult and time-consuming process of building models. To partially solve this problem, we proposed an RRP2BN method, helping inexperienced users to start model-building process. This method generates BNs, both the structure and probability tables, from risk response plans. Resulting BN models have the most important variables and relationships defined. Then, a domain expert domain expert should adjust and extend them to make them ready to use in simulations.(original abstract)
Twórcy
  • Uniwersytet Szczeciński
Bibliografia
  • A guide to the project management body of knowledge, Third Edition, Project Management Institute, Newtown Square, PA 2004.
  • Bayes T., An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F.R.S. communicated by Mr. Price, in a letter to John Canton, A.M.F.R.S., "Philosophical Transactions of the Royal Society of London" 1763, vol. 53.
  • Chapman C., Ward S., Project risk management. Second edition, John Wiley and Sons, Chichester 2003.
  • Darwiche A., Modeling and reasoning with Bayesian networks, Cambridge University Press, Cambridge 2009.
  • Das B., Generating conditional probabilities for Bayesian networks: easing the knowledge acquisition problem, http://www.citebase.org/cgi-bin/citations?id=oai:arXiv. org:cs/0411034, 2004.
  • Fenton N., Hearty P., Neil M., Radliński Ł., Software project and quality modelling using Bayesian networks, w: Artificial intelligence applications for improved software engineering development: new prospects, ed. F. Meziane, S. Vadera, Information Science Reference 2009.
  • Fenton N., Neil M., Measuring your risks, Agena, www.agenarisk.com, 2005.
  • Fenton N., Neil M., Visualising your risks, Agena, www.agenarisk.com, 2005.
  • Fenton N.E., Neil M., Caballero J.G., Using ranked nodes to model qualitative judgments in Bayesian networks, "IEEE Transactions on Knowledge and Data Engineering" 2007, vol. 19, no. 10.
  • Helsper E.M., van der Gaag L.C., Feelders A.J., Loeffen W.L.A., Geenen P.L., Albers A.R.W., Bringing order into Bayesian-network construction, Proceedings 3rd Int. Conf. on Knowledge Capture, Banff, Alberta, Canada 2005.
  • Helsper E.M., van der Gaag L.C., Groenendaal F., Designing a Proceedingsedure for the acquisition of probability constraints for Bayesian networks, "Engineering Knowledge in the Age of the Semantic Web", Springer-Verlag, Berlin-Heidelberg 2004.
  • Kraaijeveld P., Druzdzel M., Onisko A., Wasyluk H., GeNIeRate: An interactive generator of diagnostic Bayesian network models, Working Notes of the 16th International Workshop on Principles of Diagnosis (DX-05), Monterey, CA 2005.
  • Nadkarni S., Shenoy P.P., A causal mapping approach to constructing Bayesian networks, "Decision Support Systems" 2004, vol. 38, no. 2.
  • Neil M., Fenton N., Nielsen L., Building large-scale Bayesian networks, "Knowledge Engineering Review" 2000, vol. 15, no. 3.
  • Pfautz J., Cox Z., Catto G., Koelle D., Campolongo J., Roth E., User-centered methods for rapid creation and validation of Bayesian belief networks, Proceedings 5th Bayesian Modeling Applications Workshop, 23rd Annual Conference on Uncertainty in Artificial Intelligence, Vancouver, British Columbia 2007.
  • Radliński Ł., Building Bayesian nets for software defect prediction - a comparison of manual, semi- and fully-automated schemes, w: Information systems architecture and technology. New Developments in Web-Age Information Systems, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 2010.
  • Radliński Ł., Fenton N., Causal risk framework for software projects, w: Information systems architecture and technology. IT technologies in knowledge oriented management Proceess, ed. Z. Wilimowska, L. Borzemski, A. Grzech, J. Świątek, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 2009.
  • Radliński Ł., Fenton N., Neil M., Marquez D., Improved decision-making for software managers using Bayesian networks, Proceedings. 11th IASTED International Conference Software Engineering and Applications, Cambridge, MA 2007.
  • Radlinski L., Improved software project risk assessment using Bayesian nets, Ph.D. Thesis, Queen Mary, University of London, London 2008.
  • Radliński Ł., On generating Bayesian nets from small local qualitative data for software development effort and quality prediction, "Metody Informatyki Stosowanej" 2011 (w druku).
  • Radliński Ł., Software development effort and quality prediction using Bayesian nets and small local qualitative data, Proceedings. 22nd International Conference on Software Engineering and Knowledge Engineering, Redwood City, CA 2010.
  • Skaanning C., A knowledge acquisition tool for Bayesian-network troubleshooters, Proceedings. 16th Conference on Uncertainty in Artificial Intelligence, Stanford University, Stanford, CA 2000.
  • Wiegmann D.A., Developing a methodology for eliciting subjective probability estimates during expert evaluations of safety interventions: application for Bayesian belief networks, Rep. No. AHFD-05-13/NASA-05-4, Aviation Human Factors Division, Institute of Aviation, University of Illinois 2005.
Typ dokumentu
Bibliografia
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