Autonomous Agents and Multi-Agent Systems

, Volume 31, Issue 3, pp 656–695 | Cite as

A dynamic default revision mechanism for speculative computation

  • Tiago Oliveira
  • Ken Satoh
  • Paulo Novais
  • José Neves
  • Hiroshi Hosobe


In this work a default revision mechanism is introduced into speculative computation to manage incomplete information. The default revision is supported by a method for the generation of default constraints based on Bayesian networks. The method enables the generation of an initial set of defaults which is used to produce the most likely scenarios during the computation, represented by active processes. As facts arrive, the Bayesian network is used to derive new defaults. The objective with such a new dynamic mechanism is to keep the active processes coherent with arrived facts. This is achieved by changing the initial set of default constraints during the reasoning process in speculative computation. A practical example in clinical decision support is described.


Default revision Incomplete information Speculative computation Bayesian networks 

Supplementary material


  1. 1.
    Benson, A., Bekaii-Saab, T., Chan, E., Chen, Y. J., Choti, M., Cooper, H., et al. (2013). NCCN clinical practice guideline in oncology colon cancer. Technical Report. National Comprehensive Cancer Network.
  2. 2.
    Burton, F. W. (1985). Speculative computation, parallelism and functional programming. IEEE Transactions on Computers, 34(12), 1190–1193.CrossRefGoogle Scholar
  3. 3.
    Cellini, S. R., & Kee, J. E. (2010). Cost effectiveness and cost-benefit analysis. In Handbook of practical program evaluation (pp. 493–530). Wiley Online Library. doi: 10.1002/9781119171386.ch24.
  4. 4.
    Darwiche, A. (2010). Samiam—Sensitivity analysis, modelling, inference and more. Accessed from March 15, 2015, from
  5. 5.
    Franzén, T., Haridi, S., & Janson, S. (1992). An overview of the andorra kernel language. In L. H. Eriksson, L. HallnÃd’s, & P. Schroeder-Heister (Eds.), Extensions of logic programming (Vol. 596, pp. 163–179). Lecture notes in computer science. Berlin: Springer.Google Scholar
  6. 6.
    Governatori, G., Olivieri, F., Scannapieco, S., & Cristani, M. (2010). Superiority based revision of defeasible theories. In M. Dean, D. J. Hall, A. Rotolo, & S. Tabet (Eds.), Semantic web rules (Vol. 6403, pp. 104–118). Lecture notes in computer science. Berlin: Springer.Google Scholar
  7. 7.
    Gupta, V., Jagadeesan, R., & Saraswat, V. (1997). Probabilistic concurrent constraint programming. In A. Mazurkiewicz & J. Winkowski (Eds.), CONCUR’97: Concurrency theory (Vol. 1243, pp. 243–257). Lecture notes in computer science. Berlin: Springer.Google Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.CrossRefzbMATHGoogle Scholar
  9. 9.
    Hosobe, H., Satoh, K., & Codognet, P. (2007). Agent-based speculative constraint processing. IEICE Transactions on Information and Systems, E90-D(9), 1354–1362. doi: 10.1093/ietisy/e90-d.9.1354
  10. 10.
    Isern, D., & Moreno, A. (2008). Computer-based execution of clinical guidelines: A review. International Journal of Medical Informatics, 77(12), 787–808.CrossRefGoogle Scholar
  11. 11.
    Korb, K., & Nicholson, A. (2011). Bayesian artificial intelligence (2nd ed.). London: CRC Press.zbMATHGoogle Scholar
  12. 12.
    Lam, H. P., Governatori, G., Satoh, K., & Hosobe, H. (2012). Distributed defeasible speculative reasoning in ambient environment. In M. Fisher, L. Torre, M. Dastani, & G. Governatori (Eds.), Proceedings of the computational logic in multi-agent systems: 13th international workshop, CLIMA XIII. Lecture notes in computer science (pp. 43–60). Berlin: Springer. doi: 10.1007/978-3-642-32897-8_5.
  13. 13.
    Liu, Z., Malone, B., & Yuan, C. (2012). Empirical evaluation of scoring functions for Bayesian network model selection. BMC Bioinformatics, 13(Suppl 15), S14. doi: 10.1186/1471-2105-13-S15-S14.
  14. 14.
    Oliveira, T., Neves, J., Novais, P., & Satoh, K. (2014). Applying speculative computation to guideline-based decision support systems. In 2014 IEEE 27th international symposium on computer-based medical systems (CBMS) (pp. 42–47). IEEE. doi: 10.1109/CBMS.2014.32.
  15. 15.
    Oliveira, T., Novais, P., & Neves, J. (2013). Representation of clinical practice guideline components in owl. In J. B. Pérez, J. M. C. Rodríguez, J. Fëhndrich, P. Mathieu, A. Campbell, M. C. Suarez-Figueroa, A. Ortega, E. Adam, E. Navarro, R. Hermoso, & M. N. Moreno (Eds.), Trends in practical applications of agents and multiagent systems. Advances in intelligent systems and computing (Vol. 221, pp. 77–85). Springer. doi: 10.1007/978-3-319-00563-8_10.
  16. 16.
    Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. Journal of Biomedical Informatics, 46(4), 744–763.CrossRefGoogle Scholar
  17. 17.
    Prakken, H., & Sartor, G. (1997). Argument-based extended logic programming with defeasible priorities. Journal of Applied Non-classical Logics, 7(1–2), 25–75.MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Russell, L., Gold, M., Siegel, J., Daniels, N., & Weinstein, M. (1996). The role of cost-effectiveness analysis in health and medicine. JAMA, 276(14), 1172–1177. doi: 10.1001/jama.1996.03540140060028.CrossRefGoogle Scholar
  19. 19.
    Sadri, F., & Toni, F. (2006). Interleaving belief updating and reasoning in abductive logic programming. Frontiers in Artificial Intelligence and Applications, 141, 442–446.Google Scholar
  20. 20.
    Saraswat, V., Jagadeesan, R., & Gupta, V. (1996). Timed default concurrent constraint programming. Journal of Symbolic Computation, 22(5), 475–520.MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Satoh, K. (2005). Speculative computation and abduction for an autonomous agent. IEICE—Transactions on Information and Systems, E88-D(9), 2031–2038.Google Scholar
  22. 22.
    Satoh, K., Codognet, P., & Hosobe, H. (2003). Speculative constraint processing in multi-agent systems. In Intelligent agents and multi-agent systems (Vol. 2891, pp. 133–144). Berlin: Springer.Google Scholar
  23. 23.
    Satoh, K., Inoue, K., Iwanuma, K., & Sakama, C. (2000). Speculative computation by abduction under incomplete communication environments. In Proceedings of the fourth international conference on multi-agent systems (Vol. 12, pp. 263–270). Alamitos: IEEE.Google Scholar
  24. 24.
    Satoh, K., & Yamamoto, K. (2002). Speculative computation with multi-agent belief revision. In Proceedings of the first international joint conference on autonomous agents and multi-agent systems: Part 2, AAMAS ’02 (pp. 897–904). New York: ACM.Google Scholar
  25. 25.
    Scutari, M. (2010). Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3), 1–22.CrossRefGoogle Scholar
  26. 26.
    Smolka, G. (1997). The oz programming model. In M. Broy & B. Schieder (Eds.), Mathematical methods in program development. NATO ASI series (Vol. 158, pp. 409–432). Berlin: Springer.Google Scholar
  27. 27.
    Ten Teije, A., Miksch, S., & Lucas, P. (2008). Computer-based medical guidelines and protocols: A primer and current trends (Vol. 139). Amsterdam: IOS Press.Google Scholar
  28. 28.
    Van De Wetering, G., Woertman, W. H., & Adang, E. M. (2012). Time to incorporate time in cost-effectiveness analysis. European Journal of Health Economics, 13(3), 223–226. doi: 10.1007/s10198-011-0374-3.CrossRefGoogle Scholar
  29. 29.
    Van der Heijden, M., & Lucas, P. (2013). Describing disease processes using a probabilistic logic of qualitative time. Artificial Intelligence in Medicine, 59(3), 143–155.CrossRefGoogle Scholar
  30. 30.
    Visscher, S., Lucas, P., Schurink, C., & Bonten, M. (2009). Modelling treatment effects in a clinical Bayesian network using boolean threshold functions. Artificial Intelligence in Medicine, 46(3), 251–266. doi: 10.1016/j.artmed.2008.11.006.CrossRefGoogle Scholar
  31. 31.
    Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671–682.CrossRefGoogle Scholar
  32. 32.
    Witten, I., Frank, E., & Hall, M. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.). San Francisco: Morgan Kaufmann.Google Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Tiago Oliveira
    • 1
  • Ken Satoh
    • 2
  • Paulo Novais
    • 1
  • José Neves
    • 1
  • Hiroshi Hosobe
    • 3
  1. 1.Algoritmi Research Centre/Department of InformaticsUniversity of MinhoBragaPortugal
  2. 2.National Institute of InformaticsSokendai UniversityTokyoJapan
  3. 3.Department of Digital MediaHosei UniversityTokyoJapan

Personalised recommendations