Abstract
There are many places (e.g. hospital emergency rooms) where reliable diagnostic systems might support people in their work. The paper discusses the problem of designing diagnostic rule-based systems with uncertainty. Most such systems use the technique of forward chaining in their reasonings. The number and the contents of the hypotheses depend then on both the form of system’s knowledge base and the details of the inference engine performance. In particular, the hypotheses can be influenced by the rules’ priorities. In the paper we propose a method for determining priorities for the rules designed from true evidence base which contains aggregate data of an attributive representation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ligęza, A.: Logical Foundations for Rule-Based Systems, 2nd edn. Springer, Heidelberg (2006)
Bragaglia, S., Chesani, F., Mello, P., Sottara, D.: A Rule-Based Implementation of Fuzzy Tableau Reasoning. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) RuleML 2010. LNCS, vol. 6403, pp. 35–49. Springer, Heidelberg (2010)
Nalepa, G.J., Ligęza, A.: On ALSV Rules Formulation and Inference. In: Lane, H.C., Guesgen, H.W. (eds.) Proc. 2nd Int. Florida Artif. Intel. Res. Soc. Conf., pp. 396–401. AAAI Press, Florida (2009)
Jankowska, B.: Using Semantic Data Integration to Create Reliable Rule-based Systems with Uncertainty. Eng. Appl. Artif. Intel. (2011) dOI: 10.1016/j.engappai.2011.02.013
Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Mathematics in Science and Engineering, vol. 144. Academic Press, New York (1980)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Buchanan, B.G., Shortliffe, H. (eds.): Rule-Based Expert Systems. The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984)
Beeri, C., Ramakrishnan: On the Power of Magic. J. Logic Program 10, 255–299 (1991)
Agraval, R., Imielinski, T., Swani, A.: Mining Association Rules Between Sets of Items in Large Databases. SIGMOD Rec. 22(2), 805–810 (1993)
Van der Gaag, L.C.: A Conceptual Model for Inexact Reasoning in Rule-Based Systems. Int. J. Approx. Reason. 3(3), 239–258 (1989)
Szymkowiak, M., Jankowska, B.: Discovering Medical Knowledge from Data in Patients’ Files. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS(LNAI), vol. 5796, pp. 128–139. Springer, Heidelberg (2009)
Plotnick, L.H., Ducharme, F.M.: Combined Inhaled Anticholinergics and Beta2-Agonists for Initial Treatment of Acute Asthma in Children. The Cochrane Library (2005)
Krysicki, W., et al.: Mathematical Statistics. PWN, Warszawa (1994) (in Polish)
Szymkowiak, M., Jankowska, B.: Reliability of Medical Production Rules Obtained by means of Aggregate Data Mining. Journal of Medical Informatics & Technologies 14, 103–110 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jankowska, B., Szymkowiak, M. (2011). On Ranking Production Rules for Rule-Based Systems with Uncertainty. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-23935-9_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23934-2
Online ISBN: 978-3-642-23935-9
eBook Packages: Computer ScienceComputer Science (R0)