Abstract
With this paper we present a brief overview of selected prominent approaches to rule frameworks and formal rule languages for the representation of and reasoning with uncertain or imprecise knowledge. This work covers selected probabilistic and possibilistic logics, as well as implementations of uncertainty and possibilistic reasoning in rule engine software.
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References
Fuzzy clips, http://ai.iit.nrc.ca/irpublic/fuzzy/fuzzyclips/fuzzyclipsindex.html
Fuzzy jess, http://www.nrc-cnrc.gc.ca/eng/ibp/iit.html
Fuzzyshell, http://cobweb.ecn.purdue.edu/rvl/projects/fuzzy/
http://www.fico.com/en/products/dmtools/pages/fico-blaze-advisor-system.aspx
Rule interchange format (rif) working group, http://www.w3.org/2005/rules/wiki/rif_working_group
Ruleml, http://www.ruleml.org
Swrl: A semantic web rule language combining owl and ruleml, http://www.w3.org/submission/swrl/
W3c uncertainty reasoning for the web incubator group, http://www.w3.org/2005/incubator/urw3/xgr-urw3
Antoniou, G., Damásio, C.V., Grosof, B., Horrocks, I., Kifer, M., Maluszynski, J., Patel-Schneider, P.F.: Combining Rules and Ontologies. A survey (2005)
Bacchus, F.: l p , a logic for representing and reasoning with statistical knowledge. Computational Intelligence 6, 209–231 (1990)
Buchanan, B.G., Shortliffe, E.H.: Rule-based Expert Systems: the MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984)
Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering 1(1), 146–166 (1989)
Damsio, C.V., Pan, J.Z., Stoilos, G., Straccia, U.: Representing uncertainty in RuleML. Fundam. Inf. 82(3), 265–288 (2008)
Dubois, D.: Possibility theory and statistical reasoning. Computational Statistics & Data Analysis 51(1), 47–69 (2006)
Dubois, D., Esteva, F., Godo, L., Prade, H.: Fuzzy-set based logics an history-oriented presentation of their main developments. In: Gabbay, D.M., Woods, J. (eds.) Handbook of the History of Logic. The Many Valued and Non-monotonic Turn in Logic, vol. 8, pp. 325–449. Elsevier, Amsterdam (2007)
Dubois, D., Prade, H.: Possibility theory, probability theory and Multiple-Valued logics: A clarification. Annals of Mathematics and Artificial Intelligence 32(1-4), 35–66 (2001)
Forgy, C.: Rete: A fast algorithm for the many patterns/many objects match problem. Artif. Intell. 19(1), 17–37 (1982)
Fuhr, N.: Probabilistic datalog - a logic for powerful retrieval methods. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1995)
Hájek, P.: Metamathematics of Fuzzy Logic. Trends in Logic: Studia Logica Library, vol. 4. Kluwer Academic Publishers, Dordrecht (1998)
Halpern, J.Y.: An analysis of first-order logics of probability. Artificial Intelligence 46, 311–350 (1990)
Halpern, J.Y.: Reasoning about Uncertainty. MIT Press, Cambridge (2003)
Kersting, K., Raedt, L.D.: Bayesian logic programs. In: Proceedings of the 10th International Conference on Inductive Logic Programming (2000)
Klir, G.J.: Generalized information theory. Fuzzy Sets Syst. 40(1), 127–142 (1991)
Laskey, K.B., Costa, P.C.: Of klingons and starships: Bayesian logic for the 23rd century. In: Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence (2005)
Logic, P., Dubois, D., Prade, H.: Possibilistic logic
Luckham, D.C.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)
Lukasiewicz, T.: Probabilistic description logic programs. International Journal of Approximate Reasoning 45(2), 288–307 (2007)
Muggleton, S.: Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence (2000)
Pan, J.Z., Stamou, G.B., Tzouvaras, V., Horrocks, I.: f-swrl: A fuzzy extension of swrl. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 829–834. Springer, Heidelberg (2005)
Puech, A., Muggleton, S.: A comparison of stochastic logic programs and bayesian logic programs. In: IJCAI 2003 workshop on learning statistical models from relational data, IJCAI (2003)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2), 107–136 (2006)
Smets, P.: Imperfect Information: Imprecision and Uncertainty, pp. 225–254 (1996)
Wang, P.: Confidence as higher order uncertainty. null (1994)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Zadeh, L.A.: Fuzzy logic and approximate reasoning, pp. 238–259 (1996)
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Nickles, M., Sottara, D. (2009). Approaches to Uncertain or Imprecise Rules - A Survey. In: Governatori, G., Hall, J., Paschke, A. (eds) Rule Interchange and Applications. RuleML 2009. Lecture Notes in Computer Science, vol 5858. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04985-9_30
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DOI: https://doi.org/10.1007/978-3-642-04985-9_30
Publisher Name: Springer, Berlin, Heidelberg
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