Geodesic
Logical inference on the base of optimal subset of mixed diagnostic tests for intelligent systems
Prikladnaâ diskretnaâ matematika, no. 12 (2010), pp. 105-108 
A. E. Yankovskaya; A. I. Gedike. Logical inference on the base of optimal subset of mixed diagnostic tests for intelligent systems. Prikladnaâ diskretnaâ matematika, no. 12 (2010), pp. 105-108. http://geodesic.mathdoc.fr/item/PDM_2010_12_a54/
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     author = {A. E. Yankovskaya and A. I. Gedike},
     title = {Logical inference on the base of optimal subset of mixed diagnostic tests for intelligent systems},
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     pages = {105--108},
     year = {2010},
     number = {12},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/PDM_2010_12_a54/}
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Voir la notice de l'article provenant de la source Math-Net.Ru

It is proposed to realize logical inference in intelligent systems on the base of mixed diagnostic tests (MDT) consisting of an optimal combination of unconditional and conditional components. For this purpose the tree of MDT is constructed on the base an optimal subset (OS) of unconditional irredundant diagnostic tests (UIDT). The criteria of considering the sequence for UIDT as well as features involved in each of them are stated, which results in enumeration reduction and, as a rule, allows MDT construction from not all UIDT in an UIDT OS. Logical inference is performed along all MDT in the process of MDT tree construction. Matrix model of data and knowledge representation, the algorithm of MDT tree construction and logical inference regarding the object under investigation are given. At the present time logical inference on the base of MDT is realized in intelligent software tool IMSLOG.

[1] Yankovskaya A. E., “Logicheskie testy i sredstva kognitivnoi grafiki v intellektualnoi sisteme”, Novye informatsionnye tekhnologii v issledovanii diskretnykh struktur, Dokl. 3-i Vseros. konf. s mezhdunarodnym uchastiem, Izd-vo SO RAN, Tomsk, 2000, 163–168

[2] Yankovskaya A. E., “Kriterii optimizatsii vybora bezyzbytochnykh diagnosticheskikh testov dlya prinyatiya reshenii v intellektualnykh diagnosticheskikh sistemakh”, Matematicheskie metody raspoznavaniya obrazov, Sb. dokl. 13-i Vseros. konf. (MMRO-13), MAKS Press, M., 2007, 73–76

[3] Yankovskaya A. E., Ilinskikh N. N., Chernogoryuk G. E., Kuzovatkin A. N., “Rezultaty issledovanii algoritma prinyatiya reshenii neposredstvenno v protsesse postroeniya smeshannykh diagnosticheskikh testov, realizovannogo v intellektualnykh biomeditsinskikh sistemakh”, Estestvoznanie i gumanizm, Sb. nauch. trudov, 1, no. 3, Izdanie SibGMU, Tomsk, 2004, 95–99

[4] Yankovskaya A. E., “Design of Optimal Mixed Diagnostic Test With Reference to the Problems of Evolutionary Computation”, Proc. of the First International Conference on Evolutionary Computation and Its Applications, EVCA' 96, Moscow, 1996, 292–297

[5] Yankovskaya A. E., Gedike A. I., “Mixed Diagnostic Tests Building from an Optimal Unconditional Test Subset in Intelligent Pattern Recognition”, Pattern Recognition and Image Analysis, 19:4 (2009), 575–582 | DOI

[6] Yankovskaya A. E., Gedike A. I., Ametov R. V., Bleikher A. M., “IMSLOG-2002 Software Tool for Supporting Information Technologies of Test Pattern Recognition”, Pattern Recognition and Image Analysis, 13:4 (2003), 650–657