TRANSFORMING THE DECISION-MAKING EXPERIENCE
Abstract
The problem of transferring the experience of decision-making in situational analysis using
geoinformation systems is considered. The need for intellectual support from the geoinformation
system is due to the fact that spatial objects and connections of the real world are extremely dynamic.
Under these conditions, it is not possible to apply analytical models of processes and phenomena
due to the incompleteness and inconsistency of the information describing them. Statistical
models depend on a large number of factors, which vary as the geographical location of the
situation changes. An alternative is to use the experience of experts who are able to make effective
decisions in local spatial situations. The lack of control over the reuse of experience is a problem.
The knowledge gained in developing solutions in one locality can lead to inadequate solutions in
another locality. The experience of solving a problem in the same area loses its significance over
time. In this paper, we propose a representation of knowledge in the form of an image consisting
of a center and acceptable transformations of the center. Image transformation functions that
perform knowledge transfer are introduced. The properties of transformation functions that carry
procedural knowledge about the images of situations are analyzed. The use of the identified properties
for the formation of a test plan for software transformation procedures is considered. Thecriteria for successful transformation are studied. The optimization problem of finding the best
transformation function in the GIS knowledge base is formulated. A generalized method of transforming
experience is proposed. An example of the synthesis of transformation methods for selecting
an operational call center is given. The image of the situation of making a decision about
choosing a land plot for a service center is transformed into the specified area on the GIS map.
References
Moscow: Data+, 1999.
2. Motovilov Yu.G., Gel'fan A.N. Modeli formirovaniya stoka v zadachakh gidrologii rechnykh
basseynov [Models of runoff formation in the problems of river basin hydrology]. Moscow:
Izd-vo RAN, 2018.
3. Yue P., Jiang L. BigGIS: How big data can shape next-generation GIS, 2014 The Third International
Conference on Agro-Geoinformatics, Beijing, 2014, pp. 1-6.
4. Shashi S., Hui, X. Encyclopedia of GIS. Springer, New York, 2017.
5. Belyakov S., Bozhenyuk A., Rozenberg I. The intuitive cartographic representation in decisionmaking,
World Scientific Proceeding Series on Computer Engineering and Information Science,
2016, Vol. 10, pp. 13-18.
6. Falikman M. Kognitivnaya nauka: osnovopolozheniya i perspektivy [Cognitive science: fundamentals
and prospects], Logos [Logos], 2014, No. 1, pp. 316-330.
7. Vagin V.N., Golovina E.Yu., Zagoryanskaya A.A., Fomina M.V. Dostovernyy i
pravdopodobnyy vyvod v intellektual'nykh sistemakh [Reliable and plausible conclusion in intelligent
systems]. Moscow: Fizmatlit, 2001.
8. Timperley M., Mokhtar M., Bellaby G., Howe J. Explanation-based learning with analogy for
impasse resolution, Expert Systems with Applications, 2016, Vol. 61, pp. 181-191.
9. Cao N., Gotz D., Sun J., Qu H. DICON: interactive visual analysis of multidimensional clusters,
IEEE Trans. Vis. Comput. Graph., 2011, Vol. 17 (12), pp. 2581-2590.
10. Aamodt A., Plaza, E. Case-Based Reasoning: Foundational Issues, Methodological Variations,
and System Approaches, AI Communications, 1994, Vol. 7 (1), pp. 39-59.
11. Gentner D., Holyoak K.J. Reasoning and learning by analogy: introduction, Am. Psychol.,
1997, Vol. 52 (1), pp. 32-34.
12. Chattopadhyay R., Ye J., Panchanathan S., Fan W., Davidson I. Multi-source domain adaptation
and its application to early detection of fatigue, ACM Trans. Knowl. Discov. Data., 2012,
Vol. 6 (4), pp. 18-22.
13. Han J., Kamber M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers,
2000.
14. Duan L., Tsang I.W., Xu D. Domain transfer multiple kernel learning, IEEE Trans. Pattern
Anal. Mach. Intell., 2012, Vol. 34 (3), pp. 465-479.
15. Wen Y.-M., Lu B.-L. Incremental learning of support vector machines by classifier combining,
Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining,
Nanjing., 2007, pp. 904-911.
16. Long M., Wang J., Ding G., Sun J., Yu P.S. Transfer feature learning with joint distribution
adaptation, IEEE 2013 Conference on Computer Vision, Sydney, 2013, pp. 2200-2207.
17. Fruchterman T.M., Reingold E.M. Graph drawing by force-directed placement, Softw. Pract.
Exp., 1991, Vol. 21 (11), pp. 1129-1164.
18. Heimerl F., Koch S., Bosch H., Ertl T. Visual classifier training for text document retrieval,
IEEE Trans. Vis. Comput. Graph., 2012, Vol. 18 (12), pp. 2839-2848.
19. Stasch C., Scheider S., Pebesma E., Kuhn W. Meaningful spatial prediction and aggregation,
Environmental Modelling & Software, 2014, Vol. 51, pp. 149-165.
20. Vereshchaka T.V., Bakanova M.Yu. Osobennosti tekhnologii sozdaniya (obnovleniya)
spetsializirovannykh topograficheskikh kart neftegazovogo naznacheniya [Features of technology
for creating (updating) specialized topographic maps for oil and gas purposes], Izv.
vuzov «Geodeziya i aerofotos"emka» [Izvestiya vuzov "Geodesy and aerial photography"],
2019, Vol. 63, No. 6, pp. 678-688.
21. Kresnikova N.I., Vasil'evykh N.A. Primenenie dannykh distantsionnogo zondirovaniya i
geoinformatsionnykh tekhnologiy dlya obespecheniya territorial'nogo planirovaniya [Application
of remote sensing data and geoinformation technologies to ensure territorial planning],
Izv. vuzov «Geodeziya i aerofotos"emka» [Izvestiya vuzov "Geodesy and aerial photography"],
2018, Vol. 62, No. 2, pp. 212-217.
22. Palamarchuk N.A. Zonirovanie territoriy goroda [Zoning of city territories], Zemleustroystvo,
kadastr i monitoing zemel' [Land management, cadastre and land monitoring], 2013, No. 7
(103), pp. 48-52.
23. Aleksandrov A.A. Modelirovanie vzryvoopasnosti i zonirovanie territorii pri khranenii
zhidkogo uglevodorodnogo topliva po kriteriyam riska [Modeling of explosion hazard and
zoning of the territory during storage of liquid hydrocarbon fuel according to risk criteria], ed.
by V.I. Larionova. Ufa: Izd-vo: BESTS, 2004.
24. Dem'yanov V.V., Savel'eva E.A. Geostatistika: teoriya i praktika [Geostatistics: theory and
practice], ed. by R.V. Arutyunyana; In-t problem bezopasnogo razvitiya atomnoy energetiki
RAN. Moscow: Nauka, 2010.
25. Vizualizatsiya i analiz geograficheskikh dannykh na yazyke R. Available at:
https://tsamsonov.github.io/r-geo-course/ (accessed 29 November 2020).
