Control Systems and Computers, N3, 2016, Article 3

DOI: https://doi.org/10.15407/usim.2016.03.023

Upr. sist. maš., 2016, Issue 3 (263), pp. 23-28, 42.

 UDC 004.891.3

S.F. Chaliy, Doctor of Engeneering Science, E-mail: S_chaliy@mail.ru, 

I.V. Levykin, PhD of Engeneering Science, E-mail: levykinvictor@gmail.com,

Kharkiv National University of Radio Electronics, Lenin av., 14, 61166, Kharkiv, Ukraine

The Development of a Generalized Process Model Case-Based Reasoning, the Method of its Formation and Use

Introduction. The problem of modeling on the Case-Based reasoning in solving the functional tasks of information systems is considered. The Case-based reasoning modeling makes it possible to use knowledge and experience available to solve the current problems even in the case of some differences between the problem been solved and the current problem.

In using the knowledge on the issues available, arises the question of the model of case-based reasoning developing that should formalize the problem solution process as well as the method of model-building technique. The content of the case depends on the features of data domain that makes it difficult to build the generalized model. However, the problem solution for any study-case can be shown as the execution sequence, viz. a process. So, the purpose of this research is to generalize the case-study representation using a process approach.

Methods.The generalized model of the study-case including a set of possible processes for solving the problem, local results of solutions and restrictions in the data domain is proposed. Each process of the problem solving consists of a sequence of events that reflect the chain of actions that must be done to solve the task. Selection of the particular possible processes of the problem solution depends on the sub-product desired, as well as on the data domain restrictions. On the practical level, the proposed model makes it easy to adapt the study-case based on the problem solution selection.

Implementation of the process approach based on the proposed model of the case-base reasoning includes a number of successive steps providing method for forming the initial data, their selection and correction as well as saving the current prototype and study-case prototype.

Results. The method allows to form the problem solution on the base of using mining technology to develop a process of the problem solution. Besides, it makes possible to implement an adaptive control using the event attributes.

Download full text! (In Russian).

Keywords:  Generalized Process Model, process description, knowledge, attributes.

1. Schank, R.C., Abelson, R.P., 1977. Scripts, Plans, Goals and Undestsnding. Erlbaum Hillsdale, N.J., US, 248 p.
  2. Riesbeck, C.K.,  Schank, R., 1989. Inside Case-based Reasoning. Erlbaum – Northvale, N.J, 448 p.
 3. Watson, I., 1999. “Case-based reasoning is a methodology not
 a technology”. Knowlidge-based systems, 12, pp. 303–308.
  4. Nikolaychuk, O.A., Yurin, A.Yu., 2005. “Prototip intellektual’noy sistemy dlya issledovaniya tekhnicheskogo sostoyaniya mekhanicheskikh sistem”. Iskusstvennyy intellekt, 4, pp. 459–468.
5. Nikolaychuk, O.A., Yurin, A.Yu., 2009. “Primeneniye pretsedentnogo podkhoda dlya avtomatizirovannoy identifikatsii tekhnicheskogo sostoyaniya detaley mekhanicheskikh sistem”. Avtomatizatsiya i sovremennyye tekhnologii, 5, pp. 3–12.
6. Berman, A.F., Nikolaychuk, O.A., Pavlov A.I. et. al., 2012. “Intellektual’naya sistema dlya analiza otkazov slozhnykh tekhnicheskikh sistem”. Tr. Trinadtsatoy nats. konf. po iskusstvennomu intellektu s mezhdunarodnym uchastiyem KII-2012 (16–20 sent. 2012 g., Belgorod, Rossiya): Tr. konf. M.: Fizmatlit, T. 3, pp. 146–154.
  7. Aamodt, A.,  Plaza, E., 1994. “Case-Based Reasoning: Foundational issues, methodological variations, and system approaches”. AI Communications, 7 (1), pp. 39–59.
  8. Lyuger, D.F., 2003. Iskusstvennyy intellekt: strategii i metody resheniya slozhnykh problem. M.: Vil’yams, 864 p.
  9. Kolodner, J., 1993. Case-Based Reasoning. Magazin Kaufmann. San Mateo, 386 p.
https://doi.org/10.1016/B978-1-55860-237-3.50005-4
  10.Osipov, G.S., 2009. Lektsii po iskusstvennomu intellektu. M.: KRASAND. ISA RAN, 266 p.
  11. Carbonell, J.G., 1983. “Learning by analogy: Formulating and generalizing plans from past experience”. Machine learning, an artificial intelligence approach. Palo Alto, CA: Tioga Press., 1, pp. 137–162.
12.  Skurikhin, V.M., Zabrodskiy, V.A., Kopeychenko, Yu.V., 1984.
Proyektirovaniye sistem adaptivnogo upravleniya proizvodstvom. Khar’kov: Vishcha shk., 206 p.

Received 3.03.2016