Temporal Case-Based Reasoning System for Automatic Parking Complex
In this paper the problem of the application of
temporal reasoning and case-based reasoning in intelligent decision
support systems is considered. The method of case-based reasoning
with temporal dependences for the solution of problems of real-time
diagnostics and forecasting in intelligent decision support systems is
described. This paper demonstrates how the temporal case-based
reasoning system can be used in intelligent decision support systems
of the car access control. This work was supported by RFBR.
[1] Eremeev A. P., Vagin V. N. Methods and tools for modelling reasoning
in diagnostic systems // ICEIS 2009 – 11th International Conference on
Enterprise Information Systems, Proceedings, AIDSS, 2009, pp. 271–
276.
[2] David C. Wilson, David B. Leakes, Maintaining Case-Based Reasoners:
Dimensions and Directions // Computational Intelligence, Volume 17,
Number 2, 2001, pp. 196–213.
[3] Leake D.B. CBR in Context: The Present and Future // Case-Based
Reasoning: Experiences, Lessons and Future Directions, AAAI Press /
The MIT Press, 1996, pp. 3–31.
[4] Vagin V.N., Yeremeyev A.P. Modeling Human Reasoning in Intelligent
Decision Support Systems // Proc. of the Ninth International Conference
on Enterprise Information Systems. Volume AIDSS. Funchal, Madeira,
Portugal, June 12-16, INSTICC, 2007, pp. 277–282.
[5] Varshavskii P.R., Eremeev A.P. Modeling of case-based reasoning in
intelligent decision support systems // Scientific and Technical
Information Processing, 37 (5), 2010, pp. 336–345.
[6] Eremeev A.P., Kurilenko I.E. Temporal reasoning component for realtime
intelligent decision-support systems // Scientific and Technical
Information Processing, 38 (5), 2011, pp. 332–343.
[7] Aamodt A., Plaza E. Case-Based Reasoning: Foundational Issues,
Methodological Variations, and System Approaches // AI
Communications, IOS Press, Vol. 7(1), 1994, pp. 39–59.
[8] Falkenhainer B., Forbus K., Gentner D. The Structure-Mapping Engine:
Algorithm and examples // Artificial Intelligence, 41, 1989, pp. 1–63.
[9] Alexander Eremeev, Pavel Varshavsky, Analogous Reasoning and Case-
Based Reasoning for Intelligent Decision Support Systems //
International Journal INFORMATION Theories & Applications
(ITHEA) 2006, Vol.13 № 4, pp. 316–324.
[10] Eremeev A., Varshavsky P. Methods and Tools for Reasoning by
Analogy in Intelligent Decision Support Systems // Proc. of the
International Conference on Dependability of Computer Systems.
Szklarska Poreba, Poland, 14-16 June, 2007, IEEE, pp.161–168.
[11] Eremeev A.P. and Troitskii V.V. Representation of Temporal
Constraints in Intelligent Systems, Proc. Congress "Artificial
intelligence in the XXI Century" ICAI'2001, Moscow: Fizmatlit, 2001.
[12] Allen J.F. Towards a General Theory of Action and Time. Artificial
Intelligence 23, 1984, pp. 123–154.
[13] T. Drakengran and P. Johnson, Maximal tractable subclasses of Allen’s
interval algebra. Preliminary report. In Proceedings of the AAAI
National Conference on Artificial Intelligence, Portland, OR, 1996, pp.
389–394.
[14] Vila L. A survey on temporal reasoning in artificial intelligence // AI
Communications, 1994. Vol.7, no. 1, pp. 4–28.
[15] Peter van Belk, Reasoning about qualitative temporal information. In
Proceedings the AAAI National Conference on Artificial Intelligence,
1990, pp. 728–734.
[16] Gereviny A. and Schubert L. Efficient Algorithms for Qualitative
Reasoning about Time. Technical report 496, Department of Computer
Science, University of Rochester, Rochester, NY, 1993.
[17] Eremeev A.P., Kurilenko I.E. Implementation of temporal reasoning in
Intelligent Systems // Journal of Computer and Systems Sciences
International, v. 2, 2007, pp. 120–136.
[18] Borisov A.V., Kurilenko I.E. Application of temporal reasoning in
distributed system of car access control // ITMU 2005, №5, pp. 786–
794.
[1] Eremeev A. P., Vagin V. N. Methods and tools for modelling reasoning
in diagnostic systems // ICEIS 2009 – 11th International Conference on
Enterprise Information Systems, Proceedings, AIDSS, 2009, pp. 271–
276.
[2] David C. Wilson, David B. Leakes, Maintaining Case-Based Reasoners:
Dimensions and Directions // Computational Intelligence, Volume 17,
Number 2, 2001, pp. 196–213.
[3] Leake D.B. CBR in Context: The Present and Future // Case-Based
Reasoning: Experiences, Lessons and Future Directions, AAAI Press /
The MIT Press, 1996, pp. 3–31.
[4] Vagin V.N., Yeremeyev A.P. Modeling Human Reasoning in Intelligent
Decision Support Systems // Proc. of the Ninth International Conference
on Enterprise Information Systems. Volume AIDSS. Funchal, Madeira,
Portugal, June 12-16, INSTICC, 2007, pp. 277–282.
[5] Varshavskii P.R., Eremeev A.P. Modeling of case-based reasoning in
intelligent decision support systems // Scientific and Technical
Information Processing, 37 (5), 2010, pp. 336–345.
[6] Eremeev A.P., Kurilenko I.E. Temporal reasoning component for realtime
intelligent decision-support systems // Scientific and Technical
Information Processing, 38 (5), 2011, pp. 332–343.
[7] Aamodt A., Plaza E. Case-Based Reasoning: Foundational Issues,
Methodological Variations, and System Approaches // AI
Communications, IOS Press, Vol. 7(1), 1994, pp. 39–59.
[8] Falkenhainer B., Forbus K., Gentner D. The Structure-Mapping Engine:
Algorithm and examples // Artificial Intelligence, 41, 1989, pp. 1–63.
[9] Alexander Eremeev, Pavel Varshavsky, Analogous Reasoning and Case-
Based Reasoning for Intelligent Decision Support Systems //
International Journal INFORMATION Theories & Applications
(ITHEA) 2006, Vol.13 № 4, pp. 316–324.
[10] Eremeev A., Varshavsky P. Methods and Tools for Reasoning by
Analogy in Intelligent Decision Support Systems // Proc. of the
International Conference on Dependability of Computer Systems.
Szklarska Poreba, Poland, 14-16 June, 2007, IEEE, pp.161–168.
[11] Eremeev A.P. and Troitskii V.V. Representation of Temporal
Constraints in Intelligent Systems, Proc. Congress "Artificial
intelligence in the XXI Century" ICAI'2001, Moscow: Fizmatlit, 2001.
[12] Allen J.F. Towards a General Theory of Action and Time. Artificial
Intelligence 23, 1984, pp. 123–154.
[13] T. Drakengran and P. Johnson, Maximal tractable subclasses of Allen’s
interval algebra. Preliminary report. In Proceedings of the AAAI
National Conference on Artificial Intelligence, Portland, OR, 1996, pp.
389–394.
[14] Vila L. A survey on temporal reasoning in artificial intelligence // AI
Communications, 1994. Vol.7, no. 1, pp. 4–28.
[15] Peter van Belk, Reasoning about qualitative temporal information. In
Proceedings the AAAI National Conference on Artificial Intelligence,
1990, pp. 728–734.
[16] Gereviny A. and Schubert L. Efficient Algorithms for Qualitative
Reasoning about Time. Technical report 496, Department of Computer
Science, University of Rochester, Rochester, NY, 1993.
[17] Eremeev A.P., Kurilenko I.E. Implementation of temporal reasoning in
Intelligent Systems // Journal of Computer and Systems Sciences
International, v. 2, 2007, pp. 120–136.
[18] Borisov A.V., Kurilenko I.E. Application of temporal reasoning in
distributed system of car access control // ITMU 2005, №5, pp. 786–
794.
@article{"International Journal of Information, Control and Computer Sciences:70004", author = "Alexander P. Eremeev and Ivan E. Kurilenko and Pavel R. Varshavskiy", title = "Temporal Case-Based Reasoning System for Automatic Parking Complex", abstract = "In this paper the problem of the application of
temporal reasoning and case-based reasoning in intelligent decision
support systems is considered. The method of case-based reasoning
with temporal dependences for the solution of problems of real-time
diagnostics and forecasting in intelligent decision support systems is
described. This paper demonstrates how the temporal case-based
reasoning system can be used in intelligent decision support systems
of the car access control. This work was supported by RFBR.", keywords = "Analogous reasoning, case-based reasoning,
intelligent decision support systems, temporal reasoning.", volume = "9", number = "5", pages = "1283-7", }
