AICI 2009: Artificial Intelligence and Computational Intelligence pp 685-695 | Cite as
Formalizing the Modeling Process of Physical Systems in MBD
Abstract
Many researchers have proposed several theories to capture the essence of abstraction. The G-KRA model(Genera KRA model), based on the GRA model which offers a framework R to represent the world W where a set of generic abstraction operators allows abstraction to be automated, can represent the world from different abstraction granularity. This paper shows how to model a physical system in model-based diagnosis within the G-KRA model framework using various kinds of knowledge. It investigates, with the generic theory of abstraction, how to automatically generate different knowledge models of the same system. The present work formalizes the process of constructing an abstract model of the considered system (e.g., using functional knowledge) based on the fundamental model and abstract objects database and expects that formalizing the modeling process of physical systems in MBD within the G-KRA framework will open the way to explore richer and better founded kinds of abstraction to apply to the MBD task.
Keywords
G-KRA Model Knowledge Modeling Model-Based DiagnosisPreview
Unable to display preview. Download preview PDF.
References
- 1.Holte, R., Mkadmi, T., Zimmer, R., MacDonald, A.: Speeding up problem-solving by abstraction: A graph-oriented approach. J. Art. Intelligence 85, 321–361 (1996)CrossRefGoogle Scholar
- 2.Knoblock, C., Tenenberg, J., Qiang, Y.: A spectrum of abstraction hierarchies for planning. In: Proc. AAAI WS on AGAA, pp. 24–35 (1990)Google Scholar
- 3.Mozetic, I.: Hierarchical model-based diagnosis. J. Int. Journal of Man-Machine Studies 35(3), 329–362 (1991)CrossRefGoogle Scholar
- 4.Subramanian, D.: Automation of abstractions and approximations: Some challenges. In: Proc. AAAI WS on AGAA, pp. 76–77 (1990)Google Scholar
- 5.Saitta, L., Zucker, J.: Semantic abstraction for concept representation and learning. In: Proc. SARA, pp. 103–120 (1998)Google Scholar
- 6.Shan-wu, S., Nan, W., Dan-tong, O.Y.: General KRA Abstraction Model. J. Journal of Jilin University (Science Edition) 47(3), 537–542 (2009)Google Scholar
- 7.Weld, D., De Kleer, J.: Readings in Qualitative Reasoning about Physical Systems. Morgan Kaufmann, San Mateo (1990)Google Scholar
- 8.Bobrow, D.G. (ed.): Special Volume on Qualitative Reasoning about Physical Systems. J. Artificial Intell. 24 (1984)Google Scholar
- 9.Davis, R.: Diagnostic reasoning based on structure and behavior. J. Artificial Intelligence 24, 347–410 (1984)CrossRefGoogle Scholar
- 10.Sticklen, J., Bond, E.: Functional reasoning and functional modeling. IEEE Expert 6(2), 20–21 (1991)CrossRefGoogle Scholar
- 11.Chittaro, L., Guida, G., Tasso, C., Toppano, E.: Functional and teleological knowledge in the multimodeling approach for reasoning about physical system:a case study in diagnosis. IEEE Trans. Syst. Man, Cybern. 23(6), 1718–1751 (1993)CrossRefGoogle Scholar
- 12.Chittaro, L., Ranon, R.: Hierarchical model-based diagnosis based on structural abstraction. Art. Intell. 155(1-2), 147–182 (2004)MATHCrossRefMathSciNetGoogle Scholar
- 13.Saitta, L., Torasso, P., Torta, G.: Formalizing the abstraction process in model-based diagnosis. In: Tr cs, vol. 34, Univ. of Torino, Italy (2006)Google Scholar
- 14.Chittaro, L., Ranon, R.: Diagnosis of multiple faults with flow-based functional models:the functional diagnosis with efforts and flows approach. Reliability Engineering and System Safety 64, 137–150 (1999)CrossRefGoogle Scholar
- 15.Torta, G., Torasso, P.: A Symbolic Approach for Component Abstraction in Model-Based Diagnosis. In: Proceedings of the Model-Based Diagnosis International Workshop (2008)Google Scholar