Abstract
Several theories have been proposed to capture the essence of abstraction. Among these, the \(\mathcal{KRA}\) model offers a framework where a set of generic abstraction operators allows abstraction to be automated.
In this paper we show how to describe component-based abstraction for the Model-Based Diagnosis task within the \(\mathcal{KRA}\) framework, and we discuss the benefits of such a formalization.
The clear and explicit partition of the system model into different levels required by \(\mathcal{KRA}\) (going from the perception level up to the theory level) opens the way to explore richer and better founded kinds of abstraction to apply to the MBD task.
Another noticeable advantage is that, by suitably personalizing the generic abstraction operators of \(\mathcal{KRA}\), the whole abstraction process, from the definition of abstract (macro)components to the computation of their behaviors starting from those of the ground components, can be performed automatically in such a way that important relationships between ground and abstract diagnoses are guaranteed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Holte, R., Mkadmi, T., Zimmer, R., MacDonald, A.: Speeding up problem-solving by abstraction: A graph-oriented approach. Art. Intelligence 85, 321–361 (1996)
Knoblock, C., Tenenberg, J., Qiang, Y.: A spectrum of abstraction hierarchies for planning. In: Proc. AAAI WS on AGAA, pp. 24–35 (1990)
Mozetič, I.: Hierarchical model-based diagnosis. Int. Journal of Man-Machine Studies 35(3), 329–362 (1991)
Subramanian, D.: Automation of abstractions and approximations: Some challenges. In: Proc. AAAI WS on AGAA, pp. 76–77 (1990)
Giunchiglia, F., Walsh, T.: A theory of abstraction. Art. Intell. 57(2-3), 323–389 (1992)
Nayak, P., Levy, A.: A semantic theory of abstraction. In: Proc. IJCAI, pp. 196–202 (1995)
Korf, R.: Toward a model of representation changes. Art. Intell. 14, 41–78 (1980)
Saitta, L., Zucker, J.: Semantic abstraction for concept representation and learning. In: Proc. SARA, pp. 103–120 (1998)
Sachenbacher, M., Struss, P.: Task-dependent qualitative domain abstraction. Art. Intell. 162(1-2), 121–143 (2005)
Chittaro, L., Ranon, R.: Hierarchical model-based diagnosis based on structural abstraction. Art. Intell. 155(1-2), 147–182 (2004)
Torta, G., Torasso, P.: Automatic abstraction in component-based diagnosis driven by system observability. In: Proc. IJCAI, pp. 394–400 (2003)
Struss, P., Malik, A., Sachenbacher, M.: Qualitative modeling is the key to automated diagnosis. In: Proc. IFAC 1996 (1996)
Saitta, L., Zucker, J.D.: Abstraction and complexity measures. In: Proc. SARA-2007 (2007)
Plaisted, D.: Theorem proving with abstraction. Art. Intelligence 16, 47–108 (1981)
Tenenberg, J.: Preserving consistency across abstraction mappings. In: Proc. IJCAI 1987, pp. 1011–1014 (1987)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saitta, L., Torasso, P., Torta, G. (2007). Formalizing the Abstraction Process in Model-Based Diagnosis. In: Miguel, I., Ruml, W. (eds) Abstraction, Reformulation, and Approximation. SARA 2007. Lecture Notes in Computer Science(), vol 4612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73580-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-73580-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73579-3
Online ISBN: 978-3-540-73580-9
eBook Packages: Computer ScienceComputer Science (R0)