Abstract
The creation of intelligent systems for use in diagnosis and repair advisory domains has been a popular research topic in the applied artificial intelligence (AI) community during recent years {9,10,16,21}. These systems have been built utilizing a wide variety of architectures. Most early systems focused on the coding of experiential knowledge in the form of if-then style rules {15,18}. Initially, little emphasis was given to the need for incorporation of so-called “deep” knowledge about the physical principles of the systems studied. More recent research has addressed this issue by investigating ways of combining qualitative and quantitative modelling methodologies with inference techniques {17}. However, numerous problems remain to be solved in this domain. For example, little work has been conducted on methods of characterizing control knowledge in diagnosis and repair advisory systems. The research presented in this chapter focuses on this problem. A meta-level architecture that implements a control paradigm that can accommodate both “shallow” and “deep” reasoning mechanisms in a flexible plan-based architecture has been constructed and tested.
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Caviedes, J. et al. (1989). A Meta-Knowledge Architecture for Planning and Explanation in Repair Domains. In: Tzafestas, S.G. (eds) Knowledge-Based System Diagnosis, Supervision, and Control. Applied Information Technology. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-2471-1_2
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DOI: https://doi.org/10.1007/978-1-4899-2471-1_2
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