Abstract
Adaptation is probably the most difficult task in Case-Based Reasoning (CBR) systems. Most techniques for adaptation propose ad-hoc solutions that require an effort on knowledge acquisition beyond typical CBR standards.
In this paper we demonstrate the applicability of domain-independent planning techniques that exploit the knowledge already acquired in many knowledge-rich approaches to CBR. Those techniques are exemplified in a case-based training system that generates a 3D scenario from a declarative description of the training case.
Supported by the Spanish Ministry of Science and Education (TIN2006-15202-C03-03 and TIN2006-15140-C03-02).
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Sánchez-Ruiz, A., Gómez-Martín, P.P., Díaz-Agudo, B., González-Calero, P.A. (2008). Adaptation through Planning in Knowledge Intensive CBR . In: Althoff, KD., Bergmann, R., Minor, M., Hanft, A. (eds) Advances in Case-Based Reasoning. ECCBR 2008. Lecture Notes in Computer Science(), vol 5239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85502-6_34
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DOI: https://doi.org/10.1007/978-3-540-85502-6_34
Publisher Name: Springer, Berlin, Heidelberg
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