Abstract
Constructive adaptation is a search-based technique for generative reuse in CBR systems for configuration tasks. We discuss the relation of constructive adaptation (CA) with other reuse approaches and we define CA as a search process in the space of solutions where cases are used in two main phases: hypotheses generation and hypotheses ordering. Later, three different CBR systems using CA for reuse are analyzed: configuring gas treatment plants, generating expressive musical phrases, and configuring component-based software applications. After the three analyses, constructive adaptation is discussed in detail and some conclusions are drawn to close the paper.
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
Agnar Aamodt and Enric Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications, 7(1):39–59, 1994.
Josep Lluís Arcos. T-Air: A case-based reasoning system for designing chemical absorption plants. In David W. Aha and Ian Watson, editors, Case-Based Reasoning Research and Development, number 2080 in Lecture Notes in Artificial Intelligence, pages 576–588. Springer-Verlag, 2001.
Josep Lluís Arcos and Ramon López de Mántaras. Perspectives: A declarative bias mechanism for case retrieval. In David Leake and Enric Plaza, editors, Case-Based Reasoning. Research and Development, number 1266 in Lecture Notes in Artificial Intelligence, pages 279–290. Springer-Verlag, 1997.
J. L. Arcos and R. López de Mántaras. An interactive case-based reasoning approach for generating expressive music. Applied Intelligence, 14(1):115–129, 2001.
Eva Armengol and Enric Plaza. Similarity assessment for relational CBR. In David W. Aha and Ian Watson, editors, Case-Based Reasoning Research and Development, 4th International Conference on Case-Based Reasoning, ICCBR 2001, volume 2080 of Lecture Notes in Computer Science, pages 44–58, 2001.
Jaime Carbonell. Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In Machine Learning: An Artificial Intelligence Approach, volume 2, pages 371–392. Morgan Kaufmann, 1986.
D. Fensel, V. R. Benjamins, M. Gaspari S. Decker, R. Groenboom, W. Grosso, M. Musen, E. Motta, E. Plaza, G. Schreiber, R. Studer, and B. Wielinga. The component model of UPML in a nutshell. In Proceedings of the International Workshop on Knowledge Acquisition KAW’98, 1998.
Mario Gómez, Chema Abásolo, and Enric Plaza. Domain-independent ontologies for cooperative information agents. In Proceedings Workshop on Cooperative Information Agents, volume 2128 of LNAI, pages 118–129, 2001.
K. J. Hammond. Case-based planning: Viewing planning as a memory task. Academic Press, 1989.
T. Heinrich and J. L. Kolodner. The roles of adaptation in case-based design. In Proceedings of the AAAI Worksop on Case-based Reasoning, 1991.
Manuela Veloso and Jaime Carbonell. Toward scaling up machine learning: A case study with derivational analogy in prodigy. In Steven Minton, editor, Machine Learning Methods for Planning, pages 233–272. Morgan Kaufmann, 1993.
Manuela M. Veloso, Alice M. Mulvehill, and Michael T. Cox. Rationale-supported mixed-initiative case-based planning. In AAAI/IAAI, pages 1072–1077, 1997.
W. Wilke, B. Smyth, and P. Cunningham. Using configuration techniques for adaptation, pages 139–168. Number 1400 in LNAI. Springer Verlag, 1998.
W. Wilke and R. Bergmann. Techniques and knowledge used for adaptation during case-based problem solving. In IEA/AIE (Vol. 2), pages 497–506, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Plaza, E., Arcos, JL. (2002). Constructive Adaptation. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_23
Download citation
DOI: https://doi.org/10.1007/3-540-46119-1_23
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44109-0
Online ISBN: 978-3-540-46119-7
eBook Packages: Springer Book Archive