Abstract
Knowledge-intensive CBR assumes that cases are enriched with general domain knowledge. In CREEK, there is a very strong coupling between cases and general domain knowledge, in that cases are embedded within a general domain model. This increases the knowledge-intensiveness of the cases themselves. A knowledge-intensive CBR method calls for powerful knowledge acquisition and modeling techniques, as well as machine learning methods that take advantage of the general knowledge represented in the system. The focusing theme of the paper is on cases as knowledge within a knowledge-intensive CBR method. This is made concrete by relating it to the CREEK architecture and system, both in general terms, and through a set of example projects where various aspects of this theme have been studied.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Díaz-Agudo, B., González-Calero, P.A.: An Architecture for Knowledge Intensive CBR Systems. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 37–48. Springer, Heidelberg (2000)
Aamodt, A.: A Knowledge-Intensive Integrated Approach to Problem Solving and Sustained Learning. PhD. Dissertation. University of Trondheim, Department of Electrical Engineering and Computer Science, Trondheim (1991) [Downloadable from authors publications homepage]
Aamodt, A.: Explanation-driven case-based reasoning. In: Wess, S., et al. (eds.) Topics in case-based reasoning, pp. 274–288. Springer, Heidelberg (1994)
Jære, M.D., Aamodt, A., Skalle, P.: Representing temporal knowledge for case-based prediction. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 174–188. Springer, Heidelberg (2002)
Skalle, P., Aamodt, A.: Knowledge-based decision support in oil well drilling; Combining general and case-specific knowledge for problem solving. To appear in Proceedings of ICIIP-2004, International Conference on Intelligent Information Processing, Beijing (October 2004)
Clancey, W.J.: The frame of reference problem in the design of intelligent machines. In: VanLehn, K. (ed.) Architectures for Intelligence, pp. 357–423. Lawrence Erlbaum, Mahwah (1991)
Aamodt, A., Nygaard, M.: Different roles and mutual dependencies of data, information, and knowledge - an AI perspective on their integration. Data and Knowledge Enigneering 16, 191–222 (1995)
Hempel, C.G.: Aspects of scientific explanation. Free Press, New York (1965)
Thagard, P.: Computational Philosophy of Science. MIT Press/Bradford Books (1988)
Clancey, W.J.: Viewing knowldge bases as qualitative models. IEEE Expert 4(2), 9–23 (Summer 1989)
Newell, A.: The knowledge level. Artificial Intelligence 18, 87–127 (1982)
Van de Velde, W.: Issues in knowledge level modelling. In: David, J.-M., Krivine, J.-P., Simmons, R. (eds.) Second generation expert systems, pp. 211–231. Springer, Heidelberg (1993)
Aamodt, A.: Modeling the knowledge contents of CBR systems. In: Proceedings of the Workshop Program at the Fourth International Conference on Case-Based Reasoning, Vancouver, Naval Research Laboratory Technical Note AIC-01-003, pp. 32–37 (2001)
Aamodt, A.: A Knowledge Representation System for Integration of General and Case- Specific Knowledge. In: Proceedings from IEEE TAI 1994, International Conference on Tools with Artificial Intelligence, New Orleans, November 5-12 (1994)
Lippe, E.: Learning support by reasoning with structured cases. MSc Thesis, Norwegian University of Science and Technology (NTNU), Department of Computer and Information Science (2001)
Sørmo, F.: Plausible Inheritance; Semantic Network Inference for Case-Based Reasoning. MSc thesis, Norwegian University of Science and Technology (NTNU), Department of Computer and Information Science (2000)
Sørmo, F., Aamodt, A.: Knowledge communication and CBR. In: 6th European Conference on Case-Based Reasoning, ECCBR 2002, Aberdeen, Workshop proceedings. Robert Gordon University, September 2002, pp. 47–59 (2002)
Kusnierczyk, W., Aamodt, A., Lægreid, A.: Towards Automated Explanation of Gene-Gene Relationships. In: RECOMB 2004, The Eighth International Conference on Computational Molecular Biology, Poster Presentations, E9, San Diego (March 2004)
Gu, M., Aamodt, A., Tong, X.: Component retrieval using conversational case-based reasoning. To appear in Proceedings of ICIIP-2004, International Conference on Intelligent Information Processing, Beijing (October 2004)
Kofod-Petersen, A., Aamodt, A.: Case-based situation assessment in a mobile context-aware system. In: Proceedings of AIMS2003, Workshop on Artificial Intgelligence for Mobil Systems, Seattle (October 2003)
Ozturk, P., Aamodt, A.: A context model for knowledge-intensive case-based reasoning. International Journal of Human Computer Studies 48, 331–355 (1998)
Grimnes, M., Aamodt, A.: A two layercase-based reasoning architecture for medical image understanding. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 164–178. Springer, Heidelberg (1996)
Langseth, H., Aamodt, A., Winnem, O.M.: Learning retrieval knowledge from data. In: Anand, S.S., Aamodt, A., Aha, D.W. (eds.) Sixteenth International Joint Conference on Artificial Intelligence, Workshop ML- 5: Automating the Construction of Case-Based Reasoners, Stockholm, pp. 77–82 (1999)
Engelsli, S.E.: Intergration of Neural Networks in Knowledge - Intensive CBR. MSc thesis, Norwegian University of Science and Technology (NTNU), Department of Computer and Information Science (2003)
Tomassen, S.L.: Semi-automatic generation of ontologies for knwoledge-intensive CBR. MSc thesis, Norwegian University of Science and Technology (NTNU), Department of Computer and Information Science (2003)
Sverberg, P.: Steps towards an empirically responsible AI; A theoretical and methodological framework. MSc thesis, Norwegian University of Science and Technology (NTNU), Department of Computer and Information Science (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aamodt, A. (2004). Knowledge-Intensive Case-Based Reasoning in CREEK. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-28631-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22882-0
Online ISBN: 978-3-540-28631-8
eBook Packages: Springer Book Archive