Case-based reasoning foundations

Knowledge Engineering Review

Volumn 20, Issue 3, 2005, Pages 203-207

  Richter, Michael M ^a   Aamodt, Agnar ^b  

^a Rheinland Pfälzische Technische Universität Kaiserslautern Landau   (Germany)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

COGNITIVE SYSTEMS; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS;

CASE-BASED REASONING; COGNITIVE SCIENCE;

KNOWLEDGE ACQUISITION;

EID: 33646545049     PISSN: 02698889     EISSN: 14698005     Source Type: Journal    
DOI: 10.1017/S0269888906000695     Document Type: Review

References (28)

1. Aamodt, A, 1994, A knowledge representation system for integration of general and case-specific knowledge. In Proceedings from IEEE Tools with Artificial Intelligence (TAI), New Orleans, 5-12 November 1994, pp. 836-839.
2. Aamodt, A and Plaza, E, 1994, Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39-59.
3. Aha, D. Kibler, MK and Albert, 1991, Instance based learning algorithms. Machine Learning 6, 37-66.
4. Bartlett, FC, 1932, Remembering: A Study in Experimental and Social Psychology, Cambridge: Cambridge University Press.
5. Carbonell, J, 1986, Derivational analogy: a theory of reconstructive problem solving and expertise acquisition. In Michalski, R, Carbonell, J and Mitchell, T (eds) Machine Learning: an Artificial Intelligence Approach, vol. 2. Morgan Kaufmann, pp. 371-392.
6. Díaz-Agudo, B and González-Calero, P, 2001, A declarative similarity framework for knowledge intensive CBR. Proceedings of ICCBR 2001 (Lecture Notes in Artificial Intelligence, 2080), pp. 158-172.
7. Falkenhainer, B, Forbus, KD and Gentner, D, 1990, The structure mapping engine: algorithm and examples. Artificial Intelligence 41, 1-63.
8. Forbus, K, 2001, Exploring analogy in the large. In Gentner, D, Holyoak, K and Kokinov, B (eds) Analogy: Perspectives from Cognitive Sciences. MIT Press, pp. 24-58.
9. Gentner, D, 1983, Structure mapping - a theoretical framework for analogy. Cognitive Science 1, 155-170.
10. Globig, C, Jantke, K, Lange, S and Sakakibara, Y, 1997, On case-based learnability of languages. New Generation Computing 15, 57-63.
11. Hammer, B, 2002, Perspectives on learning symbolic data with connectionistic methods. In Kuehn, R et al. (eds) Adaptivity and Learning. Springer, pp. 141-160.
12. Keane, MT, 1994, Analogical asides of case-based reasoning. In Wess, S, Althoff, K-D and Richter, MM (eds) Topics in Case-Based Reasoning (Lecture Notes in Artificial Intelligence, 837). Springer, pp. 21-32.
13. Kibler, MK and Aha, D, 1987, Learning representative exemplars of concepts: an initial case study. Proceedings of the 4th International Workshop on Machine Learning, Irvine, CA. Morgan Kaufmann, pp. 24-30.
14. Kolodner, J, 1983, Reconstructive memory: a computer model. Cognitive Science 7, 281-328.
15. Kolodner, J and Riesbeck, C (eds), 1986, Experience, Memory, and Reasoning. Lawrence Erlbaum.
16. Kolodner, JL, 1993, Case-Based Reasoning. Morgan Kaufmann.
17. Kolodner, J and Leake, D, 1996, A tutorial introduction to case-based reasoning. In Leake, D (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press/MIT Press, pp. 31-65.
18. Koton. P, 1988, Reasoning about evidence in causal explanations. In Proceedings of the Seventh National Conference on Artificial Intelligence. AAAI Press, pp. 256-261.
19. Leake, D, 1988, Evaluating explanations. Proceedings of AAAI-88. Morgan Kaufmann, pp. 251-55.
20. Plaza, E, 1995, Cases as terms: A feature term approach to the structured representation of cases. In Veloso, M and Aamodt, A (eds) Case-Based Reasoning Research and Development, Proceedings of ICCBR 1995 (Lecture Notes in Artificial Intelligence, 1010). Springer, pp. 265-276.
21. Porter, B, Bareiss, R and Holte, R, 1990, Concept learning and heuristic classification in weak theory domains. Artificial Intelligence 45(1-2), 229-263.
22. Richter, MM, 1995, The knowledge contained in similarity measures. In Invited Talk ICCBR95, Sesimbra, Portugal.
23. Richter, MM, 2005, Knowledge containers. To appear.
24. Roth-Berghofer, T, 2002, Knowledge maintenance of case-based reasoning systems: the SIAM methodology. Doctoral Dissertation, University of Kaiserslautern.
25. Schank, RC, 1982, Dynamic Memory. Cambridge University Press.
26. Smith, E and Medin, D, 1981, Categories and Concepts. Harvard University Press.
27. Tulving, E, 1972, Episodic and semantic memory. In Tulving, E and Donaldson, W (eds) Organization of Memory. Academic Press.
28. Wess, S, Althoff, KD and Derwand, G, 1994, Using k-d trees to improve the retrieval step in case-based reasoning. In Wess, S, Althoff, KD and Richter, MM (eds) Topics in Case-Based Reasoning. Berlin: Springer, pp. 167-181.