A method for predicting solutions in case-based problem solving

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1898, Issue , 2000, Pages 124-135

  Hüllermeier, Eyke ^a  

^a IRIT   (France)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING;

CASE BASED; CONSTRAINT-BASED; FORMAL MODEL; HIGH PROBABILITY; INFERENCE PROCESS; METAHEURISTIC; SET-VALUED; SIMILAR SOLUTION;

CASE BASED REASONING;

EID: 84948157990     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44527-7_12     Document Type: Conference Paper

References (19)

1. A. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39-59, 1994.
2. D W Aha, editor. Lazy Learning. Kluwer Academic Publ., 1997.
3. D. W. Aha, D. Kibler, and M. K. Albert. Instance-based learninga lgorithms. Machine Learning, 6(1):37-66, 1991.
4. B. V. Dasarathy, editor. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, California, 1991.
5. D. Dubois, F. Esteva, P. Garcia, L. Godo, R. Lopez de Mantaras, and H. Prade. Fuzzy set modellingin case-based reasoning. Int. J. Intelligent Systems, 13:345-373, 1998.
6. F. Esteva, P. Garcia, L. Godo, and R. Rodriguez. A modal account of similaritybased reasoning. Int. J. Approximate Reasoning, 16:235-260, 1997.
7. B. Faltings. Probabilistic indexing for case-based prediction. Proceedings ICCBR-97, pages 611-622. Springer-Verlag, 1997.
8. E. Hüllermeier. Similarity-based inference as constraint-based reasoning: Learning similarity hypotheses. Technical Report 64, Department of Economics, University of Paderborn, September 1999.
9. E. Hüllermeier. Toward a probabilistic formalization of case-based inference. In Proc. IJCAI-99, pages 248-253, Stockholm, Sweden, July/August 1999.
10. D. Kibler and D. W. Aha. Instance-based prediction of real-valued attributes. Computational Intelligence, 5:51-57, 1989.
11. G. J. Klir and M. J. Wierman. Uncertainty-Based Information. Physica-Verlag, Heidelberg, 1998.
12. J. L. Kolodner. Case-based Reasoning. Morgan Kaufmann, San Mateo, 1993.
13. T. M. Michell. Version spaces: A candidate elimination approach to rule learning. In Proceedings IJCAI-77, pages 305-310, 1977.
14. E. Plaza, F. Esteva, P. Garcia, L. Godo, and R. Lopez de Mantaras. A logical approach to case-based reasoningus ing fuzzy similarity relations. Journal of Information Sciences, 106:105-122, 1998.
15. C. Reiser and H. Kaindl. Case-based reasoningf or multi-step problems and its integration with heuristic search. Proc. EWCBR-94, pages 113-125, 1994.
16. R. Short and K. Fukunaga. The optimal distance measure for nearest neighbor classification. IEEE Transactions on Information Theory, 27:622-627, 1981.
17. B. Smyth and P. Cunningham. The utility problem analysed. Proc. EWCBR-96, pages 392-399. Springer-Verlag, 1996.
18. B. Smyth and E. McKenna. Buildingcom pact competent case-bases. Proc. ICCBR-99, pages 329-342, 1999.
19. S. Smyth and T. Keane. Rememberingto forget. Proc. IJCAI-95, pages 377-382, 1995.