Qualitative knowledge to support reasoning about cases

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

Volumn 1266, Issue , 1997, Pages 489-498

  Aarts, Robert J ^a   Rousu, Juho ^a  

^a VTT TECHNICAL RESEARCH CENTRE OF FINLAND   (Finland)

Author keywords

Domain knowledge; Planning; Qualitative reasoning

Indexed keywords

ARTIFICIAL INTELLIGENCE; PLANNING;

ADAPTATION KNOWLEDGE; DOMAIN KNOWLEDGE; INFLUENCE GRAPH; MODEL REPRESENTATION; QUALITATIVE KNOWLEDGE; QUALITATIVE MODEL; QUALITATIVE REASONING; REASONING TASKS;

CASE BASED REASONING;

EID: 21744438223     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63233-6_518     Document Type: Conference Paper

References (18)

1. Aamodt, A. (1994). Explanation-driven case-based reasoning, In S. Wess, K. Althoff, M. Richter (Eds.): Topics in Case-based reasoning. Springer Verlag, pp. 274-288.
2. Aarts, R.J. & Rousu, J. (1996). Towards CBR for bioprocess planning. In Smith I., Faltings, B., (Eds.): Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 16-27.
3. Ashley, K.D. & Aleven, V. (1996). How different is different? Arguing About the Significance of Similarities and Differences. In Smith I., Faltings, B., (Eds.): Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 1-15.
4. Bhatta, S., Goel, A. & Prabhakar, S. (1994). Innovation in Analogical Design: A Model-Based Approach. Proc. of the Third International Conference on AI in Design, Aug. 1994, Lausanne, Switzerland.
5. DeJong, G. F. (1994). Learning to plan in continuous domains. Artificial Intelligence 65: 71-141.
6. Falkenhainer, B., Forbus, K.D. & Gentner, D. (1989). The Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence 41: 1-63.
7. Forbus, K.D. (1984). Qualitative Process Theory. Artificial Intelligence 24: 85-168.
8. Hammond, K. (1990). Explaining and Repairing Plans That Fail. Artificial Intelligence, 45:173-228.
9. Hanney, K. & Keane, M.T. (1996). Learning Adaptation Rules from a Case-Base. In Smith I., Faltings, B., (Eds). Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 179-192.
10. Hastings, J.D., Branting, L.K. & Lockwood, J.A. (1995), Case Adaptation Using an Incomplete Causal Model. In: Veloso, M. & Aamodt, A. (Eds.): Proceedings ICCBR-95, Sesimbra, Lecture Notes in Artificial Intelligence, 1010: 181-192.
11. Leake, D.B., Kinley, A. & Wilson, D. Learning to Improve Case Adaptation by Introspective Reasoning and CBR. In: Veloso, M. & Aamodt, A. (Eds.): Proceedings ICCBR-95, Sesimbra, Lecture Notes in Artificial Intelligence, 1010: 229-240.
12. Kamp, G. (1996). Using Description Logics for Knowledge Intensive Case-Based Reasoning. In Smith I., Faltings, B., (Eds). Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 204-218
13. Koton, P. (1989). Using experience in learning and problem solving. Massachusetts Institute of Technology, Laboratory of Computer Science (Ph.D. diss., October 1988), MIT/LCS/TR-441.
14. Nayak P. & Joskowicz, L. (1996). Efficient compositional modeling for generating causal explanations. Artificial Intelligence 83: 193-227.
15. Richter, M. (1995). The similarity Issue in CBR: The knowledge contained in similarity measures, Invited talk at ICCBR-95, Sesimbra.
16. Say, A.C.C. & Kuru, S. (1996). Qualitative system identification: deriving structure from behavior. Artificial Intelligence 83: 75-141.
17. Schank, R.C. & Leake, D.B. (1989). Creativity and Learning in a Case-Based Explainer. Artificial Intelligence 40: 353-385.
18. Sycara, K., Guttal, R., Koning, J., Narasimhan, S. & Navinchandra, D. (1992) CADET: a Case-based Synthesis Tool for Engineering Design, Intl. J. Expert Systems, 4:2.