Learning qualitative models of physical and biological systems

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

Volumn 4660 LNAI, Issue , 2007, Pages 248-272

  Garrett, Simon M ^a   Coghill, George M ^b   Srinivasan, Ashwin ^c   King, Ross D ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF ABERDEEN   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING;

LEARNING MODELS; SCIENTIFIC DISCOVERY PROBLEMS;

NATURAL SCIENCES COMPUTING;

EID: 38149130546     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73920-3_12     Document Type: Conference Paper

References (48)

1. Bakshi, B.R., Stephanopoulos, G.: Representation of process trends - Part 3: multiscale extraction of trends from process data. Computers and Chemical Engineering 18, 267-302 (1994)
2. Bhaskhar, R., Nigam, A.: Qualitative physics using dimensional analysis. Artificial Intelligence 45, 73-111 (1990)
3. Blackman, R.B., Tukey, J.W.: The measurement of power spectra. John Wiley and Sons, New York (1958)
4. Bratko, Muggleton, S., Varšek, A.: Learning qualitative models of dynamic systems. In: Muggleton, S. (ed.) Inductive logic programming, pp. 437-452. Academic Press, San Diego, CA (1992)
5. Cellier, F.E.: Continuous system modelling. Springer, Berlin (1991)
6. Cheung, J.T.-Y., Stephanopoulos, G.: Representation of process trends - Part 1: a formal representation framework. Computers and Chemical Engineering 14, 495-510 (1990a)
7. Cheung, J.T.-Y., Stephanopoulos, G.: Representation of process trends - Part 2: the problem of scale and qualitative scaling. Computers and Chemical Engineering 14, 511-539 (1990b)
8. Cleland, W. W.: The kinetics of enzyme-catalysed reactions with two or more substrates and products: 1. nomenclature and rate equations. Biochimica et Biophysica Acta 67, 104-137 (1963)
9. Coiera, E.W.: Generating qualitative models from example behaviours (Technical Report 8901). University of New South Wales, Deptartment of Computer Science (1989a)
10. Coiera, E.W.: Learning qualitative models from example behaviours. In: Proceedings of the Third Workshop on Qualitative Physics, Stanford, CA, pp. 45-51 (1989)
11. DeCoste, D.: Dynamic across-time measurement interpretation. Artificial Intelligence 51, 273-341 (1991)
12. Džeroski, S.: Learning qualitative models with inductive logic programming. Informatica 16, 30-41 (1992)
13. Džeroski, S., Todorovski, L.: Discovering dynamics: from inductive logic programming to machine discovery. Journal of Intelligenty Information Systems 4, 89-108 (1995)
14. Flach, P.A., Kakas, A.C.: Abduction and induction: Essays on their relation and integration. Kluwer Academic Publishers, Amsterdam, The Netherlands (2000)
15. Forbus, K.D.: Qualitative process theory. Artificial Intelligence 24, 169-204 (1984)
16. Fürnkranz, J.: Separate-and-conquor rule learning. Artificial Intelligence Review 13, 3-54 (1999)
17. Garrett, S.M., Coghill, G.M., King, R.D., Srinivasan, A.: On learning qualitative models of qualitative and real-valued data (Technical Report UWA-DCS-01-037). University of Wales, Aberystwyth (2001)
18. Gawthrop, P.J., Smith, L.P.S.: Metamodelling: Bond graphs and dynamic systems. Prentice Hall, Hemel Hempstead, Herts, England (1996)
19. Gold, E.M.: Language identification in the limit. Information and Control 10, 447-474 (1967)
20. Hau, DT., Coiera, E.W.: Learning qualitative models of dynamic systems. Machine Learning 26, 177-211 (1993)
21. Iwasaki, Y., Simon, H.A.: Causality in device behavior. Artificial Intelligence (See also de Kleer and Brown's rebuttal and Iwasaki and Simon's reply to their rebuttal in the same volume of this journal) 29, 3-32 (1986)
22. Kakas, A.C., Kowalski, R.A., Toni, F.: Abductive logic programming. Journal of Logic and Computation 2, 719-770 (1992)
23. King, R.D., Muggleton, S., Lewis, R.A., Sternberg, M.J.E.: Drug design by machine learning -the use of inductive logic programming to model the structure-activityrelationships of trimethoprim analogs binding to dihydrofolate-reductase. In: Proceedings of the National Academy of Sciences of the USA, vol. 89, pp. 11322-11326 (1992)
24. King, R.D., Srinivasan, A.: The discovery of indicator variables for QSAR using inductive logic programming. Journal of Computer-Aided Molecular Design 11, 571-580 (1997)
25. Koza, J.R.: Genetic programming. MIT Press, Cambridge, MA (1992)
26. Kraan, I.C., Richards, B.L., Kuipers, B. J.: Automatic abduction of qualitative models. In: Proceedings of the Fifth International Workshop on Qualitative Reasoning about Physical Systems, Austin, TX, pp. 295-301 (1991)
27. Kuipers, B.: Qualitative simulation. Artificial Intelligence 29, 289-338 (1986)
28. Kuipers, B.: Qualitative reasoning. MIT Press, Cambridge, MA (1994)
29. Laird, J.E., Rosenbloom, P.S., Newell, A.: Chunking in SOAR: The anatomy of a general learning mechanism. Machine Learning 1, 11-46 (1986)
30. Lavrač, N., Džeroski, S.: Inductive logic programming: Techniques and applications., Ellis-Horwood, New York (1994)
31. Ljung, L.: System identification: Theory for the user, 2nd edn. PRT Prentice Hall, Upper Saddle River, NJ (1999)
32. Mitchell, T.M.: Version spaces: An approach to concept learning. Doctoral dissertation, Stanford University, CA (1979)
33. Muggleton, S.: Inductive logic programming. Academic Press, London (1992)
34. Muggleton, S.: Inverse entailment and PROGOL. New Generation Computing 13, 245286 (1995)
35. Muggleton, S.: Learning from positive data. In: Proceedings of the Sixth International Workshop on Inductive Logic Programming, Stockholm, Sweden, pp. 358-376 (1996)
36. Muggleton, S., Feng, C.: Efficient induction of logic programs. In: Proceedings of the First Conference on Algorithmic Learning Theory, Tokyo, Japan, pp. 368-381 (1990)
37. Plotkin, G.: Automatic methods of inductive inference. Doctoral dissertation, Edinburgh University (1971)
38. Ramachandran, S., Mooney, R.J., Kuipers, B.J.: Learning qualitative models for systems with multiple operating regions. In: Working Papers of the Eighth International Workshop on Qualitative Reasoning about Physical Systems, Nara, Japan, pp. 212223 (1994)
39. Richards, B.L., Kraan, I., Kuipers, B.J.: Automatic abduction of qualitative models. In: Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, CA, pp. 723-728 (1992)
40. Richards, B.L., Mooney, R.J.: Automated refinement of first-order horn-clause domain theories. Machine Learning 19, 95-131 (1995)
41. Say, A.C.C, Kuru, S.: Qualitative system indentification: deriving structure from behavior. Artificial Intelligence 83, 75-141 (1996)
42. Shoup, T.E.: A practical guide to computer methods for engineers. Prentice-Hall, Englewood Cliffs, NJ (1979)
43. Srinivasan, A.: Aleph web site (2000), http://web.comlab.ox.ac.uk/oucl/ research/ areas/macb-learn/Aleph/aleph_toc.html
44. Srinivasan, A., King, R.D.: Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. Data Mining and Knowledge Discovery 3, 37-57 (1999)
45. Srinivasan, A., Muggleton, S.H., Sternberg, M.J.E., King, R.D.: Theories for mutagenicity: A study in first-order and feature-based induction. Artificial Intelligence 85, 277-299 (1996)
46. Sternberg, M.J.E., King, R.D., Lewis, R.A., Muggleton, S.: Application of machine learning to structural molecular biology. Philosophical Transactions of the Royal Society of London Series B - Biological Sciences 344, 365-371 (1994)
47. Todorovski, L., Dzěroski, S.: Declarative bias in equation discovery. In: Proceedings of the Fourteenth International Conference on Machine Learning, Nashville, TN, pp. 376-384 (1997)
48. Yamamoto, A.: Revising the logical foundations of inductive logic programming systems with ground reduced programs. New Generation Computing 17, 119-127 (1999)