Using Decision Trees for Agent Modeling: Improving Prediction Performance

User Modeling and User-Adapted Interaction

Volumn 8, Issue 1-2, 1998, Pages 131-152

  Chiu, Bark Cheung ^a,b,c   Webb, Geoffrey I ^a,d  

^a DEAKIN UNIVERSITY   (Australia)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

^c UNIVERSITY OF WOLLONGONG   (Australia)

^d LA TROBE UNIVERSITY   (Australia)

Author keywords

Agent modeling; Decision tree; Inductive learning; Student modeling

Indexed keywords

DECISION THEORY; MATHEMATICAL MODELS; TREES (MATHEMATICS);

AGENT MODELING; FEATURE BASED MODELING; INDUCTIVE LEARNING; STUDENT MODELING;

ARTIFICIAL INTELLIGENCE;

EID: 0031681786     PISSN: 09241868     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (48)

1. Ali, K., Brunk, C., and Pazzani, M.: 1994, On learning multiple descriptions of a concept. In Proceedings of the Sixth International Conference on Tools with Artificial Intelligence, New Orleans, LA: IEEE Press, 476-483.
2. Amato, N. H. and Tsang, C. P.: 1990, Student modeling in a keyboard scale tutoring system. In C. J. Barter & M. J. Brooks (Eds.) Proceedings of the Second Australian Joint Conference on Artificial Intelligence, Berlin: Springer-Verlag, 225-239.
3. Anderson, J. R., Boyle, C. F., and Reiser, B. J.: 1985, Intelligent tutoring systems. Science, 228 456-462.
4. Anderson, J. R., Boyle, C. F., Corbett, A. T., and Lewis, M. W.: 1990, Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42 7-49.
5. Baffes, P., and Mooney, R.: 1996, Refinement-based student modeling and automated bug library construction. Journal of Artificial Intelligence in Education, 7(1) 75-117.
6. Balabanovic, M.: 1998, Exploring versus exploiting when learning user models for text recommendation. In this volume.
7. Breiman, L.: 1996, Bagging predictors. Machine Learning, 24 123-140.
8. Brown, J. S., and VanLehn, K.: 1980, Repair theory: A generative theory of bugs in procedural skills. Cognitive Science, 4 319-426.
9. Brown, J. S., and Burton, R. R.: 1978, Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2 155-192.
10. Burton, R. R, and Brown, J. S.: 1976, A tutoring and student modeling paradigm for gaming environments. Computer Science and Education. ACM SIGCSE Bulletin, 8(1) 236-246.
11. Carbonell, J. R.: 1970, AI in CAI: An artificial intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, 11(4) 190-202.
12. Chan, P. K., and Stolfo, S. J.: 1995, A comparative evaluation of voting and meta-learning on Partitioned data. In Prieditis, A., and Russell, S., eds., Proceedings of the 12th International Conference on Machine Learning, 90-98.
13. Chiu, B. C., Webb, G. I., and Kuzmycz, M.: 1997, A comparison of first-order and zeroth-order induction for input-output agent modeling. In Jameson A., Paris C., and Tasso C., eds., Proceedings of the Sixth International Conference on User Modeling, UM97, 347-358.
14. Corbett, A. T., and Anderson, J. R.: 1992, Student modeling and mastery learning in a computer-based programming tutor. In Frasson, C., Gauthier, G., and McCalla, G. I., eds., Intelligent Tutoring Systems. Berlin: Springer-Verlag, 413-420.
15. Desmoulins, C., and Van Labeke, N.: 1996, Towards student modeling in geometry with inductive logic programming. In Brna, P., Paiva, A., and Self, J., eds., Proceedings of the European Conference on Artificial Intelligence in Education, 94-100.
16. Dietterich, T., and Bakiri, G.: 1994, Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2 263-286.
17. Giangrandi, P., and Tasso, C.: 1995, Truth maintenance techniques for modeling students' behavior. Journal of Artificial Intelligence in Education, 6(2/3) 153-202.
18. Giangrandi, P., and Tasso, C.: 1996, Modeling the temporal evolution of student's knowledge. In Brna, P., Paiva, A., and Self, J., eds., Proceedings of the European Conference on Artificial Intelligence in Education, 184-190.
19. Gilmore, D., and Self, J.: 1988, The application of machine learning to intelligent tutoring systems. In Self, J., ed., Artificial Intelligence and Human Learning: Intelligent Computer-aided Instruction. London: Chapman and Hall, 179-196.
20. Goldstein, I. P.: 1979, The genetic graph: A representation for the evolution of procedural knowledge. International Journal of Man-machine Studies, 11(1) 51-77.
21. Hoppe, H. U.: 1994, Deductive error diagnosis and inductive error generalization for intelligent tutoring systems. Journal of Artificial Intelligence in Education, 5(1) 27-49.
22. Heath D., Kasif, S. and Salzberg, S.: 1996, Committees of decision trees. In Gorayska, B., and Mey, J., eds., Cognitive Technology: In Search of a Human Interface. Amsterdam: Elsevier Science B.V., 305-317.
23. Ikeda, M., Kono, Y., and Mizoguchi, R.: 1993, Nonmonotonic model inference: A formalization of student modeling. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence: IJCAI'93, 467-473.
24. Kohavi, R.: 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection. In Mellish, C. S., ed., Proceedings of the 14th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1137-1145.
25. Kuzmycz, M.: 1994, A dynamic vocabulary for student modeling. Proceedings of the Fourth International Conference on User Modeling, Hyannis, MA, 185-190.
26. Kuzmycz, M.: 1997, Resolving conflicting knowledge in student models. Proceedings of the Eighth World Conference on Artificial Intelligence in Education. Amsterdam: IOS Press, 522-529.
27. Kuzmycz, M., and Webb, G. I.: 1992, Evaluation of feature based modelling in subtraction. In Frasson, C., Gauthier, G., and McCalla, G. I., eds., Intelligent Tutoring Systems. Berlin: Springer-Verlag, 269-276.
28. Kwok, S., and Carter, C.: 1990, Multiple decision trees. Uncertainty in Artificial Intelligence, 4 327-335.
29. Langley, P., and Ohlsson, S.: 1984, Automated cognitive modeling. Proceedings of the National Conference on Artificial Intelligence, Austin, Texas, 193-197.
30. Langley, P., Wogulis, J., and Ohlsson, S.: 1990, Rules and principles in cognitive diagnosis. Diagnostic Monitoring of Skill and Knowledge Acquisition. Hillsdale, NJ: Erlbaum, 217-250.
31. Martin, J., and VanLehn, K.: 1995, Student assessment using Bayesian nets. International Journal of Human-Computer Studies, 42(6) 575-591.
32. Nock, R., and Cascuel, O.: 1995, On learning decision committees. Proceedings of the Twelfth International Conference on Machine Learning, San Mateo, CA: Morgan Kaufmann. 413-420.
33. Ohlsson, S., and Langley, P.: 1985, Identifying solution paths in cognitive diagnosis. Technical Report CMU-RI-TR-85-2, Carnegie-Mellon University, Pittsburgh, PA.
34. Oliver, J. J., and Hand, D. J.: 1995, On pruning and averaging decision trees. Proceedings of the Twelfth International Conference on Machine Learning, San Mateo, CA: Morgan Kaufmann, 430-437.
35. Quinlan, J. R.: 1986, The effect of noise on concept learning. In Michalski, R. S., Carbonell J. G. and Mitchell T.M. eds., Machine Learning: An Artificial Intelligence Approach, Vol. 2. San Mateo, CA: Morgan Kaufmann, 149-166.
36. Quinlan, J. R.: 1993, C4.5: Programs for Machine Learning San Mateo, CA: Morgan Kaufmann.
37. Salvia, A. A.: 1990, Introduction to Statistics. Saunders College Pub., Philadelphia.
38. Schapire, R. E.: 1990, The strength of weak learnability. Machine Learning, 5 197-227.
39. Sleeman, D.: 1982, Assessing aspects of competence in basic algebra. In Sleeman, D. H., and Brown, J. S., eds., Intelligent Tutoring Systems. London: Academic Press, 185-199.
40. Sleeman, D., Ward, R. D., Kelly, E., Martinak, R., and Moore, J.: 1991, An overview of recent studies with Pixie. In Goodyear, P., ed., Teaching Knowledge and Intelligent Tutoring. Norwood, NJ: Ablex, 173-185.
41. Ting, K. M., and Low, B. T.: 1997, Model combination in the multiple-data-batches scenario. Proceedings of the Ninth European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, Vol. 1224, Berlin: Springer-Verlag, 250-265.
42. Webb, G. I.: 1989, A machine learning approach to student modeling. Proceedings of the Third Australian Joint Conference on Artificial Intelligence, Melbourne, 195-205.
43. Webb, G. I.: 1991, Inside the Unification Tutor: The architecture of an intelligent educational system. In Proceedings of the Fourth Australian Society for Computers in Learning in Tertiary Education Conference, Launceston, 677-684.
44. Webb, G. I., Chiu, B. C., and Kuzmycz, M.: 1997, Comparative evaluation of alternative induction engines for Feature Based Modelling. International Journal of Artificial Intelligence in Education, 8, to be printed.
45. Webb, G. I., and Kuzmycz, M.: 1996, Feature based modeling: A methodology for producing coherent, dynamically changing models of agent's competencies. User Modeling and User-Adapted Interaction, 5(2) 117-150.
46. Wolpert, D. H.: 1992, Stacked generalisation. Neural Networks, 5 241-259.
47. Young, R., and O'Shea, T.: 1981, Errors in children's subtraction. Cognitive Science, 5(1) 153-177.
48. Zheng, Z., and Webb, G. I.: 1997, Lazy Bayesian Tree. Technical Report TC97/07, School of computing and mathematics, Deakin University.