Active preference learning for personalized calendar scheduling assistance

International Conference on Intelligent User Interfaces, Proceedings IUI

Volumn , Issue , 2005, Pages 90-97

  Gervasio, Melinda T ^a   Moffitt, Michael D ^b   Pollack, Martha E ^b   Taylor, Joseph M ^b   Uribe, Tomas E ^a  

^a SRI INTERNATIONAL   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Active learning; Adaptive user interfaces; Learning preferences; Machine learning

Indexed keywords

ACTIVE LEARNING; ADAPTIVE USER INTERFACES; LEARNING PREFERENCES;

ADAPTIVE ALGORITHMS; FEEDBACK; USER INTERFACES;

LEARNING SYSTEMS;

EID: 33644604723     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1040830.1040857     Document Type: Conference Paper

References (20)

1. Berry, P. M, Gervasio, M., Uribe, T. E., Myers, K., & Nitz, K. (2004). A personalized calendar assistant. Working Notes of the AAAI Spring Symposium on Interaction between Humans and Autonomous Systems over Extended Operation.
2. Cohn, D., Ghaharamani, Z., & Jordan, M. I. (1994). Active learning with statistical models. Advances in Neural Information Processing Systems 7.
3. Cristianini, N. & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines. Cambridge University Press.
4. Engelbrecht, A. P. (2001). Selective learning for multilayer feedforward neural networks. Fundamenta Informaticae, 45.
5. Ephrati, E., Zlotkin, G., & Rosenschein, J.S. (1994). A non manipulable meeting scheduling system. Proceedings of the 13th International Distributed AI Workshop. Seattle, WA.
6. Fiechter, C.-N. & Rogers, S. (2000). Learning subjective functions with large margins. Proceedings of the Seventeenth International Conference on Machine Learning, 287-294.
7. Fung, G., Mangasarian, O. L., & Shavlik, J. (2002). Knowledge-based support vector machine classifiers. Advances in Neural Information Processing Systems 15.
8. Gervasio, M., Iba, W., & Langley, P. (1999). Learning user evaluation functions for adaptive scheduling assistance. Proceedings of the Sixteenth International Conference on Machine Learning, 152-161.
9. Gratch, J. & Chien, S. (1996). Adaptive problem-solving for large-scale scheduling problems: a case study. Journal of Artificial Intelligence Research, 4:365-396.
10. Joachims, T. (2002). Optimizing search engines using clickthrough data. Proceedings of the ACM Conference on Knowledge Discovery and Data Mining.
11. Kiefer, J. (1959). Optimum experimental designs. J. R. Stat. Soc., series B, 21:272-304.
12. MacKay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Computation, 4 (4): 590-604.
13. Mitchell, T. M., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994). Experience with a learning personal assistant. Communications of the ACM 37 (7):80-91.
14. Miyashita, K. & Sycara, K. (1995). CABINS: a framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair. Artificial Intelligence, 76.
15. Morley, D. & Myers, K. (2004). The SPARK agent framework. Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems.
16. Murphy, K. P. (2003). Active learning of causal Bayes net structure. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
17. Sandip, S. & Durfee, E.H. (1998). A formal study of distributed meeting scheduling. Group Decision and Negotiation, 7:265-298.
18. Tong, S. & Koller, D. (2000). Support vector machine active learning with applications to text classification. Proceedings of the 17th International Conference on Machine Learning.
19. Thrun, S.B. (2002). The role of exploration in learning control. Neural Networks, 15 (4):665-687.
20. Zhang, W. & Dietterich, T. (1995). A reinforcement learning approach to job-shop scheduling. Proceedings of the 14th International Joint Conference on Artificial Intelligence.