Leveraging users for efficient interruption management in agent-user systems

Proceedings - 2009 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2009

Volumn 2, Issue , 2009, Pages 123-130

  Shrot, Tammar ^a   Rosenfeld, Avi ^b   Kraus, Sarit ^a  

^a BAR ILAN UNIVERSITY   (Israel)

^b JERUSALEM COLLEGE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

AMOUNT OF INFORMATION; CLASSIFICATION ALGORITHM; COLLABORATIVE FILTERING METHODS; COLLABORATIVE SYSTEMS; COMMUNICATION BETWEEN AGENTS; COST AND BENEFITS; COST ESTIMATIONS; COURSE OF ACTION; INFORMATION SHARING; INTERRUPTION MANAGEMENT; NUMBER OF STATE;

COST BENEFIT ANALYSIS; COST ESTIMATING; INTELLIGENT AGENTS; LEARNING ALGORITHMS;

INFORMATION MANAGEMENT;

EID: 84856913585     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI-IAT.2009.140     Document Type: Conference Paper

References (23)

1. P. Adamczyk and B. Bailey, "If not now, when?: the effects of interruption at different moments within task execution," In CHI 04, pp. 271-278, 2004.
2. D. Same and B. J. Grosz, "Timing interruptions for better human-computer coordinated planning," In AAAI Spring Syrnp. on Distributed Plan and Schedule Management, 2006.
3. L. Dabbish and R. Kraut, "Controlling interruptions: Awareness displays and social motivation for coordination," in In Proc of CSCW 2004, ACM Press. ACM Press, 2004, pp. 182-191.
4. E. Kamar, B. J. Grosz, and D. Same, "Modeling user perception of interaction opportunities in collaborative human-computer settings," in AAAI 2007, 2007, pp. 1872-1873.
5. R. M. Bell and Y. Koren, "Improved neighborhood-based collaborative filtering," in Proc. KDD-Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
6. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, "An algorithmic framework for performing collaborative filtering," in SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. New York, NY, USA: ACM Press, 1999, pp. 230-237.
7. J. R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
8. B. Dasarathy, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. Computer Society Press, 1991.
9. M. Fleming and R. Cohen, "A user modeling approach to determining system initiative in mixed-initiative ai systems," In UM 01, pp. 54-63, 2001.
10. E. Horvitz and J. Apacible, "Learning and reasoning about interruption," In ICMI 03, pp. 20-27, 2003.
11. M. Tambe, E. Bowring, J. Pearce, P. Varakantham, P. Scerri, and F. Pynadath, "Electric elves: What went wrong and why," In AI Magazine, 2007.
12. S. T. Iqbal and B. P. Bailey, "Effects of intelligent notification management on users and their tasks," In CHIOS, pp. 93-102, 2008.
13. J. Xu, L.-J. Zhang, H. Lu, and Y. Li, 'The development and prospect of personalized tv program recommendation systems," in IEEE Fourth International Symposium on Multimedia Software Engineering (MSE'02), 2002, pp. 82-89.
14. Breese, Heckermen, and Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," in Technical report, Microsoft Research, 1998.
15. C. C. Aggarwal, J. L. Wolf, K. lung Wu, and P. S. Yu, "Hotting hatches an egg: A new graph-theoretic approach to collaborative filtering," in In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge discovery and data mining. ACM Press, 1999, pp. 201-212.
16. R. Lin, S. Kraus, and J. Tew, "Osgs - a personalized online store for e-commerce environments," Information Retrieval journal, vol. vol.7, no. 3-4, pp. 369-394, 2004.
17. S. E. Middleton, N. R. Shadbolt, and D. C. D. Roure, "Ontological user profiling in recommender systems," ACM Transactions on Information Systems (TOIS) 22(1), pp. 5488, 2004.
18. C.-S. Hwang and P.-J. Tsai, "A collaborative recommender system based on user association clusters," Lecture Note on Computer Science, vol. vol. 3806, pp. 463-469, 2005.
19. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," Proc. 10th International Conference on the World Wide Web, pp. 285-295, 2001.
20. G. Linden, B. Smith, and J. York, "Amazon.com recommendations: Item-to-item collaborative filtering," IEEE Internet Computing 7, pp. 76-80, 2003.
21. G. Karypis, "Evaluation of item-based top-n recommendation algorithms," in CIKM, 2001, pp. 247-254.
22. M. Vozalis and K. G. Margaritis, "On the combination of collaborative and item-based filtering," in presented at the 3rd Hellenic Conference on Artificial Intelligence (SETN 04), Samos, Greece, 2004.
23. K.-z. Huang, H.-q. Yang, I. King, and M. Lyu, Machine Learning: Modeling Data Locally and Globally. Springer-Verlag New York Inc. 2008.