Predicting User Actions Using Interface Agents with Individual User Models
The incompleteness and uncertainty about the state of the world and about the consequences of actions are unavoidable. If we want to predict the performance of multiuser computing systems, we have the uncertainty of what the users are going to do, and how that affects system performance. Intelligent interface agent development is one way to mitigate the uncertainty about user behaviors by predicting what users will do based on learned users’ behaviors, preferences, and intentions. This work focuses on developing user models that can analyze and predict user behavior in multi-agent systems. We have developed a formal theory of user behavior prediction based on hidden Markov models. This work learns the user model through a time-series action analysis and abstraction by taking users’ preferences and intentions into account in order to formally define user modeling.
KeywordsPrediction Accuracy Hide Markov Model User Model User Behavior State Transition Probability
Unable to display preview. Download preview PDF.
- 1.J. Allen. Recognizing intentions from natural language utterances, In Computational Models of Discourse M. Brady and R. Berwick eds, The MIT Press. 1983. 155Google Scholar
- 2.D. Litman. Plan recognition and discourse analysis: an integrated approach for understanding dialogues. Ph.D. Thesis, University of Rochester. 1986. 155Google Scholar
- 3.S. Carberry. Plan recognition in natural language, Plan Recognition in Natural Language. The MIT Press, Cambridge, MA. 1990. 155Google Scholar
- 5.M. Bauer. Acquisition of user preferences for plan recognition, In Proceedings of the Fifth International Conference on User Modeling pp. 105–112. Kailua-Kona, Hawaii 1996. 155Google Scholar
- 6.N. Lesh and O. Etzioni. A sound and fast goal recognizer. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1704–1710. 1995. 155Google Scholar
- 7.D. Albrecht, I. Zukerman, A. Nicholson, and A. Bud. Towards a Bayesian model for keyhole plan recognition in large domains, In Proceedings of the Sixth International Conference on User Modeling pp. 365–376. Sardinia, Italy 1997. 155Google Scholar
- 10.J. Allen. Natural Language Understanding. The Benjamin/Cummings Publishing Company, 1995. 158Google Scholar
- 12.E. Charniak. Statistical Language Learning. The MIT Press, 1996. 166Google Scholar
- 13.J.J. Lee and R. McCartney. Partial Plan Recognition Using Predictive Agents. In Pacific-Rim International Workshop on Multi-Agent systems (PRIMA98), Springer-Verlag LNAI 1599, 1998. 159Google Scholar
- 14.B. Davison and H. Hirsh. Predicting Sequences of User Actions. In AAAI 98 Workshop Notes on Predicting the Future: AI Approaches to Time-Series Problems, 1998. 166Google Scholar