Who's asking for help?: a Bayesian approach to intelligent assistance

ABSTRACT

Automated software customization is drawing increasing attention as a means to help users deal with the scope, complexity, potential intrusiveness, and ever-changing nature of modern software. The ability to automatically customize functionality, interfaces, and advice to specific users is made more difficult by the uncertainty about the needs of specific individuals and their preferences for interaction. Following recent probabilistic techniques in user modeling, we model our user with a dynamic Bayesian network (DBN) and propose to explicitly infer the "user's type" --- a composite of personality and affect variables --- in real time. We design the system to reason about the impact of its actions given the user's current attitudes. To illustrate the benefits of this approach, we describe a DBN model for a text-editing help task. We show, through simulations, that user types can be inferred quickly, and that a myopic policy offers considerable benefit by adapting to both different types and changing attitudes. We then develop a more realistic user model, using behavioural data from 45 users to learn model parameters and the topology of our proposed user types. With the new model, we conduct a usability experiment with 4 users and 4 different policies. These experiments, while preliminary, show encouraging results for our adaptive policy.

REFERENCES

• 1: David W. Albrecht , Ingrid Zukerman , Ann E. Nicholson, Pre-sending Documents on the WWW: A Comparative Study, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, p.1274-1279, July 31-August 06, 1999
• 2: J. Boger, P. Poupart, J. Hoey, C. Boutilier, G. Fernie, and A. Mihailidis. A decision-theoretic approach to task assistance for persons with dementia. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, pages 1293--1299, Edinburgh, Scotland, 2005.
• 3: Thorsten Bohnenberger , Anthony Jameson, When policies are better than plans: decision-theoretic planning of recommendation sequences, Proceedings of the 6th international conference on Intelligent user interfaces, p.21-24, January 14-17, 2001, Santa Fe, New Mexico, United States [doi>10.1145/359784.359829]
• 4: X. Boyen and D. Koller. Tractable inference for complex stochastic processes. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 33--42, Madison, WI, 1998.
• 5: Andrea Bunt , Cristina Conati , Joanna McGrenere, What role can adaptive support play in an adaptable system?, Proceedings of the 9th international conference on Intelligent user interface, January 13-16, 2004, Funchal, Madeira, Portugal [doi>10.1145/964442.964465]
• 6: Cristina Conati , Abigail Gertner , Kurt Vanlehn, Using Bayesian Networks to Manage Uncertainty in Student Modeling, User Modeling and User-Adapted Interaction, v.12 n.4, p.371-417, November 2002 [doi>10.1023/A:1021258506583]
• 7: Thomas Dean , Keiji Kanazawa, A model for reasoning about persistence and causation, Computational Intelligence, v.5 n.3, p.142-150, Aug. 1990 [doi>10.1111/j.1467-8640.1989.tb00324.x]
• 8: A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B, 1(39):1--38, 1977.
• 9: G. Foster, P. Langlais, and G. Lapalme. TransType: Text prediction for translators. In Proceedings of the Association of Computational Linguistics Demonstrations, pages 93--94, Philadelphia, 2002.
• 10: Krzysztof Gajos , Daniel S. Weld, SUPPLE: automatically generating user interfaces, Proceedings of the 9th international conference on Intelligent user interface, January 13-16, 2004, Funchal, Madeira, Portugal [doi>10.1145/964442.964461]
• 11: Peter Gorniak , David Poole, Building a Stochastic Dynamic Model of Application Use, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, p.230-237, June 30-July 03, 2000
• 12: R.L. Gorsuch. Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum, 1983.
• 13: R. Gunning. The techniques of Clear Writing. New York: McGraw-Hill, 1968.
• 14: T. Hasselbring and C. Glaser. Use of computer technology to help students with special needs. The Future of Children: Children and Computer Technology, 10(2):102--122, 2000.
• 15: Eric Horvitz , Johnson Apacible, Learning and reasoning about interruption, Proceedings of the 5th international conference on Multimodal interfaces, November 05-07, 2003, Vancouver, British Columbia, Canada [doi>10.1145/958432.958440]
• 16: E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 256--265, Madison, WI, 1998.
• 17: C. Huang and A. Darwiche. Inference in Belief Networks: A Procedural Guide. International Journal of Approximate Reasoning, 15(3):225--263, 1996.
• 18: Bowen Hui , Sotirios Liaskos , John Mylopoulos, Requirements Analysis for Customizable Software Goals-Skills-Preferences Framework, Proceedings of the 11th IEEE International Conference on Requirements Engineering, p.117, September 08-12, 2003
• 19: A. Jameson, B. Großmann-Hutter, L. March, R. Rummer, T. Bohnenberger, and F. Wittig. When actions have consequences: Empirically based decision making for intelligent user interfaces. Knowlege Based Systems, 14(1--2):75--92, 2001.
• 20: S. Kullback and R. Leibler. On information and sufficiency. Annals of Mathematical Statistics, 22(1):79--86, 1951.
• 21: Kevin Patrick Murphy , Stuart Russell, Dynamic bayesian networks: representation, inference and learning, 2002
• 22: F. Shein, T. Nantais, R. Nishiyama, C. Tam, and P. Marshall. Word cueing for persons with writing difficulties: WordQ. In Proceedings of the 16th Annual International Conference on Technology and Persons with Disabilities, Los Angeles, 2001.
• 23: Xiaoming Zhou , Cristina Conati, Inferring user goals from personality and behavior in a causal model of user affect, Proceedings of the 8th international conference on Intelligent user interfaces, January 12-15, 2003, Miami, Florida, USA [doi>10.1145/604045.604078]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Intelligent agents

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)

General Terms:
Human Factors

Keywords:
dynamic Bayesian networks, intelligent assistance, user modeling