Abstract
In this paper, we present a unified approach to handling uncertainty during plan inference in cooperative consultations. This approach assists with the following aspects of the plan inference process: inferring a user's intentions among a number of possibilities, deciding whether to admit an unlikely interpretation of the user's request or to actively acquire information from the user, determining whether a perceived ambiguity in a user's request is to be resolved by heuristics or by soliciting information from the user, and deciding whether a recognized intention is sufficiently detailed so that a plan may be proposed to satisfy it. We define an information-theoretic measure which allows us to determine the amount of information in an interpretation of a user's request, and show how this measure is combined with probabilities of interpretations to give preference to interpretations that are better defined. Our approach is implemented as part of a computerized consultant that operates as a travel agent.
The permission of the Director of Telstra Research Laboratories to publish this work is gratefully acknowledged.
Preview
Unable to display preview. Download preview PDF.
References
Allen, J.F. and Perrault, C.R., Analyzing Intention in Utterances. Artificial Intelligence 15, 143–178, 1981.
Appelt, D.E. and Pollack, M.E., Weighted Abduction for Plan Ascription. User Modeling and User Adapted Interaction 2(1–2), 1–25, 1992.
Bauer, M., Acquisition of User Preferences for Plan Recognition. In UM96 Proceedings — the Fifth International Conference on User Modeling, 105–112, Kona, Hawaii, 1996.
Berger, J.O. and Berry, D.A., The Relevance of Stopping Rules in Statistical Inferences. In Gupta, S.S. and Berger, J.O. (Eds.), Statistical Decision Theory and Related Topics IV, Vol. 1, Springer Verlag, 1988.
Carberry, S., Modeling the User's Plans and Goals. Computational Linguistics 14(3), 23–37, 1988.
Carberry, S., Incorporating Default Inferences into Plan Recognition. In AAAI Proceedings — the Eighth National Conference on Artificial Intelligence, 471–478, Boston, Massachusetts, 1990.
Eller, R. and Carberry, S., A Meta-Rule Approach to Dynamic Plan Recognition. User Modeling and User Adapted Interaction 2(1–2), 27–53, 1992.
Goldman, R. and Charniak, E., A Probabilistic Model for Plan Recognition. In AAAI Proceedings — the Ninth National Conference on Artificial Intelligence, 160–165, Anaheim, California, 1991.
Kautz, H. and Allen, J.F., Generalized Plan Recognition. In AAAI Proceedings — the Fifth National Conference on Artificial Intelligence, 32–37, Philadelphia, Pennsylvania, 1986.
Kobsa, A., User Modeling and User Adapted Interaction 1(4), 2(1–2), 1991–2.
Konolige, K. and Pollack, M.E., Ascribing Plans to Agents. In IJCAI-89 Proceedings — the Eleventh International Joint Conference on Artificial Intelligence, 924–930, Detroit, Michigan, 1989.
Raskutti, B. (1993), Handling Uncertainty during Plan Recognition for Response Generation. Ph.D. Thesis, Department of Computer Science, Monash University.
Raskutti, B. and Zukerman, I., Acquisition of Information to Determine a User's Plan. In ECAI-94 Proceedings — the European Conference on Artificial Intelligence, 28–32, Amsterdam, The Netherlands, 1994.
Shannon, C.E. (1948), A Mathematical Theory of Communications. Bell System Technical Journals, October 1948.
Sullivan, M. and Cohen, P.R., An Endorsement based Plan Recognition Program. In IJCAI-85 Proceedings — the Ninth International Joint Conference on Artificial Intelligence, 8–23, Los Angeles, California, 1985.
van Beek, P. and Cohen, R., Resolving Plan Ambiguity for Cooperative Response Generation. In IJCAI-91 Proceedings — the Twelfth International Joint Conference on Artificial Intelligence, 938–944, Sydney, Australia, 1991.
Wu, D., Implications of Active User Model Acquisition for Decision-Theoretic Dialog Planning and Plan Recognition. User Modeling and User Adapted Interaction 1(2), 149–172, 1991.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raskutti, B., Zukerman, I. (1996). A unified approach to handling uncertainty during cooperative consultations. In: Foo, N., Goebel, R. (eds) PRICAI'96: Topics in Artificial Intelligence. PRICAI 1996. Lecture Notes in Computer Science, vol 1114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61532-6_8
Download citation
DOI: https://doi.org/10.1007/3-540-61532-6_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61532-3
Online ISBN: 978-3-540-68729-0
eBook Packages: Springer Book Archive