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User Modeling and User-Adapted Interaction

, Volume 1, Issue 3, pp 203–258 | Cite as

Building a user model implicitly from a cooperative advisory dialog

  • Robert Kass
Article

Abstract

This paper reviews existing methods for building user models to support adaptive, interactive systems, identifies sigificant problems with these approaches, and describes a new method for implicitly acquiring user models from an ongoing user-system dialog. Existing explicit user model acquisition methods, such as user edited models or model building dialogs put additional burden on the user and introduce artificial model acquisition dialogs. Hand coding stereotypes, another explicit acquisition method, is a tedious and error-prone process. On the other hand, implicit acquisition techniques such as computing presuppositions or entailments either draw too few inferences to be generally useful, or too many to be trusted.

In contrast, this paper describes GUMAC, a General User Model Acquisition Component that uses heuristic rules to make default inferences about users' beliefs from their interaction with an advisory expert system. These rules are based on features of human action and conversation that constrain people's behavior and establish expectations about their knowledge. The application of these rules is illustrated with two examples of extended dialogs between users and an investment advisory system. During the course of these conversations, GUMAC is able to acquire an extensive model of the users' beliefs about the aspects of the domain considered in the dialog. These models, in turn, provide the sort of information needed by an explanation generator to tailor explanations the advisory system gives to its users.

Key words

User Model Acquisition Implicit Acquisition Belief Modelling User Model Representation General User Modelling User Modelling Shells Advisory Systems Explanation Tailoring 

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Copyright information

© Kluwer Academic Publishers 1991

Authors and Affiliations

  • Robert Kass
    • 1
  1. 1.EDS Center for Advanced ResearchAnn ArborUSA

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