Showing papers in "User Modeling and User-adapted Interaction in 1994"
TL;DR: This paper investigates how hypertext — in its current node-and-link form — can be augmented by an adaptive, user-model-driven tool.
Abstract: Presentation of textual information is undergoing rapid transition. Millennia of experience writing linear documents is gradually being discarded in favor of non-linear hypertext writing. In this paper, we investigate how hypertext — in its current node-and-link form — can be augmented by an adaptive, user-model-driven tool. Currently the reader of a document has to adapt to that document — if the detail level is wrong the reader either skims the document or has to consult additional sources of information for clarification. The MetaDoc system not only has hypertext capabilities but also has knowledge about the documents it represents. This knowledge enables the document to modify its level of presentation to suit the user. MetaDoc builds and dynamically maintains a user model for each reader. The model tailors the presentation of the document to the reader. The three-dimensionality of MetaDoc allows the text presented to be changed either by the user model or through explicit user action. MetaDoc is more a documentation reading system rather than a hypertext navigation or reading tool. MetaDoc is a fully developed and debugged system that has been applied to technical documentation.
215 citations
TL;DR: Two applications of ANATOM-TUTOR’s user model are described: tailoring hypertext to the level of knowledge of the individual user; and generating explanations and questions in a simulated examination situation, also taking into consideration the individual users’level of knowledge.
Abstract: This article is a comparative description of the user modelling component of ANATOM-TUTOR, an intelligent anatomy tutoring system for use at university level. We introduce ITSs in general, discussing some of the psychological and pedagogical issues involved in using computers in education, and ANATOM-TUTOR in parlicular, and locate ANATOM-TUTOR’s user modelling component in the field of existing user models. Details of the user model’s construction and maintenance, the knowledge representation techniques used in it, and its relation to the domain knowledge base are then discussed. Two applications of ANATOM-TUTOR’s user model are described: (1) tailoring hypertext to the level of knowledge of the individual user; and (2) generating explanations and questions in a simulated examination situation, also taking into consideration the individual user’s level of knowledge.
121 citations
TL;DR: TECHDOC-I provides support to a user in the area of car maintenance by combining a number of ideas and work from various areas in a single system, add some unique features, and apply them to a new domain (instructions for car maintenance).
Abstract: There has been a great deal of research on specific issues of user modeling (eg, generation of explanations (Paris 88), implicit knowledge acquisition (Kass, Finin 87), exploitation of user feedback to compensate for the unreliability of user models (Moore, Paris 92), but to our knowledge no work has been done on how to integrate this work into a single system In TECHDOC-I we combine a number of ideas and work from various areas in a single system, add some unique features (eg, application of a double-stereotype mechanism to plan representation), and apply them to a new domain (instructions for car maintenance) TECHDOC-I provides support to a user in the area of car maintenance All maintenance activities are represented by plans, which consist of plan steps The dialogue between user and system is based on these plans A user model ensures that the system adapts the content of the output to the user The commands and explanations are given in natural language that is generated by a (multilingual) text generator
17 citations
TL;DR: This paper presents a method by which a detailed model of the user's relevant domain-specific, plan-oriented beliefs can gradually be formed by trying to understand user feedback in an on-going advisory dialog.
Abstract: An intelligent advisory system should be able to provide explanatory responses that correct mistaken user beliefs. This task requires the ability to form a model of the user's relevant beliefs and to understand and address feedback from users who are not satisfied with its advice. This paper presents a method by which a detailed model of the user's relevant domain-specific, plan-oriented beliefs can gradually be formed by trying to understand user feedback in an on-going advisory dialog. In particular, we consider the problem of constructing an automated advisor capable of participating in a dialog discussing which UNIX command should be used to perform a particular task. We show how to construct a model of a UNIX user's beliefs about UNIX commands from several different classes of user feedback. Unlike other approaches to inferring user beliefs, our approach focuses on inferring only the small set of beliefs likely to be relevant in contributing to the user's misconception. And unlike other approaches to providing advice, we focus on the task of understanding the user's descriptions of perceived problems with that advice.
7 citations
2 citations