Showing papers on "User story published in 1989"

KNOME: Modeling What the User Knows in UC

Abstract: KNOME is the user modeling component of UC, a natural language consultation system for the UNIX operating system. During the course of an interactive session with a user, KNOME infers the user’s level of expertise from the dialog and maintains a model of the user’s knowledge of the UNIX domain. KNOME’s model of the user makes use of a double-stereotype system in which one set of stereotypes represents the user’s expertise and another represents the difficulty level of the information. KNOME is used in UC to help disambiguate the user’s statements, avoid telling the user something that the user already knows, take advantage of prior user knowledge in presenting new information, and detect situations where the user lacks pertinent facts or where the user has a misconception. UC also models its own knowledge of UNIX with meta-knowledge (explicit facts about the limitations of the system’s own knowledge base), which is used to help in correcting user misconceptions.

The Use of Explicit User Models in a Generation System for Tailoring Answers to the User’s Level of Expertise

Abstract: A question-answering program providing access to a large amount of data will be most useful if it can tailor its answers to each individual user. In particular, a user’s level of knowledge about the domain of discourse is an important factor in this tailoring if the answer provided is to be both informative and understandable to the user. In this research, we address the issue of how the user’s domain knowledge can affect an answer. By studying texts, we found that the user’s level of domain knowledge affected the kind of information provided and not just the amount of information, as was previously assumed. Depending on the user’s assumed domain knowledge, a description can be either parts-oriented or process-oriented. Thus the user’s level of expertise in a domain can guide a system in choosing the appropriate facts from the knowledge base to include in an answer. We propose two distinct descriptive strategies that can be used in a question-answering program, and show how they can be mixed to include the appropriate information from the knowledge base, given the user’s domain knowledge. We have implemented these strategies in TAILOR, a computer system that generates descriptions of devices. TAILOR uses one of the two discourse strategies identified in texts to construct a description for either a novice or an expert. It can merge the strategies automatically to produce a wide range of different descriptions to users who fall between the extremes of novice or expert, without requiring an a priori set of user stereotypes.

User Models and Conversational Settings: Modeling the User’s Wants

Abstract: In general, the cycle of system development can be viewed as consisting of four phases: theoretical considerations, design practical implementation and experimentation evaluation of the system, documentation reorganizing the field of research, thus using the experience which has been gained by workers in the field.