24,781 research outputs found
Modeling Human Interaction to Design a Human-Computer Dialog System
This article presents the Cogni-CISMeF project, which aims at improving the
health information search engine CISMeF, by including a conversational agent
that interacts with the user in natural language. To study the cognitive
processes involved during information search, a bottom-up methodology was
adopted. An experiment has been set up to obtain human dialogs related to such
searches. The analysis of these dialogs underlines the establishment of a
common ground and accommodation effects to the user. A model of artificial
agent is proposed, that guides the user by proposing examples, assistance and
choices
Generating socially appropriate tutorial dialog
Analysis of student-tutor coaching dialogs suggest that good human tutors attend to and attempt to influence the motivational state of learners. Moreover, they are sensitive to the social face of the learner, and seek to mitigate the potential face threat of their comments. This paper describes a dialog generator for pedagogical agents that takes motivation and face threat factors into account. This enables the agent to interact with learners in a socially appropriate fashion, and foster intrinsic motivation on the part of the learner, which in turn may lead to more positive learner affective states
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
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