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  1. Proceedings of the International Workshop on Affective-Aware Virtual Agents and Social Robots (AFFINE '09)
  2. Designing a game companion for long-term social interaction
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A natural head pose and eye gaze dataset
Evaluating emotional algorithms using psychological scales
Hybrid text affect sensing system for emotional language analysis
Towards an intelligent affective multimodal virtual agent for uncertain environments
A Wizard-of-Oz game for collecting emotional audio data in a children-robot interaction
An integrated approach to emotional speech and gesture synthesis in humanoid robots
Appraising emotional events during a real-time interactive game
Expressive virtual modalities for augmenting the perception of affective movements
An exploration of user engagement in HCI
Designing a game companion for long-term social interaction
Postural expressions of emotion in a motion captured database and in a humanoid robot

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Designing a game companion for long-term social interaction

Content Provider ACM Digital Library
Author Leite, Iolanda Pereira, André Paiva, Ana Martinho, Carlos Castellano, Ginevra McOwan, Peter W.
Abstract The ability to understand the user's affective states and expressions is an important requirement for a robot companion. In this paper we focus on an interaction scenario where an iCat robot plays chess with children. We discuss the challenges to address in the design of an affect recognition system for a game companion and we present the initial steps carried out to build a framework for the dynamic modeling of the user's affect. We conclude by providing examples of how we intend to exploit information about the user's affect to generate an empathic behavior in our game companion.
Starting Page 1
Ending Page 3
Page Count 3
File Format PDF
ISBN 9781605586922
DOI 10.1145/1655260.1655270
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-11-06
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Non-verbal behavior Human-robot interaction Contextual information Affect recognition
Content Type Text
Resource Type Article
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