The CAVE: audio visual experience automatic virtual environment
Communications of the ACM
Embodiment in conversational interfaces: Rea
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
The EMOTE model for effort and shape
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
The impact of eye gaze on communication using humanoid avatars
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A social reinforcement learning agent
Proceedings of the fifth international conference on Autonomous agents
BEAT: the Behavior Expression Animation Toolkit
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Integrated learning for interactive synthetic characters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Model of Nonverbal Communication and Interpersonal Relationship Between Virtual Actors
CA '96 Proceedings of the Computer Animation
Integrating Autonomous Behavior and User Control for Believable Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
An Overview of the COVEN Platform
Presence: Teleoperators and Virtual Environments
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Presence in immersive virtual environments
VRAIS '93 Proceedings of the 1993 IEEE Virtual Reality Annual International Symposium
Spelunking: experiences using the DIVE system on CAVE-like platforms
EGVE'01 Proceedings of the 7th Eurographics conference on Virtual Environments & 5th Immersive Projection Technology
Direct manipulation like tools for designing intelligent virtual agents
Lecture Notes in Computer Science
Applying direct manipulation interfaces to customizing player character behaviour
ICEC'06 Proceedings of the 5th international conference on Entertainment Computing
ACM Transactions on Applied Perception (TAP)
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Non-verbal communication, or "body language", is a critical component in constructing believable virtual characters. Most often, body language is implemented by a set of ad-hoc rules. We propose a new method for authors to specify and refine their character's body-language responses. Using our method, the author watches the character acting in a situation, and provides simple feedback on-line. The character then learns to use its body language to maximize the rewards, based on a reinforcement learning algorithm.