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Jon7

Jonathan Gratch

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Research Professor of Computer Science and Psychology

Director for Virtual Human Research,

USC Institute for Creative Technologies

Co-Director, USC Computational Emotion Group

Editor-in-Chief, IEEE Transactions on Affective Computing

12015 Waterfront Way, Playa Vista, CA 90094

gratch AT ict.usc.edu

 

CSCI 534  (Spring 2014) Course Information

 

 

  Research

Bio

Projects

Publications

Vita

Emotion Links

Research Interests

Affective Computing, Cognitive Modeling, Human-Computer Interaction, Virtual Humans, Persuasive Technology

My research is directed toward developing human-like software agents for virtual training environments and to use these computational methods to concretize psychological theories of human behavior. Specifically, I investigate how algorithms can control the behavior of characters in virtual worlds, endowing them with an ability to think and engage in socio-emotional interactions with human users, using both verbal and nonverbal communication. Such methods can deepen our understanding of human behavior, by instantiating and systematically manipulating psychological theories. They also have wide application to such areas as training, entertainment and clinical diagnosis, assessment and treatment.

Emotion Modeling

Emotions have a pervasive impact over our lives. They influence how we perceive the world, how we make decisions, and play a key role in social communication. In the context of virtual training environments, emotions also play a key role in the believability of the simulation and the extent to which a student will feel immersed in the experience. In the Emotion Project, we develop models that allow synthetic characters to derive an emotional response to events in the world and respond with behaviors consistent with that emotional state. Unlike work in "believable agents" for entertainment, the focus is more constrained (modeling typical human behavior); there is much greater need for generality and much less tolerance for domain-specific knowledge.

EMA

Virtual Humans

The Virtual Human Project brings together research in intelligent tutoring, natural language recognition and generation, interactive narrative, emotional modeling, and immersive graphics and audio. The focus is on creating a highly realistic and compelling training environment. The system includes an 8'x30' wrap-around screen, 10.2 channels of immersive audio, and interactive synthetic humans that can interact with the trainee and respond emotionally to their decisions. Applications of this technology include the Mission Rehearsal Exercise [MRE Movie Clip] and the Stability and Support Operations [SASO Movie Clip] training prototypes.

Social Emotions and Rapport

When people interact their speech prosody, gesture, gaze, posture, and facial expression contribute to establishment of a sense of rapport. Rapport is argued to underlie success in negotiations, psychotherapeutic effectiveness, classroom performance and even susceptibility to hypnosis.  The rapport project uses machine vision and prosody analysis to create virtual humans that can detect and respond in real-time to human gestures, facial expressions and emotional cues and create a sense of rapport. These techniques have a demonstrable beneficial impact on human interaction. [Rapport Movie Clip]

Anthropomorphism in Human-Machine Interaction

There is growing interest in endowing machines with more human-like characteristics, fueled by the assumption that this will enhance the effectiveness of human-machine interactions. Research has demonstrated that machines can be made more human-like, but less research has considered if this benefits or harms human-machine team performance. Indeed, a review of the literature illustrates that human-like qualities can results in unintended and disruptive consequences. Sometimes, getting people to treat a computer like a human can often undermine some of the unique advantages machines bring to human-machine teams Our anthropomorphism aims to cast such contradictorily into an overall framework that can inform the design of effective machines. (e.g., see this example).

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