SOAR: an architecture for general intelligence
Artificial Intelligence
Unified theories of cognition
Artificial Intelligence
Virtual actors and avatars in a flexible user-determined-scenario environment
VRAIS '97 Proceedings of the 1997 Virtual Reality Annual International Symposium (VRAIS '97)
Modeling the Dynamics of Virtual Agent's Social Relations
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
Never Alone in the Crowd: A Microscopic Crowd Model Based on Emotional Contagion
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Petri Nets and Ontologies: Tools for the "Learning Player" Assessment in Serious Games
ICALT '11 Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies
A domain-independent framework for modeling emotion
Cognitive Systems Research
V3s: A virtual environment for risk-management training based on human-activity models
Presence: Teleoperators and Virtual Environments
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Taking human-factors into account in training simulations enables these systems to address issues such as coactivity and management training. However, systems which use virtual reality technologies are usually designed so as to immerse the users in perfectly realistic virtual environment, focusing only on technical gestures and prescribed procedures. Therefore, they can only tackle situations with little complexity, where the user's activity is highly constrained; otherwhise they can't ensure the pedagogic control and the relevance of the simulation. The HUMANS (HUman Models based Artificial eNvironments Software) platform is a generic framework, designed to build tailor-made virtual environments, which can be adapted to different application cases, technological configurations or pedagogical strategies. This suite rests upon the integration of multiple explicit models (domain, activity and risk model). In order to build ecologically valid virtual environments, these models represent not only the prescribed activity but the situated knowledge of operators about their tasks, including deviations from the procedures. Moreover, rather than a fixed world only populated by reactive characters, they are used to build a dynamic world populated with autonomous characters. These models can be used both by domain and procedures experts, and by computer experts. They are used both: to monitor learners actions, detecting errors and compromises; and to generate virtual characters behaviours.