Increasing believability in animated pedagogical agents
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Cognitive modeling: knowledge, reasoning and planning for intelligent characters
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Extending virtual humans to support team training in virtual reality
Exploring artificial intelligence in the new millennium
Automated derivation of behavior vocabularies for autonomous humanoid motion
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Evaluating motion graphs for character navigation
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Controllable real-time locomotion using mobility maps
GI '05 Proceedings of Graphics Interface 2005
Motion patches: building blocks for virtual environments annotated with motion data
ACM SIGGRAPH 2006 Papers
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Many simulations are populated with physically embodied agents capable of taking physical actions in the virtual world. Creating these agents, or virtual humans, is demanding; not only must the agents demonstrate visual verisimilitude, but they must plan and act in a way that is consistent with that of humans, especially for training simulations in which the participants are attempting to learn real-world skills. This article discusses an approach for adapting agent decision-making techniques to accurately model the physical capabilities of human subjects. To achieve this, the authors rely on human movement data acquired with a motion capture apparatus to build physically realistic models of human movement. To aid agents' planning, the authors construct a physical capability model for the agents, an accurate estimate of the time required for a real human to perform various movement sequences. A cost map over the space of agent actions is calculated by creating and stochastically sampling motion graphs assembled from the human data exemplars. The agents can use this cost model during the planning process to select between equivalent goal-achieving plans. This technique leverages highly accurate movement information acquired from human subjects to create agents that plan in physically realistic ways.