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
A cost minimization approach to human behavior recognition
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Evaluating motion graphs for character animation
ACM Transactions on Graphics (TOG)
Modeling human behavior for virtual training systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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In this paper we demonstrate a method for fine-grained modeling of a synthetic agentýs physical capabilities 驴 running, jumping, sneaking, and other modes of movement. Using motion capture data acquired from human subjects, we extract a motion graph and construct a cost map for the space of agent actions. We show how a planner can incorporate this cost model into the planning process to select between equivalent goal-achieving plans. We explore the utility of our model in three different capacities: 1) modeling other agents in the environment; 2) representing heterogeneous agents with different physical capabilities; 3) modeling agent physical states (e.g., wounded or tired agents). This technique can be incorporated into applications where human-like, high-fidelity physical models are important to the agentsý reasoning process, such as virtual training environments.