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» Learning Partially Observable Deterministic Action Models
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ATAL
2006
Springer
13 years 11 months ago
Action awareness: enabling agents to optimize, transform, and coordinate plans
As agent systems are solving more and more complex tasks in increasingly challenging domains, the systems themselves are becoming more complex too, often compromising their adapti...
Freek Stulp, Michael Beetz
ICML
2007
IEEE
14 years 8 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
CVPR
2007
IEEE
14 years 9 months ago
Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video
We present a scalable approach to recognizing and describing complex activities in video sequences. We are interested in long-term, sequential activities that may have several par...
Benjamin Laxton, Jongwoo Lim, David J. Kriegman
ECCV
2004
Springer
14 years 9 months ago
Modeling and Synthesis of Facial Motion Driven by Speech
We introduce a novel approach to modeling the dynamics of human facial motion induced by the action of speech for the purpose of synthesis. We represent the trajectories of a numbe...
Payam Saisan, Alessandro Bissacco, Alessandro Chiu...
AIED
2009
Springer
14 years 2 months ago
I learn from you, you learn from me: How to make iList learn from students
We developed a new model for iList, our system that helps students learn linked list. The model is automatically extracted from past student data, and allows iList to track student...
Davide Fossati, Barbara Di Eugenio, Stellan Ohlsso...