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» Guiding Agent Learning in Design
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TSMC
2008
146views more  TSMC 2008»
13 years 7 months ago
Decentralized Learning in Markov Games
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is tha...
Peter Vrancx, Katja Verbeeck, Ann Nowé
ATAL
2009
Springer
14 years 2 months ago
Learning a model of speaker head nods using gesture corpora
During face-to-face conversation, the speaker’s head is continually in motion. These movements serve a variety of important communicative functions. Our goal is to develop a mod...
Jina Lee, Stacy Marsella
ROBOCUP
2007
Springer
153views Robotics» more  ROBOCUP 2007»
14 years 1 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
ITS
2010
Springer
176views Multimedia» more  ITS 2010»
13 years 9 months ago
A Time for Emoting: When Affect-Sensitivity Is and Isn't Effective at Promoting Deep Learning
We have developed and evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to learners’...
Sidney K. D'Mello, Blair Lehman, Jeremiah Sullins,...
AAAI
1997
13 years 9 months ago
Navigation and Planning in a Mixed-Initiative User Interface
Mixed-initiative planning is one approach to building an intelligent decision-making environment. A mixedinitiative system shares decision-making responsibility with the user such...
Robert St. Amant