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AR
2008
118views more  AR 2008»
13 years 5 months ago
Efficient Behavior Learning Based on State Value Estimation of Self and Others
The existing reinforcement learning methods have been seriously suffering from the curse of dimension problem especially when they are applied to multiagent dynamic environments. ...
Yasutake Takahashi, Kentarou Noma, Minoru Asada
ICASSP
2008
IEEE
14 years 6 days ago
Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?
While the ”‘quasi-state-of-the-art”’ towards acoustic emotion recognition relies on multivariate time-series analysis of e.g. pitch, energy, or MFCC by statistical functio...
Björn Schuller, Matthias Wimmer, Lorenz Moese...
ICML
1999
IEEE
13 years 10 months ago
Learning Hierarchical Performance Knowledge by Observation
Developing automated agents that intelligently perform complex real world tasks is time consuming and expensive. The most expensive part of developing these intelligent task perfo...
Michael van Lent, John E. Laird
ROBOCUP
2005
Springer
119views Robotics» more  ROBOCUP 2005»
13 years 11 months ago
Flexible Coordination of Multiagent Team Behavior Using HTN Planning
The domain of robotic soccer is known as a highly dynamic and non-deterministic environment for multiagent research. We introduce an approach using Hierarchical Task Network planni...
Oliver Obst, Joschka Boedecker
IICAI
2007
13 years 7 months ago
Modeling Temporal Behavior via Structured Hidden Markov Models: an Application to Keystroking Dynamics
Structured Hidden Markov Models (S-HMM) are a variant of Hierarchical Hidden Markov Models; it provides an abstraction mechanism allowing a high level symbolic description of the k...
Ugo Galassi, Attilio Giordana, Charbel Julien, Lor...