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AIPS
2000
15 years 3 months ago
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-Driven Robot Behavior
This paper develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans. PHA...
Michael Beetz, Henrik Grosskreutz
ICASSP
2010
IEEE
15 years 14 days ago
A HMM-based speech synthesis system using a new glottal source and vocal-tract separation method
This paper introduces a HMM-based speech synthesis system which uses a new method for the Separation of Vocal-tract and LiljencrantsFant model plus Noise (SVLN). The glottal sourc...
Pierre Lanchantin, Gilles Degottex, Xavier Rodet
ATAL
2009
Springer
15 years 6 months ago
Effective bidding and deal identification for negotiations in highly nonlinear scenarios
Most real-world negotiation scenarios involve multiple, interdependent issues. These scenarios are specially challenging because the agents' utility functions are nonlinear, ...
Ivan Marsá-Maestre, Miguel A. López-...
GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
15 years 8 months ago
Learning noise
In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
Michael D. Schmidt, Hod Lipson
ICRA
2006
IEEE
87views Robotics» more  ICRA 2006»
15 years 8 months ago
Learning to Predict Slip for Ground Robots
— In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do t...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...