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» Using model knowledge for learning inverse dynamics
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JAIR
2008
148views more  JAIR 2008»
15 years 3 months ago
Learning Partially Observable Deterministic Action Models
We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the ac...
Eyal Amir, Allen Chang
134
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IROS
2008
IEEE
191views Robotics» more  IROS 2008»
15 years 10 months ago
Local Gaussian process regression for real-time model-based robot control
— High performance and compliant robot control requires accurate dynamics models which cannot be obtained analytically for sufficiently complex robot systems. In such cases, mac...
Duy Nguyen-Tuong, Jan Peters
105
Voted
CI
2002
92views more  CI 2002»
15 years 3 months ago
Model Selection in an Information Economy: Choosing What to Learn
As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance...
Christopher H. Brooks, Robert S. Gazzale, Rajarshi...
150
Voted
ICML
2009
IEEE
16 years 4 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
126
Voted
IJON
2006
84views more  IJON 2006»
15 years 3 months ago
Dynamic regulation of spike-timing dependent plasticity in electrosensory processing
This study investigates the control of spike-timing dependent plasticity (STDP) by regulation of the dendritic spike threshold of the postsynaptic neuron. The control of synaptic ...
Patrick D. Roberts, Gerardo Lafferriere, Nathaniel...