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NIPS
1998
15 years 7 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
IAT
2007
IEEE
16 years 9 days ago
Dynamic Data Driven Multi-Agent Simulation
Networks of sensors and simulation models of the physical environment have been implemented separately, often using agent-based methodologies. Some work has been done in providing...
Gary M. Pereira
ICRA
2002
IEEE
161views Robotics» more  ICRA 2002»
15 years 11 months ago
A Method for Co-Evolving Morphology and Walking Pattern of Biped Humanoid Robot
— In this paper, we present a method for co-evolving structures and controller of biped walking robots. Currently, biped walking humanoid robots are designed manually on trial-an...
Ken Endo, Fuminori Yamasaki, Takashi Maeno, Hiroak...
AAAI
1996
15 years 7 months ago
Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations
: Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...
Craig Boutilier, David Poole
CEC
2003
IEEE
15 years 11 months ago
Stochastic neural network models for gene regulatory networks
AbstractRecent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of geno...
Tianhai Tian, Kevin Burrage