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ECML
2004
Springer
16 years 6 days ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
IVA
2009
Springer
16 years 1 months ago
Teaching Computers to Conduct Spoken Interviews: Breaking the Realtime Barrier with Learning
Abstract. Several challenges remain in the effort to build software capable of conducting realtime dialogue with people. Part of the problem has been a lack of realtime flexibili...
Gudny Ragna Jonsdottir, Kristinn R. Thóriss...
CSE
2008
IEEE
16 years 1 months ago
Adaptation to Dynamic Resource Availability in Ad Hoc Grids through a Learning Mechanism
Ad-hoc Grids are highly heterogeneous and dynamic networks, one of the main challenges of resource allocation in such environments is to find mechanisms which do not rely on the ...
Behnaz Pourebrahimi, Koen Bertels
IROS
2008
IEEE
125views Robotics» more  IROS 2008»
16 years 1 months ago
Dynamic correlation matrix based multi-Q learning for a multi-robot system
—Multi-robot reinforcement learning is a very challenging area due to several issues, such as large state spaces, difficulty in reward assignment, nondeterministic action selecti...
Hongliang Guo, Yan Meng
AAAI
2007
15 years 9 months ago
Machine Learning for Automatic Mapping of Planetary Surfaces
We describe an application of machine learning to the problem of geomorphic mapping of planetary surfaces. Mapping landforms on planetary surfaces is an important task and the fi...
Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta