509 search results - page 59 / 102 » Using Learning for Approximation in Stochastic Processes |
109
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ACL
2010
15 years 14 days ago
2010
In this paper, we address the task of mapping high-level instructions to sequences of commands in an external environment. Processing these instructions is challenging--they posit...
114
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IAT
2006
IEEE
15 years 8 months ago
2006
IEEE
The Distributed Probabilistic Protocol (DPP) is a new, approximate algorithm for solving Distributed Constraint Satisfaction Problems (DCSPs) that exploits prior knowledge to impr...
112
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ICML
2008
IEEE
16 years 3 months ago
2008
IEEE
We consider the task of reinforcement learning in an environment in which rare significant events occur independently of the actions selected by the controlling agent. If these ev...
140
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NLPRS
2001
Springer
15 years 6 months ago
2001
Springer
Recent research in automated learning has focused on algorithms that learn from a combination of tagged and untagged data. Such algorithms can be referred to as semi-supervised in...
183
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PKDD
2010
Springer
15 years 10 days ago
2010
Springer
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...