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NIPS
2007
13 years 10 months ago
What makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
David Hsu, Wee Sun Lee, Nan Rong
AIPS
2008
13 years 11 months ago
HiPPo: Hierarchical POMDPs for Planning Information Processing and Sensing Actions on a Robot
Flexible general purpose robots need to tailor their visual processing to their task, on the fly. We propose a new approach to this within a planning framework, where the goal is ...
Mohan Sridharan, Jeremy L. Wyatt, Richard Dearden
SBMF
2009
Springer
126views Formal Methods» more  SBMF 2009»
14 years 1 months ago
Undecidability Results for Distributed Probabilistic Systems
Abstract. In the verification of concurrent systems involving probabilities, the aim is to find out the maximum/minimum probability that a given event occurs (examples of such ev...
Sergio Giro
IJFCS
2008
130views more  IJFCS 2008»
13 years 8 months ago
Equivalence of Labeled Markov Chains
We consider the equivalence problem for labeled Markov chains (LMCs), where each state is labeled with an observation. Two LMCs are equivalent if every finite sequence of observat...
Laurent Doyen, Thomas A. Henzinger, Jean-Fran&cced...
ML
2002
ACM
121views Machine Learning» more  ML 2002»
13 years 8 months ago
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Michael J. Kearns, Satinder P. Singh