280 search results - page 27 / 56 » Planning for Markov Decision Processes with Sparse Stochasti... |
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AI
2006
Springer
14 years 10 months ago
2006
Springer
In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behaviour...
ATAL
2010
Springer
14 years 11 months ago
2010
Springer
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
NIPS
2007
14 years 11 months ago
2007
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
ISSS
1999
IEEE
15 years 2 months ago
1999
IEEE
The policy optimization problem for dynamic power management has received considerable attention in the recent past. We formulate policy optimization as a constrained optimization...
ECBS
2009
IEEE
15 years 4 months ago
2009
IEEE
Probabilistic models are useful for analyzing systems which operate under the presence of uncertainty. In this paper, we present a technique for verifying safety and liveness prop...