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FLAIRS
2001
13 years 7 months ago
Probabilistic Planning for Behavior-Based Robots
Partially Observable Markov Decision Process models (POMDPs) have been applied to low-level robot control. We show how to use POMDPs differently, namely for sensorplanning in the ...
Amin Atrash, Sven Koenig
AAAI
1997
13 years 7 months ago
Model Minimization in Markov Decision Processes
Many stochastic planning problems can be represented using Markov Decision Processes (MDPs). A difficulty with using these MDP representations is that the common algorithms for so...
Thomas Dean, Robert Givan
ICML
2010
IEEE
13 years 6 months ago
Convergence of Least Squares Temporal Difference Methods Under General Conditions
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least square...
Huizhen Yu
ICCAD
1992
IEEE
137views Hardware» more  ICCAD 1992»
13 years 9 months ago
Equivalent design representations and transformations for interactive scheduling
High-level synthesis (HLS) requires more designer interaction to better meet the needs of experienced designers. However, attempts to create a highly interactive synthesis process...
Roger P. Ang, Nikil D. Dutt
NFM
2011
225views Formal Methods» more  NFM 2011»
13 years 22 days ago
Synthesis for PCTL in Parametric Markov Decision Processes
Abstract. In parametric Markov Decision Processes (PMDPs), transition probabilities are not fixed, but are given as functions over a set of parameters. A PMDP denotes a family of ...
Ernst Moritz Hahn, Tingting Han, Lijun Zhang