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FLAIRS
2001
15 years 8 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
15 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
15 years 7 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»
15 years 10 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»
15 years 1 months 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