771 search results - page 38 / 155 » Markov Decision Processes with Arbitrary Reward Processes |
110
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ATAL
2008
Springer
15 years 3 months ago
2008
Springer
The ability for an agent to reason under uncertainty is crucial for many planning applications, since an agent rarely has access to complete, error-free information about its envi...
125
Voted
IAT
2005
IEEE
15 years 7 months ago
2005
IEEE
Partially observable Markov decision process (POMDP) is commonly used to model a stochastic environment with unobservable states for supporting optimal decision making. Computing ...
107
Voted
ATAL
2010
Springer
15 years 3 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 ...
140
Voted
ICML
1995
IEEE
16 years 2 months ago
1995
IEEE
Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor fee...
109
Voted
ICTAI
2005
IEEE
15 years 7 months ago
2005
IEEE
Partially Observable Markov Decision Processes (POMDPs) provide a general framework for AI planning, but they lack the structure for representing real world planning problems in a...