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ATAL
2005
Springer
15 years 6 months ago
Using decision-theoretic models to enhance agent system survivability
A survivable agent system depends on the incorporation of many recovery features. However, the optimal use of these features requires the ability to assess the actual state of the...
Anthony R. Cassandra, Marian H. Nodine, Shilpa Bon...
IJRR
2010
162views more  IJRR 2010»
14 years 11 months ago
Planning under Uncertainty for Robotic Tasks with Mixed Observability
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...
91
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AAAI
2012
13 years 2 months ago
POMDPs Make Better Hackers: Accounting for Uncertainty in Penetration Testing
Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic...
Carlos Sarraute, Olivier Buffet, Jörg Hoffman...
AAAI
2010
15 years 1 months ago
PUMA: Planning Under Uncertainty with Macro-Actions
Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan...
Ruijie He, Emma Brunskill, Nicholas Roy
ICML
2004
IEEE
16 years 1 months ago
Utile distinction hidden Markov models
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Part...
Daan Wierstra, Marco Wiering