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» A Logic for Planning under Partial Observability
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UAI
2008
13 years 9 months ago
Sampling First Order Logical Particles
Approximate inference in dynamic systems is the problem of estimating the state of the system given a sequence of actions and partial observations. High precision estimation is fu...
Hannaneh Hajishirzi, Eyal Amir

Publication
273views
13 years 2 months ago
Monte Carlo Value Iteration for Continuous-State POMDPs
Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algo...
Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
JAIR
2008
130views more  JAIR 2008»
13 years 7 months ago
Online Planning Algorithms for POMDPs
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
Stéphane Ross, Joelle Pineau, Sébast...
KBS
2007
86views more  KBS 2007»
13 years 7 months ago
On-line monitoring of plan execution: A distributed approach
The paper introduces and formalizes a distributed approach for the model-based monitoring of the execution of a plan, where concurrent actions are carried on by a team of mobile r...
Roberto Micalizio, Pietro Torasso
AAAI
2012
11 years 10 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...