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AIPS
2009
13 years 8 months ago
Computing Robust Plans in Continuous Domains
We define the robustness of a sequential plan as the probability that it will execute successfully despite uncertainty in the execution environment. We consider a rich notion of u...
Christian Fritz, Sheila A. McIlraith
AAAI
2000
13 years 9 months ago
Towards Feasible Approach to Plan Checking under Probabilistic Uncertainty: Interval Methods
The main problem of planning is to find a sequence of actions that an agent must perform to achieve a given objective. An important part of planning is checking whether a given pl...
Raul Trejo, Vladik Kreinovich, Chitta Baral
ECP
1997
Springer
105views Robotics» more  ECP 1997»
13 years 11 months ago
Planning, Learning, and Executing in Autonomous Systems
Systems that act autonomously in the environment have to be able to integrate three basic behaviors: planning, execution, and learning. Planning involves describing a set of action...
Ramón García-Martínez, Daniel...

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
IROS
2009
IEEE
206views Robotics» more  IROS 2009»
14 years 2 months ago
Bayesian reinforcement learning in continuous POMDPs with gaussian processes
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...
Patrick Dallaire, Camille Besse, Stéphane R...