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ICML
2004
IEEE
16 years 6 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
ICRA
2007
IEEE
126views Robotics» more  ICRA 2007»
15 years 11 months ago
A formal framework for robot learning and control under model uncertainty
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
Robin Jaulmes, Joelle Pineau, Doina Precup
AIPS
2003
15 years 6 months ago
Synthesis of Hierarchical Finite-State Controllers for POMDPs
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...
Eric A. Hansen, Rong Zhou
ATAL
2008
Springer
15 years 7 months ago
Exploiting locality of interaction in factored Dec-POMDPs
Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute an expressive framework for multiagent planning under uncertainty, but solving them is provabl...
Frans A. Oliehoek, Matthijs T. J. Spaan, Shimon Wh...
181
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SOCO
2010
Springer
15 years 3 days ago
Using evolution strategies to solve DEC-POMDP problems
Decentralized partially observable Markov decision process (DEC-POMDP) is an approach to model multi-robot decision making problems under uncertainty. Since it is NEXP-complete the...
Baris Eker, H. Levent Akin