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IJRR
2011
218views more  IJRR 2011»
13 years 2 months ago
Motion planning under uncertainty for robotic tasks with long time horizons
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
LREC
2008
134views Education» more  LREC 2008»
13 years 9 months ago
Evaluating the Ontology underlying sMail - the Conceptual Framework for Semantic Email Communication
The lack of structure in the content of email messages makes it very hard for data channelled between the sender and the recipient to be correctly interpreted and acted upon. As a...
Simon Scerri, Myriam Mencke, Brian Davis, Siegfrie...
ICRA
2005
IEEE
263views Robotics» more  ICRA 2005»
14 years 1 months ago
An Integrated Path Planning and Control Framework for Nonholonomic Unicycles
— In this paper, navigation and control of autonomous mobile unicycle robots in a complex and partially known obstacleridden environment is considered. The unicycle dynamic model...
Kaustubh Pathak, Sunil Kumar Agrawal
AMAI
2004
Springer
14 years 1 months ago
A Framework for Sequential Planning in Multi-Agent Settings
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Piotr J. Gmytrasiewicz, Prashant Doshi
NIPS
2007
13 years 9 months ago
Bayes-Adaptive POMDPs
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...
Stéphane Ross, Brahim Chaib-draa, Joelle Pi...