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» Inference and Learning in Planning
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KCAP
2009
ACM
15 years 4 months ago
POIROT: acquiring workflows by combining models learned from interpreted traces
The POIROT project is a four-year effort to develop an architecture that integrates the products of a number of targeted reasoning and learning components to produce executable re...
Mark H. Burstein, Fusun Yaman, Robert Laddaga, Rob...
JAIR
2010
145views more  JAIR 2010»
14 years 8 months ago
Planning with Noisy Probabilistic Relational Rules
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
Tobias Lang, Marc Toussaint
RAS
2010
164views more  RAS 2010»
14 years 8 months ago
Towards performing everyday manipulation activities
This article investigates fundamental issues in scaling autonomous personal robots towards open-ended sets of everyday manipulation tasks which involve high complexity and vague j...
Michael Beetz, Dominik Jain, Lorenz Mösenlech...
82
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IROS
2008
IEEE
123views Robotics» more  IROS 2008»
15 years 4 months ago
Learning predictive terrain models for legged robot locomotion
— Legged robots require accurate models of their environment in order to plan and execute paths. We present a probabilistic technique based on Gaussian processes that allows terr...
Christian Plagemann, Sebastian Mischke, Sam Prenti...
ML
1998
ACM
220views Machine Learning» more  ML 1998»
14 years 9 months ago
Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments
Abstract. Coordination is an essential technique in cooperative, distributed multiagent systems. However, sophisticated coordination strategies are not always cost-effective in all...
Toshiharu Sugawara, Victor R. Lesser