73 search results - page 6 / 15 » Incremental Learning of Planning Operators in Stochastic Dom... |
ICRA
2007
IEEE
14 years 1 months ago
2007
IEEE
— This paper describes a new extension to the Rapidly–exploring Random Tree (RRT) path planning algorithm. The Particle RRT algorithm explicitly considers uncertainty in its do...
AIPS
1996
13 years 8 months ago
1996
Learning from experimentation allows a system to acquire planning domain knowledge by correcting its knowledge when an action execution fails. Experiments are designed and planned...
APIN
2004
13 years 7 months ago
2004
In this paper we are concerned with the problem of learning how to solve planning problems in one domain given a number of solved instances. This problem is formulated as the probl...
ICML
1996
IEEE
14 years 8 months ago
1996
IEEE
Generating good, production-quality plans is an essential element in transforming planners from research tools into real-world applications, but one that has been frequently overl...
UAI
2008
13 years 9 months ago
2008
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...