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AIPS
2006
14 years 11 months ago
Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
Malcolm J. A. Strens
ATAL
2010
Springer
14 years 11 months ago
Linear options
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Jonathan Sorg, Satinder P. Singh
IROS
2006
IEEE
217views Robotics» more  IROS 2006»
15 years 3 months ago
Active SLAM using Model Predictive Control and Attractor based Exploration
– Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, a...
Cindy Leung, Shoudong Huang, Gamini Dissanayake
PUK
2001
14 years 11 months ago
Plan Representation and Plan Execution in Multi-agent Systems for Robot Control
Hardware agents as a part of cooperative multi-agent systems act in dynamically changing environments and accomplish tasks jointly. Since the pure hybrid plan representation provid...
Alexander Osherenko
ATAL
1995
Springer
15 years 1 months ago
Experiences with an Architecture for Intelligent, Reactive Agents
This paper describes an implementation of the 3T robot architecture which has been under development for the last eight years. The architecs three levels of abstraction and descri...
R. Peter Bonasso, David Kortenkamp, David P. Mille...