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...
In today’s dynamic computing environments, the available resources and even underlying computation engine can change during the execution of a program. Additionally, current tre...
Apala Guha, Jason Hiser, Naveen Kumar, Jing Yang, ...
— We explore the use of computational optimal control techniques for automated construction of policies in complex dynamic environments. Our implementation of dynamic programming...
Mike Stilman, Christopher G. Atkeson, James Kuffne...
Planning and scheduling attracts an unceasing attention of computer science community. However, despite of similar character of both tasks, in most current systems planning and sch...