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Planning in Action Language $\cal BC$ while Learning Action Costs for Mobile Robots.
Piyush
Khandelwal, Fangkai Yang, Matteo
Leonetti, Vladimir Lifschitz, and Peter
Stone.
In International Conference on Automated Planning and Scheduling (ICAPS), June 2014.
The action language $\cal BC$ provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how $\cal BC$ can be used for robot task planning to solve these issues. Additionally, action costs are incorporated with planning to produce optimal plans, and we estimate these costs from experience making planning adaptive. This paper presents the first application of $\cal BC$ on a real robot in a realistic domain, which involves human-robot interaction for knowledge acquisition, optimal plan generation to minimize navigation time, and learning for adaptive planning.
@InProceedings{ICAPS14-khandelwal,
author = "Piyush Khandelwal and Fangkai Yang and Matteo Leonetti and Vladimir
Lifschitz and Peter Stone",
title = "Planning in Action Language ${\cal BC}$ while Learning Action Costs
for Mobile Robots",
booktitle = "International Conference on Automated Planning and Scheduling
(ICAPS)",
month = "June",
year = "2014",
abstract = {
The action language ${\cal BC}$ provides an elegant way of formalizing
dynamic domains which involve indirect effects of actions and recursively
defined fluents. In complex robot task planning domains, it may be
necessary for robots to plan with incomplete information, and reason
about indirect or recursive action effects. In this paper, we demonstrate
how ${\cal BC}$ can be used for robot task planning to solve these
issues. Additionally, action costs are incorporated with planning to
produce optimal plans, and we estimate these costs from experience making
planning adaptive. This paper presents the first application of ${\cal
BC}$ on a real robot in a realistic domain, which involves human-robot
interaction for knowledge acquisition, optimal plan generation to
minimize navigation time, and learning for adaptive planning.
},
}
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