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Planning in Action Language
BC
while Learning Action Costs for Mobile Robots (2014)
Piyush Khandelwal
, Fangkai Yang, Matteo Leonetti, Vladimir Lifschitz, and
Peter Stone
The action language
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
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
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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Citation:
In
International Conference on Automated Planning and Scheduling (ICAPS)
, June 2014.
Bibtex:
@inproceedings{ICAPS14-khandelwal, title={Planning in Action Language
BC
while Learning Action Costs for Mobile Robots}, author={Piyush Khandelwal and Fangkai Yang and Matteo Leonetti and Vladimir Lifschitz and Peter Stone}, booktitle={International Conference on Automated Planning and Scheduling (ICAPS)}, month={June}, url="http://www.cs.utexas.edu/users/ai-lab?khandelwal:icaps14", year={2014} }
People
Piyush Khandelwal
Ph.D. Alumni
piyushk [at] cs utexas edu
Peter Stone
Faculty
pstone [at] cs utexas edu
Areas of Interest
Planning
Robotics
Labs
Learning Agents