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Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy.
Shiqi
Zhang, Fangkai Yang, Piyush
Khandelwal, and Peter Stone.
In Proceedings of the 13th International
Conference on Logic Programming and Non-monotonic Reasoning (LPNMR), September 2015.
Demo Video
Planning in real-world environments can be challenging for intelligent robots due to incomplete domain knowledge that results from unpredictable domain dynamism, and due to lack of global observability. Action language BC can be used for planning by formalizing the preconditions and (direct and indirect) effects of actions, and is especially suited for planning in robotic domains by incorporating defaults with the incomplete domain knowledge. However, planning with BC is very computationally expensive, especially when action costs are considered. We introduce algorithm PlanHG for formalizing BC domains at different abstraction levels in order to trade optimality for significant efficiency improvement when aiming to minimize overall plan cost. We observe orders of magnitude improvement in efficiency compared to a standard “flat” planning approach.
@InProceedings{LPNMR15-zhang,
author = {Shiqi Zhang and Fangkai Yang and Piyush Khandelwal and Peter Stone},
title = {Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy},
booktitle = {Proceedings of the 13th International Conference on Logic
Programming and Non-monotonic Reasoning (LPNMR)},
location = {Lexington, KY, USA},
month = {September},
year = {2015},
abstract = {
Planning in real-world environments can be challenging for intelligent
robots due to incomplete domain knowledge that results from unpredictable
domain dynamism, and due to lack of global observability. Action language BC
can be used for planning by formalizing the preconditions and (direct and
indirect) effects of actions, and is especially suited for planning in
robotic domains by incorporating defaults with the incomplete domain
knowledge. However, planning with BC is very computationally expensive,
especially when action costs are considered. We introduce algorithm PlanHG
for formalizing BC domains at different abstraction levels in order to trade
optimality for significant efficiency improvement when aiming to minimize
overall plan cost. We observe orders of magnitude improvement in efficiency
compared to a standard “flat†planning approach.
},
wwwnote={<a href="https://youtu.be/-QpFj7BbiRU"> Demo Video</a>},
}
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