Peter Stone's Selected Publications

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Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy

Shiqi Zhang, Fangkai Yang, Piyush Khandelwal, and Peter Stone. Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy. In Proceedings of the 13th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR), September 2015.
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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.

BibTeX Entry

@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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