Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy (2015)
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.
In Proceedings of the 13th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR), Lexington, KY, USA, September 2015.

Slides (PDF)
Piyush Khandelwal Ph.D. Alumni piyushk [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu
Shiqi Zhang Postdoctoral Alumni szhang [at] cs utexas edu