# Peter Stone's Selected Publications

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## Mobile Robot Planning using Action Language $\cal BC$ with Hierarchical Domain Abstractions

Shiqi Zhang, Fangkai Yang, Piyush Khandelwal, and Peter Stone. Mobile Robot Planning using Action Language $\cal BC$ with Hierarchical Domain Abstractions. In The 7th Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP), July 2014.

### Abstract

Action language $\cal BC$ provides an elegant way of formalizing robotic domains which need to be expressed using default logic as well as indirect and recursive action effects. However, generating plans efficiently for large domains using $\cal BC$ can be challenging, even when state-of-the-art answer set solvers are used. In this paper, we investigate the computational gains achieved by describing task planning domains at different abstraction levels using $\cal BC$, where lower levels describe more domain details by adding fluents not included in higher levels and actions at different levels are formalized independently. Two algorithms are presented to efficiently calculate the near-optimal short and low-cost plans respectively. We present a case study where at least an order of magnitude speedup was achieved in a robot mail collection task using hierarchical domain abstractions.

### BibTeX Entry

@InProceedings{ASPOCP14-zhang,
author = {Shiqi Zhang and Fangkai Yang and Piyush Khandelwal and Peter Stone},
title = {Mobile Robot Planning using Action Language ${\cal BC}$ with Hierarchical Domain Abstractions},
booktitle = {The 7th Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP)},
location = {Vienna, Austria},
month = {July},
year = {2014},
abstract = {
Action language ${\cal BC}$ provides an elegant way of formalizing robotic
domains which need to be expressed using default logic as well as indirect
and recursive action effects. However, generating plans efficiently for
large domains using ${\cal BC}$ can be challenging, even when
state-of-the-art answer set solvers are used. In this paper, we investigate
the computational gains achieved by describing task planning domains at
different abstraction levels using ${\cal BC}$, where lower levels describe
more domain details by adding fluents not included in higher levels and
actions at different levels are formalized independently. Two algorithms are
presented to efficiently calculate the near-optimal short and low-cost plans
respectively. We present a case study where at least an order of magnitude
speedup was achieved in a robot mail collection task using hierarchical
domain abstractions.
},
}


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