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Task Planning in Robotics: an Empirical Comparison of PDDL- and ASP-based Systems.
Yuqian
Jiang, Shiqi Zhang, Piyush
Khandelwal, and Peter Stone.
Frontiers of Information Technology
and Electronic Engineering, 20(3):363–373, Springer, March 2019.
Official version from Publisher's
Webpage
Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.
@Article{FITEE19-jiang,
author = {Yuqian Jiang and Shiqi Zhang and Piyush Khandelwal and Peter Stone},
title = {Task Planning in Robotics: an Empirical Comparison of PDDL- and ASP-based Systems},
journal = {Frontiers of Information Technology and Electronic Engineering},
year = {2019},
month = {March},
volume={20},
number={3},
pages={363--373},
publisher={Springer},
abstract = {
Robots need task planning algorithms to sequence actions toward
accomplishing goals that are impossible through individual actions.
Off-the-shelf task planners can be used by intelligent robotics
practitioners to solve a variety of planning problems. However, many
different planners exist, each with different strengths and weaknesses, and
there are no general rules for which planner would be best to apply to a
given problem. In this study, we empirically compare the performance of
state-of-the-art planners that use either the planning domain description
language (PDDL) or answer set programming (ASP) as the underlying action
language. PDDL is designed for task planning, and PDDL-based planners are
widely used for a variety of planning problems. ASP is designed for
knowledge-intensive reasoning, but can also be used to solve task planning
problems. Given domain encodings that are as similar as possible, we find
that PDDL-based planners perform better on problems with longer solutions,
and ASP-based planners are better on tasks with a large number of objects or
tasks in which complex reasoning is required to reason about action
preconditions and effects. The resulting analysis can inform selection among
general-purpose planning systems for particular robot task planning domains.
},
wwwnote={Official version from <a href="https://link.springer.com/article/10.1631/FITEE.1800514">Publisher's Webpage</a>},
}
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