Peter Stone's Selected Publications

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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. Task Planning in Robotics: an Empirical Comparison of PDDL- and ASP-based Systems. Frontiers of Information Technology and Electronic Engineering, 20(3):363–373, Springer, March 2019.
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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.

BibTeX Entry

@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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