Peter Stone's Selected Publications

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Planning in Action Language $\cal BC$ while Learning Action Costs for Mobile Robots

Piyush Khandelwal, Fangkai Yang, Matteo Leonetti, Vladimir Lifschitz, and Peter Stone. Planning in Action Language $\cal BC$ while Learning Action Costs for Mobile Robots. In International Conference on Automated Planning and Scheduling (ICAPS), June 2014.

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Abstract

The action language $\cal BC$ provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how $\cal BC$ can be used for robot task planning to solve these issues. Additionally, action costs are incorporated with planning to produce optimal plans, and we estimate these costs from experience making planning adaptive. This paper presents the first application of $\cal BC$ on a real robot in a realistic domain, which involves human-robot interaction for knowledge acquisition, optimal plan generation to minimize navigation time, and learning for adaptive planning.

BibTeX Entry

@InProceedings{ICAPS14-khandelwal,
  author = "Piyush Khandelwal and Fangkai Yang and Matteo Leonetti and Vladimir
    Lifschitz and Peter Stone",
  title = "Planning in Action Language ${\cal BC}$ while Learning Action Costs
    for Mobile Robots",
  booktitle = "International Conference on Automated Planning and Scheduling
    (ICAPS)",
  month = "June",
  year = "2014",
  abstract = { 
    The action language ${\cal BC}$ provides an elegant way of formalizing
      dynamic domains which involve indirect effects of actions and recursively
      defined fluents. In complex robot task planning domains, it may be
      necessary for robots to plan with incomplete information, and reason
      about indirect or recursive action effects. In this paper, we demonstrate
      how ${\cal BC}$ can be used for robot task planning to solve these
      issues. Additionally, action costs are incorporated with planning to
      produce optimal plans, and we estimate these costs from experience making
      planning adaptive.  This paper presents the first application of ${\cal
        BC}$ on a real robot in a realistic domain, which involves human-robot
        interaction for knowledge acquisition, optimal plan generation to
        minimize navigation time, and learning for adaptive planning.
  },
}

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