Peter Stone's Selected Publications

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Planning in Answer Set Programming while Learning Action Costs for Mobile Robots

Fangkai Yang, Piyush Khandelwal, Matteo Leonetti, and Peter Stone. Planning in Answer Set Programming while Learning Action Costs for Mobile Robots. In AAAI Spring 2014 Symposium on Knowledge Representation and Reasoning in Robotics (AAAI-SSS), March 2014.

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Abstract

For mobile robots to perform complex missions, it may be necessary for them to plan with incomplete information and reason about the indirect effects of their actions. Answer Set Programming (ASP) provides an elegant way of formalizing domains which involve indirect effects of an action and recursively defined fluents. In this paper, we present an approach that uses ASP for robotic task planning, and demonstrate how ASP can be used to generate plans that acquire missing information necessary to achieve the goal. Action costs are also incorporated with planning to produce optimal plans, and we show how these costs can be estimated from experience making planning adaptive. We evaluate our approach using a realistic simulation of an indoor environment where a robot learns to complete its objective in the shortest time.

BibTeX Entry

@InProceedings{AAAISSS14-yang,
  author = {Fangkai Yang and Piyush Khandelwal and Matteo Leonetti and Peter Stone},
  title = {Planning in Answer Set Programming while Learning Action Costs for
    Mobile Robots},
  booktitle = {AAAI Spring 2014 Symposium on Knowledge Representation and Reasoning in Robotics (AAAI-SSS)},
  location = {Stanford, California, USA},
  month = {March},
  year = {2014},
  abstract = {
    For mobile robots to perform complex missions, it may be necessary for them
    to plan with incomplete information and reason about the indirect effects
    of their actions. Answer Set Programming (ASP) provides an elegant way of
    formalizing domains which involve indirect effects of an action and
    recursively defined fluents. In this paper, we present an approach that
    uses ASP for robotic task planning, and demonstrate how ASP can be used
    to generate plans that acquire missing information necessary to achieve
    the goal. Action costs are also incorporated with planning to produce
    optimal plans, and we show how these costs can be estimated from
    experience making planning adaptive. We evaluate our approach using a
    realistic simulation of an indoor environment where a robot learns to
    complete its objective in the shortest time.
  },
}

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