Peter Stone's Selected Publications

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Integrated Commonsense Reasoning and Probabilistic Planning

Shiqi Zhang and Peter Stone. Integrated Commonsense Reasoning and Probabilistic Planning. In Proceedings of 2017 ICAPS Workshop on Planning and Robotics, June 2017.

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Abstract

Commonsense reasoning and probabilistic planning are two of the most important research areas in artificial intelligence. This paper focuses on Integrated commonsense Reasoning and probabilistic Planning (IRP) problems. On one hand, commonsense reasoning algorithms aim at drawing conclusions using structured knowledge that is typically provided in a declarative way. On the other hand, probabilistic planning algorithms aim at generating an action policy that can be used for action selection under uncertainty. Intuitively, reasoning and planning techniques are good at ``understanding the world'' and ``accomplishing the task'' respectively. This paper discusses the complementary features of the two computing paradigms, presents the (potential) advantages of their integration, and summarizes existing research on this topic.

BibTeX Entry

@InProceedings{PlanRob17-Zhang,
  author = {Shiqi Zhang and Peter Stone},
  title = {Integrated Commonsense Reasoning and Probabilistic Planning},
  booktitle = {Proceedings of 2017 ICAPS Workshop on Planning and Robotics},
  location = {Pittsburgh, PA},
  month = {June},
  year = {2017},
  abstract = {
    Commonsense reasoning and probabilistic planning are two 
    of the most important research areas in artificial intelligence.
    This paper focuses on Integrated commonsense Reasoning
    and probabilistic Planning (IRP) problems. On one hand,
    commonsense reasoning algorithms aim at drawing conclusions
    using structured knowledge that is typically provided
    in a declarative way. On the other hand, probabilistic planning
    algorithms aim at generating an action policy that can be
    used for action selection under uncertainty. Intuitively, reasoning
    and planning techniques are good at ``understanding
    the world'' and ``accomplishing the task'' respectively. This
    paper discusses the complementary features of the two computing
    paradigms, presents the (potential) advantages of their
    integration, and summarizes existing research on this topic.
  },
}

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