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CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot

Shiqi Zhang and Peter Stone. CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot. In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), January 2015.

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Abstract

In order to be fully robust and responsive to a dynamically changing real-world environment, intelligent robots will need to engage in a variety of simultaneous reasoning modalities. In particular, in this paper we consider their needs to i) reason with commonsense knowledge, ii) model their nondeterministic action outcomes and partial observability, and iii) plan toward maximizing long-term rewards. On one hand, Answer Set Programming (ASP) is good at representing and reasoning with commonsense and default knowledge, but is ill-equipped to plan under probabilistic uncertainty. On the other hand, Partially Observable Markov Decision Processes (POMDPs) are strong at planning under uncertainty toward maximizing long-term rewards, but are not designed to incorporate commonsense knowledge and inference. This paper introduces the CORPP algorithm which combines P-log, a probabilistic extension of ASP, with POMDPs to integrate commonsense reasoning with planning under uncertainty. Our approach is fully implemented and tested on a shopping request identification problem both in simulation and on a real robot. Compared with existing approaches using P-log or POMDPs individually, we observe significant improvements in both efficiency and accuracy.

BibTeX Entry

@InProceedings{AAAI15-szhang,
  author = {Shiqi Zhang and Peter Stone},
  title = {CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied
      to Dialog with a Mobile Robot},
  booktitle = {Proceedings of the 29th Conference on Artificial Intelligence (AAAI)},
  location = {Austin, Texas},
  month = {January},
  year = {2015},
  abstract = {
      In order to be fully robust and responsive to a dynamically changing
      real-world environment, intelligent robots will need to engage in a
      variety of simultaneous reasoning modalities. In particular, in this paper
      we consider their needs to i) reason with commonsense knowledge, ii) model
      their nondeterministic action outcomes and partial observability, and iii)
      plan toward maximizing long-term rewards. On one hand, Answer Set
      Programming (ASP) is good at representing and reasoning with commonsense
      and default knowledge, but is ill-equipped to plan under probabilistic
      uncertainty. On the other hand, Partially Observable Markov Decision
      Processes (POMDPs) are strong at planning under uncertainty toward
      maximizing long-term rewards, but are not designed to incorporate
      commonsense knowledge and inference. This paper introduces the CORPP
      algorithm which combines P-log, a probabilistic extension of ASP, with
      POMDPs to integrate commonsense reasoning with planning under uncertainty.
      Our approach is fully implemented and tested on a shopping request
      identification problem both in simulation and on a real robot. Compared
      with existing approaches using P-log or POMDPs individually, we observe
      significant improvements in both efficiency and accuracy.
  },
}

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