Peter Stone's Selected Publications

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State Aggregation through Reasoning in Answer Set Programming

Ginevra Gaudioso, Matteo Leonetti, and Peter Stone. State Aggregation through Reasoning in Answer Set Programming. In Proceedings of the IJCAI Workshop on Autonomous Mobile Service Robots (WSR 16), July 2016.

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Abstract

For service robots gathering increasing amounts of information, the ability to realize which bits are relevant and which are not for each task is going to be crucial. Abstraction is, indeed, a fundamental characteristic of human intelligence, while it is still a challenge for AI. Abstraction through machine learning can inevitably only work in hindsight: the agent can infer whether some information was pertinent from experience. However, service robots are required to be functional and effective quickly, and their users often cannot let the robot explore the environment long enough. We propose a method to perform state aggregation through reasoning in answer set programming, which allows the robot to determine if a piece of information is irrelevant for the task at hand before taking the first action. We demonstrate our method on a simulated mobile service robot, carrying out tasks in an office environment.

BibTeX Entry

@InProceedings{IJCAI-WSR-gaudioso,
  author = {Ginevra Gaudioso and Matteo Leonetti and Peter Stone},
  title = {State Aggregation through Reasoning in Answer Set Programming},
  booktitle = {Proceedings of the IJCAI Workshop on Autonomous Mobile Service Robots (WSR 16)},
  location = {New York City, NY, USA},
  month = {July},
  year = {2016},
  abstract = {For service robots gathering increasing amounts of information, the ability to 
realize which bits are relevant and which are not for each task is going to be 
crucial. Abstraction is, indeed, a fundamental characteristic of human 
intelligence, while it is still a challenge for AI. Abstraction through machine 
learning can inevitably only work in hindsight: the agent can infer whether some 
information was pertinent from experience. However, service robots are required 
to be functional and effective quickly, and their users often cannot let the 
robot explore the environment long enough. We propose a method to perform state 
aggregation through reasoning in answer set programming, which allows the robot 
to determine if a piece of information is irrelevant for the task at hand before 
taking the first action. We demonstrate our method on a simulated mobile service 
robot, carrying out tasks in an office environment.},
}

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