@inproceedings{hofmannAbstractingNoisyRobot2023,
  abstract = {Abstraction is a commonly used process to represent some low-level system by a more coarse specification with the goal to omit unnecessary details while preserving important aspects. While recent work on abstraction in the situation calculus has focused on non-probabilistic domains, we describe an approach to abstraction of probabilistic and dynamic systems. Based on a variant of the situation calculus with probabilistic belief, we define a notion of bisimulation that allows to abstract a detailed probabilistic basic action theory with noisy actuators and sensors by a possibly non-stochastic basic action theory. By doing so, we obtain abstract Golog programs that omit unnecessary details and which can be translated back to a detailed program for actual execution. This simplifies the implementation of noisy robot programs, opens up the possibility of using non-stochastic reasoning methods (e.g., planning) on probabilistic problems, and provides domain descriptions that are more easily understandable and explainable.},
  author = {Hofmann, Till and Belle, Vaishak},
  booktitle = {Proceedings of the 22nd {{International Conference}} on {{Autonomous Agents}} and {{Multiagent Systems}} ({{AAMAS}})},
  eprint = {2204.03536},
  eprinttype = {arXiv},
  extraurl = {https://dl.acm.org/doi/10.5555/3545946.3598681},
  keywords = {hideurl,hidearxiv},
  pubtype = {2-conference-papers},
  shorthand = {AAMAS23},
  title = {Abstracting Noisy Robot Programs},
  url = {https://dl.acm.org/doi/10.5555/3545946.3598681},
  year = {2023}
}

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