@inproceedings{AbstractionXLoKR22,
  title = {Using Abstraction for Interpretable Robot Programs in Stochastic Domains},
  author = {Till Hofmann and Vaishak Belle},
  booktitle = {Proceedings of the 3rd Workshop on Explainable Logic-Based Knowledge Representation (XLoKR)},
  year = {2022},
  abstract = {
    A robot's actions are inherently stochastic, as its sensors are noisy
    and its actions do not always have the intended effects. For this
    reason, the agent language Golog has been extended to models with
    degrees of belief and stochastic actions. While this allows more precise
    robot models, the resulting programs are much harder to comprehend,
    because they need to deal with the noise, e.g., by looping until some
    desired state has been reached with certainty, and because the resulting
    action traces consist of a large number of actions cluttered with sensor
    noise. To alleviate these issues, we propose to use abstraction. We
    define a high-level and nonstochastic model of the robot and then map
    the high-level model into the lower-level stochastic model. The
    resulting programs are much easier to understand, often do not require
    belief operators or loops, and produce much shorter action traces.
  },
  url = {https://kbsg.rwth-aachen.de/papers/xlokr22-abstraction.pdf},
  url_Slides = {https://kbsg.rwth-aachen.de/papers/xlokr22-abstraction-slides.pdf},
  doi = {10.48550/arXiv.2207.12763}
}

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