Abstracting Noisy Robot Programs. Hofmann, T. & Belle, V. In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023. ˘rlhttps://dl.acm.org/doi/10.5555/3545946.3598681abstract bibtex 29 downloads 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.
@inproceedings{hofmannAbstractingNoisyRobot2023,
title = {Abstracting Noisy Robot Programs},
author = {Hofmann, Till and Belle, Vaishak},
year = 2023,
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.},
contribution = {The idea of extending abstraction to noisy programs is due to Vaishak Belle. I came up with the main results while Vaishak Belle provided frequent feedback. I wrote major parts of the paper, while Vaishak Belle provided critical feedback and helped clarifying major aspects.},
copyright = {All rights reserved},
credit = {{Methodology, Formal analysis, Writing - Original Draft, Writing - Review \& Editing, Visualization}},
publicationtype = {{original research article}},
shorthand = {AAMAS23},
keywords = {Computer Science - Artificial Intelligence},
note = {\url{https://dl.acm.org/doi/10.5555/3545946.3598681}},
booktitle = {Proceedings of the 22nd {{International Conference}} on {{Autonomous Agents}} and {{Multiagent Systems}} ({{AAMAS}})}
}
Downloads: 29
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