论文标题
抽象嘈杂的机器人程序
Abstracting Noisy Robot Programs
论文作者
论文摘要
抽象是一个常用的过程，可以通过更粗糙的规范来表示一些低级系统，其目标是省略不必要的细节，同时保留重要方面。尽管计算中的最新抽象工作集中在非稳定领域，但我们描述了一种概率和动态系统的抽象方法。基于有概率信念的微积分的变体，我们定义了一个仿真的概念，该概念允许通过可能是非稳态的基本动作理论来抽象具有嘈杂的执行器和传感器的详细概率基本行动理论。通过这样做，我们获得了抽象的Golog程序，这些程序省略了不必要的详细信息，可以将其转换回详细的程序进行实际执行。这简化了嘈杂的机器人程序的实现，开辟了在概率问题上使用非探索推理方法（例如计划）的可能性，并提供了更容易理解和解释的域描述。
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.
