论文标题
在随机域中使用抽象来解释机器人程序
Using Abstraction for Interpretable Robot Programs in Stochastic Domains
论文作者
论文摘要
机器人的动作本质上是随机的，因为它的传感器很嘈杂，其动作并不总是具有预期的效果。因此，代理语言已扩展到具有信念和随机行动程度的模型。尽管这允许更精确的机器人模型，但所产生的程序很难理解，因为它们需要处理噪声，例如，通过循环循环直到达到某种所需状态，并且由于所得的动作痕迹包括大量的动作，这些操作杂乱无章。为了减轻这些问题，我们建议使用抽象。我们定义机器人的高级和非稳态模型，然后将高级模型映射到较低级别的随机模型中。最终的程序更容易理解，通常不需要信念操作员或循环，并且会产生更短的动作痕迹。
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.
