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.

该研究提出了一种抽象方法，将高层次、非随机的机器人模型映射到低层次、随机的模型中，从而消除传感器噪声对机器人行为模型的影响，简化行为模型的可理解性。

在随机领域中使用抽象化进行可解释的机器人程序设计

Using Abstraction for Interpretable Robot Programs in Stochastic Domains

The problem of belief tracking in the presence of stochastic actions and
observations is pervasive and yet computationally intractable. In this work we
show however that probabilistic beliefs can be maintained in factored form
exactly and efficiently across a number of causally closed beams, when the
state variables that appear in more than one beam obey a form of backward
determinism. Since computing marginals from the factors is still
computationally intractable in general, and variables appearing in several
beams are not always backward-deterministic, the basic formulation is extended
with two approximations: forms of belief propagation for computing marginals
from factors, and sampling of non-backward-deterministic variables for making
such variables backward-deterministic given their sampled history. Unlike,
Rao-Blackwellized particle-filtering, the sampling is not used for making
inference tractable but for making the factorization sound. The resulting
algorithm involves sampling and belief propagation or just one of them as
determined by the structure of the model.

本文研究了在随机行为和观测存在的情况下，信念跟踪问题无法计算的问题。作者提出了一种新的算法，该算法可以保持概率信念在因果封闭的 beam 中的确切且高效的分解形式，扩展了基本公式与两种近似方法：信念传播形式和采样方法。