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。

因子化概率信念跟踪

Factored Probabilistic Belief Tracking

Strictly proper scoring rules (SPSR) are incentive compatible for eliciting
information about random variables from strategic agents when the principal can
reward agents after the realization of the random variables. They also quantify
the quality of elicited information, with more accurate predictions receiving
higher scores in expectation. In this paper, we extend such scoring rules to
settings where a principal elicits private probabilistic beliefs but only has
access to agents' reports. We name our solution \emph{Surrogate Scoring Rules}
(SSR). SSR build on a bias correction step and an error rate estimation
procedure for a reference answer defined using agents' reports. We show that,
with a single bit of information about the prior distribution of the random
variables, SSR in a multi-task setting recover SPSR in expectation, as if
having access to the ground truth. Therefore, a salient feature of SSR is that
they quantify the quality of information despite the lack of ground truth, just
as SPSR do for the setting \emph{with} ground truth. As a by-product, SSR
induce \emph{dominant truthfulness} in reporting. Our method is verified both
theoretically and empirically using data collected from real human forecasters.

本文提出了一种称为代理评分规则（Surrogate Scoring Rules, SSR）的方法，在私有概率信念的情况下，实现了激励兼容性和支配性诚实性，将之应用于多任务场景中，它们能够评估信息质量，即使没有绝对答案。作者通过真实数据的分析和经验验证，证明了这种方法的有效性。

替代评分规则

Surrogate Scoring Rules

Agents interacting with an incompletely known world need to be able to reason
about the effects of their actions, and to gain further information about that
world they need to use sensors of some sort. Unfortunately, both the effects of
actions and the information returned from sensors are subject to error. To cope
with such uncertainties, the agent can maintain probabilistic beliefs about the
state of the world. With probabilistic beliefs the agent will be able to
quantify the likelihood of the various outcomes of its actions and is better
able to utilize the information gathered from its error-prone actions and
sensors. In this paper, we present a model in which we can reason about an
agent's probabilistic degrees of belief and the manner in which these beliefs
change as various actions are executed. We build on a general logical theory of
action developed by Reiter and others, formalized in the situation calculus. We
propose a simple axiomatization that captures an agent's state of belief and
the manner in which these beliefs change when actions are executed. Our model
displays a number of intuitively reasonable properties.

介绍了一种能够帮助代理人处理不确定性的概率信念模型并建议了一种能够捕捉代理人信念状态的简单公理化方法，该方法基于 Reiter 等人开发的行动的一般逻辑理论并在情境演算中进行形式化。