Learning commonsense knowledge from natural language text is nontrivial due
to reporting bias: people rarely state the obvious, e.g., "My house is bigger
than me." However, while rarely stated explicitly, this trivial everyday
knowledge does influence the way people talk about the world, which provides
indirect clues to reason about the world. For example, a statement like, "Tyler
entered his house" implies that his house is bigger than Tyler.
In this paper, we present an approach to infer relative physical knowledge of
actions and objects along five dimensions (e.g., size, weight, and strength)
from unstructured natural language text. We frame knowledge acquisition as
joint inference over two closely related problems: learning (1) relative
physical knowledge of object pairs and (2) physical implications of actions
when applied to those object pairs. Empirical results demonstrate that it is
possible to extract knowledge of actions and objects from language and that
joint inference over different types of knowledge improves performance.

这篇论文提出了一种从非结构化自然语言文本中推断对象和行为的物理知识的方法，并将知识获取作为两个相关问题的联合推理，即学习对象对的相对物理知识和应用于这些对象对时行为的物理含义。经验证明，从语言中提取对象和行为的知识是可行的，并且不同类型知识的联合推理可以提高性能。

动词物理学：动作和物体的相对物理知识

Verb Physics: Relative Physical Knowledge of Actions and Objects

Although many investigators affirm a desire to build reasoning systems that
behave consistently with the axiomatic basis defined by probability theory and
utility theory, limited resources for engineering and computation can make a
complete normative analysis impossible. We attempt to move discussion beyond
the debate over the scope of problems that can be handled effectively to cases
where it is clear that there are insufficient computational resources to
perform an analysis deemed as complete. Under these conditions, we stress the
importance of considering the expected costs and benefits of applying
alternative approximation procedures and heuristics for computation and
knowledge acquisition. We discuss how knowledge about the structure of user
utility can be used to control value tradeoffs for tailoring inference to
alternative contexts. We address the notion of real-time rationality, focusing
on the application of knowledge about the expected timewise-refinement
abilities of reasoning strategies to balance the benefits of additional
computation with the costs of acting with a partial result. We discuss the
benefits of applying decision theory to control the solution of difficult
problems given limitations and uncertainty in reasoning resources.

讨论在计算能力不足时使用替代逼近过程和启发式方法以及使用用户效用结构对推理进行量身定制来平衡部分结果的成本和收益的应用决策理论来解决困难问题的解决方案。

在计算资源限制下推理信念和行动

Reasoning About Beliefs and Actions Under Computational Resource  Constraints

I introduce a temporal belief-network representation of causal independence
that a knowledge engineer can use to elicit probabilistic models. Like the
current, atemporal belief-network representation of causal independence, the
new representation makes knowledge acquisition tractable. Unlike the atemproal
representation, however, the temporal representation can simplify inference,
and does not require the use of unobservable variables. The representation is
less general than is the atemporal representation, but appears to be useful for
many practical applications.

本文介绍了一种时间信念网络表示的因果独立，可以用来引导概率模型的知识工程师。该表示可以简化推理，而不需要使用不可观测的变量，适用于许多实际应用。

知识获取和推断的因果独立性

Causal Independence for Knowledge Acquisition and Inference

The noisy-or and its generalization noisy-max have been utilized to reduce
the complexity of knowledge acquisition. In this paper, we present a new
representation of noisy-max that allows for efficient inference in general
Bayesian networks. Empirical studies show that our method is capable of
computing queries in well-known large medical networks, QMR-DT and CPCS, for
which no previous exact inference method has been shown to perform well.

本文介绍了一种新的 noisy-max 表示法，可有效进行常规贝叶斯网络的推理。实证研究表明，我们的方法能够计算出多个大型医疗网络中的查询，其中之前未曾表现良好的准确推理方法。