Research Initiation: Induction of Casual Theories

研究者将调查一般理论的习得情况 通过观察事件的例子及其 后果 因果关系理论是关于 使人相信一个行为具有 必要的后果。 例如，人们倾向于选择 在空间和时间上与结果接近的原因。 工作 建立在计算机模型的先前实现之上，该计算机模型使用 因果关系理论作为一种明确的偏见，以促进 因果关系理论的获得（即，推理规则 对动作效果的知识进行编码，例如“玻璃物体 当被足够的力量击打时会破裂。"). 新作品是一个 调查的计算模型，以获得这些一般 从例子中的原则。 学习计划的输入将 由一个时间序列的命题，代表 世界各国。 输入表示将是 通过模拟构建（以允许通过 变化的参数，例如数据中的噪声量），以及 通过手工编码的因果变化的例子，从儿童的 介绍性科学文本（以确保学习计划 将能够解释各种常见的因果关系， 关系）。

The researcher will investigate the acquisition of general theory of causality by observing examples of events and their consequences. A theory of causality is information about the principles that lead one to believe that an action has a necessary consequence. For example, people tend to select a cause which is close in space and time to an effect. The work builds on prior implementation of a computer model which uses a theory of causality as an explicit bias to facilitate the acquisition of a theory of causation (i.e., inference rules encode knowledge of the effect of actions, such as "glass objects break when struck with sufficient force."). The new work is an investigation of computational models for acquiring these general principles from examples. Input to the learning program will consists of a temporal sequence of propositions representing the states of the world. The input representation will be constructed by simulation (to allow systematic experimentation by varying parameters such as the amount of noise in the data) and by hand coding examples of causal changes from children's introductory science texts (to insure that the learning program will be able to account for a wide variety of common causal relationships).