This paper presents Tyche, a Python library to facilitate probabilistic
reasoning in uncertain worlds through the construction, querying, and learning
of belief models. Tyche uses aleatoric description logic (ADL), which provides
computational advantages in its evaluation over other description logics. Tyche
belief models can be succinctly created by defining classes of individuals, the
probabilistic beliefs about them (concepts), and the probabilistic
relationships between them (roles). We also introduce a method of observation
propagation to facilitate learning from complex ADL observations. A
demonstration of Tyche to predict the author of anonymised messages, and to
extract author writing tendencies from anonymised messages, is provided. Tyche
has the potential to assist in the development of expert systems, knowledge
extraction systems, and agents to play games with incomplete and probabilistic
information.

该论文介绍了 Tyche Python 库，通过构建、查询和学习信念模型，在不确定的世界中促进概率推理。Tyche 使用 aleatoric 描述逻辑，可以通过定义个体类、对它们的概率信念（概念）和它们之间的概率关系（角色）简洁地创建信念模型。我们还介绍了一种观测传播方法来促进复杂的 ADL 观测的学习。展示了 Tyche 预测匿名消息的作者以及从匿名消息中提取作者写作倾向的能力。Tyche 有助于开发专家系统、知识提取系统以及代理处理不完整和概率信息的游戏。