The technique of forgetting in knowledge representation has been shown to be a powerful and useful knowledge engineering tool with widespread application. Yet, very little research has been done on how different policies of forgetting, or use of different forgetting operators, affects the inferential strength of the original theory. The goal of this paper is to define loss functions for measuring changes in inferential strength based on intuitions from model counting and probability theory. Properties of such loss measures are studied and a pragmatic knowledge engineering tool is proposed for computing loss measures using Problog. The paper includes a working methodology for studying and determining the strength of different forgetting policies, in addition to concrete examples showing how to apply the theoretical results using Problog. Although the focus is on forgetting, the results are much more general and should have wider application to other areas.

翻译：知识表示中的遗忘技术已被证明是一种功能强大且实用的知识工程工具，具有广泛的应用场景。然而，关于不同遗忘策略或不同遗忘算子如何影响原始理论的推理强度，目前相关研究极为有限。本文旨在基于模型计数和概率论的基本原理定义损失函数，以量化推理强度的变化。我们研究了此类损失测度的性质，并提出了一种基于Problog计算损失测度的实用知识工程工具。本文不仅包含研究并确定不同遗忘策略强度的方法论框架，还通过具体实例展示了如何利用Problog应用理论成果。尽管本文聚焦于遗忘领域，但研究结论具有普适性，可广泛推广至其他相关领域。