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，具有广泛的应用前景。然而，关于不同遗忘策略或不同遗忘算子的使用如何影响原始理论的推理强度，目前的研究仍然非常有限。本文旨在基于模型计数和概率论的基本直觉，定义用于衡量推理强度变化的损失函数。我们研究了此类损失度量的性质，并提出了一种实用的知识工程工具，利用 Problog 计算损失度量。本文不仅包含用于研究和评估不同遗忘策略强度的工作方法论，还提供了具体示例，展示如何使用 Problog 应用理论结果。尽管本文重点关注遗忘问题，但所得结果具有更广泛的普适性，可推广至其他领域。