测量遗忘策略推理强度的技术
Techniques for Measuring the Inferential Strength of Forgetting Policies
AI总结：
本文提出基于模型计数和概率论的损失函数来量化不同遗忘策略对推理强度的影响，并给出利用 ProbLog 计算损失度量的实用工具与方法。
摘要：
知识表示中的遗忘技术已被证明是一种强大且有用的知识工程工具，具有广泛的应用。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理论的推理强度，目前的研究非常少。本文的目标是基于模型计数和概率论的直觉，定义用于测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，用于使用 ProbLog 计算损失度量。本文包括一套用于研究和确定不同遗忘策略强度的工作方法，以及展示如何使用 ProbLog 应用理论结果的具体示例。尽管本文关注的是遗忘问题，但其结果具有更广泛的普适性，应能推广到其他领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
