# Techniques for Measuring the Inferential Strength of Forgetting Policies

arXiv: https://arxiv.org/abs/2404.02454 · HTML: https://mlsanity.com/paper/2404.02454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 67 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [The Model Counting Competition 2020](https://mlsanity.com/paper/2012.01323/) (2020)
- [Stable Model Counting and Its Application in Probabilistic Logic Programming](https://mlsanity.com/paper/1411.5410/) (2014)
- [Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic](https://mlsanity.com/paper/1305.2254/) (2013)
- [Forgetting in Answer Set Programming -- A Survey](https://mlsanity.com/paper/2107.07016/) (2021)
- [Dual Forgetting Operators in the Context of Weakest Sufficient and Strongest Necessary Conditions](https://mlsanity.com/paper/2305.07233/) (2023)

## Abstract (arXiv)

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.