Summary of Incorporating Expert Rules Into Neural Networks in the Framework Of Concept-based Learning, by Andrei V. Konstantinov and Lev V. Utkin
Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
by Andrei V. Konstantinov, Lev V. Utkin
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to incorporating expert rules into machine learning models for concept-based learning is presented in this paper. By combining logical rules and neural networks that predict concept probabilities, researchers propose two methods: forming constraints for a joint probability distribution over concept combinations to satisfy expert rules, or representing feasible sets of probability distributions as convex polytopes with vertices or faces. The proposed solutions ensure that output probabilities do not violate expert rules, effectively combining inductive and deductive learning. This framework expands the applicability of the results by treating any logical function connecting concepts or class labels as an expert rule. Numerical examples illustrate the approaches, which are publicly available for implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to combine machine learning models with expert rules to improve concept-based learning. The goal is to create a system that can learn and reason together using both inductive (machine learning) and deductive (expert rules) methods. To achieve this, the authors propose two ways to combine expert rules with neural networks that predict concept probabilities. This combination ensures that the output probabilities are accurate and follow the rules set by experts. The authors also provide numerical examples to illustrate their approaches and make the code publicly available for implementation. |
Keywords
* Artificial intelligence * Machine learning * Probability
