# Towards Active Learning Based Smart Assistant for Manufacturing

arXiv: https://arxiv.org/abs/2103.16177 · HTML: https://mlsanity.com/paper/2103.16177/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 2 of 2)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Review of Explainable Artificial Intelligence in Manufacturing](https://mlsanity.com/paper/2107.02295/) (2021)
- [Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing](https://mlsanity.com/paper/2209.05486/) (2022)
- [Knowledge Modelling and Active Learning in Manufacturing](https://mlsanity.com/paper/2107.02298/) (2021)

## Abstract (arXiv)

A general approach for building a smart assistant that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps is presented. We develop a methodology to build such a system. The system is demonstrated on a demand forecasting use case in manufacturing. The methodology can be extended to several use cases in manufacturing. The system provides means for knowledge acquisition, gathering data from users. We envision active learning can be used to get data labels where labeled data is scarce.