Summary of Accurately Predicting Probabilities Of Safety-critical Rare Events For Intelligent Systems, by Ruoxuan Bai et al.
Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
by Ruoxuan Bai, Jingxuan Yang, Weiduo Gong, Yi Zhang, Qiujing Lu, Shuo Feng
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 multi-stage learning framework is proposed to predict the probability of safety-critical events occurring within autonomous systems. This framework aims to address the curse of rarity, which arises from rare events in high-dimensional variables. The approach consists of progressive densification stages, designed to mitigate this challenge. Evaluations are conducted on lunar lander and bipedal walker scenarios, demonstrating improved accuracy and reliability over traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Intelligent systems are becoming a part of our daily lives, but they also pose safety risks. To make them safer, we need to predict when something bad might happen. This is called “criticality” prediction. It’s hard because most events don’t happen, so we have to figure out which ones will. Current methods aren’t good enough because they’re either too cautious or miss important events. We want to create a better way to predict criticality and make autonomous systems safer. |
Keywords
* Artificial intelligence * Probability
