Core Concepts
Developing a multi-stage learning framework to predict criticality in intelligent systems with high precision and recall rates.
Abstract
The content discusses the challenges of predicting safety-critical events in intelligent systems due to data imbalance and rarity. It introduces a multi-stage learning framework to address these challenges, focusing on unsupervised learning, supervised learning, and reinforcement learning. The study evaluates the proposed method in lunar lander and bipedal walker scenarios, demonstrating improved precision and recall rates compared to traditional approaches.
Introduction to safety-critical rare events in intelligent systems.
Challenges posed by data imbalance and rarity in predicting criticality.
Proposal of a multi-stage learning framework to enhance prediction accuracy.
Evaluation of the method in lunar lander and bipedal walker scenarios.
Comparison with traditional methods and demonstration of improved results.
Stats
"The imbalance ratio studied in this work exceeds 104."
"Our method exhibits applicability to real-world scenarios featuring extremely imbalanced test datasets."
Quotes
"Intelligent systems are increasingly integral to our daily lives."
"Our method surpasses traditional approaches, providing a more accurate assessment of criticality."