@inproceedings{shi-etal-2024-aegis,
title = "Aegis:An Advanced {LLM}-Based Multi-Agent for Intelligent Functional Safety Engineering",
author = "Shi, Lu and
Qi, Bin and
Luo, Jiarui and
Zhang, Yang and
Liang, Zhanzhao and
Gao, Zhaowei and
Deng, Wenke and
Sun, Lin",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.115",
pages = "1571--1583",
abstract = "Functional safety is a critical aspect of automotive engineering, encompassing all phases of a vehicle{'}s lifecycle, including design, development, production, operation, and decommissioning. This domain involves highly knowledge-intensive tasks. This paper introduces Aegis: An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering. Aegis is specifically designed to support complex functional safety tasks within the automotive sector. It is tailored to perform Hazard Analysis and Risk Assessment (HARA), document Functional Safety Requirements (FSR), and plan test cases for Automatic Emergency Braking (AEB) systems. The most advanced version, Aegis-Max, leverages Retrieval-Augmented Generation (RAG) and reflective mechanisms to enhance its capability in managing complex, knowledge-intensive tasks. Additionally, targeted prompt refinement by professional functional safety practitioners can significantly optimize Aegis{'}s performance in the functional safety domain. This paper demonstrates the potential of Aegis to improve the efficiency and effectiveness of functional safety processes in automotive engineering.",
}
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<abstract>Functional safety is a critical aspect of automotive engineering, encompassing all phases of a vehicle’s lifecycle, including design, development, production, operation, and decommissioning. This domain involves highly knowledge-intensive tasks. This paper introduces Aegis: An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering. Aegis is specifically designed to support complex functional safety tasks within the automotive sector. It is tailored to perform Hazard Analysis and Risk Assessment (HARA), document Functional Safety Requirements (FSR), and plan test cases for Automatic Emergency Braking (AEB) systems. The most advanced version, Aegis-Max, leverages Retrieval-Augmented Generation (RAG) and reflective mechanisms to enhance its capability in managing complex, knowledge-intensive tasks. Additionally, targeted prompt refinement by professional functional safety practitioners can significantly optimize Aegis’s performance in the functional safety domain. This paper demonstrates the potential of Aegis to improve the efficiency and effectiveness of functional safety processes in automotive engineering.</abstract>
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%0 Conference Proceedings
%T Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering
%A Shi, Lu
%A Qi, Bin
%A Luo, Jiarui
%A Zhang, Yang
%A Liang, Zhanzhao
%A Gao, Zhaowei
%A Deng, Wenke
%A Sun, Lin
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F shi-etal-2024-aegis
%X Functional safety is a critical aspect of automotive engineering, encompassing all phases of a vehicle’s lifecycle, including design, development, production, operation, and decommissioning. This domain involves highly knowledge-intensive tasks. This paper introduces Aegis: An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering. Aegis is specifically designed to support complex functional safety tasks within the automotive sector. It is tailored to perform Hazard Analysis and Risk Assessment (HARA), document Functional Safety Requirements (FSR), and plan test cases for Automatic Emergency Braking (AEB) systems. The most advanced version, Aegis-Max, leverages Retrieval-Augmented Generation (RAG) and reflective mechanisms to enhance its capability in managing complex, knowledge-intensive tasks. Additionally, targeted prompt refinement by professional functional safety practitioners can significantly optimize Aegis’s performance in the functional safety domain. This paper demonstrates the potential of Aegis to improve the efficiency and effectiveness of functional safety processes in automotive engineering.
%U https://aclanthology.org/2024.emnlp-industry.115
%P 1571-1583
Markdown (Informal)
[Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering](https://aclanthology.org/2024.emnlp-industry.115) (Shi et al., EMNLP 2024)
ACL
- Lu Shi, Bin Qi, Jiarui Luo, Yang Zhang, Zhanzhao Liang, Zhaowei Gao, Wenke Deng, and Lin Sun. 2024. Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1571–1583, Miami, Florida, US. Association for Computational Linguistics.