Concept-Guided LLM Agents for Human-AI Safety Codesign

• URL: http://arxiv.org/abs/2404.15317v1
• Date: Wed, 3 Apr 2024 11:37:01 GMT
• Title: Concept-Guided LLM Agents for Human-AI Safety Codesign
• Authors: Florian Geissler, Karsten Roscher, Mario Trapp,
• Abstract summary: Generative AI is increasingly important in software engineering, including safety engineering, where its use ensures that software does not cause harm to people. It is crucial to develop more advanced and sophisticated approaches that can effectively address the complexities and safety concerns of software systems. We present an efficient, hybrid strategy to leverage Large Language Models for safety analysis and Human-AI codesign.
• Score: 6.603483691167379
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI is increasingly important in software engineering, including safety engineering, where its use ensures that software does not cause harm to people. This also leads to high quality requirements for generative AI. Therefore, the simplistic use of Large Language Models (LLMs) alone will not meet these quality demands. It is crucial to develop more advanced and sophisticated approaches that can effectively address the complexities and safety concerns of software systems. Ultimately, humans must understand and take responsibility for the suggestions provided by generative AI to ensure system safety. To this end, we present an efficient, hybrid strategy to leverage LLMs for safety analysis and Human-AI codesign. In particular, we develop a customized LLM agent that uses elements of prompt engineering, heuristic reasoning, and retrieval-augmented generation to solve tasks associated with predefined safety concepts, in interaction with a system model graph. The reasoning is guided by a cascade of micro-decisions that help preserve structured information. We further suggest a graph verbalization which acts as an intermediate representation of the system model to facilitate LLM-graph interactions. Selected pairs of prompts and responses relevant for safety analytics illustrate our method for the use case of a simplified automated driving system.

Related papers

• Safeguarding AI Agents: Developing and Analyzing Safety Architectures [0.0]
  This paper addresses the need for safety measures in AI systems that collaborate with human teams. We propose and evaluate three frameworks to enhance safety protocols in AI agent systems. We conclude that these frameworks can significantly strengthen the safety and security of AI agent systems.
  arXiv  Detail & Related papers  (2024-09-03T10:14:51Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications [1.6495054381576084]
  This paper explores the role and challenges of Artificial Intelligence (AI) algorithms in autonomous driving systems. A primary concern relates to the ability (and necessity) of AI models to generalize beyond their initial training data. This paper investigates the risk associated with overconfident AI models in safety-critical applications like autonomous driving.
  arXiv  Detail & Related papers  (2024-02-13T04:15:26Z)
• Building Guardrails for Large Language Models [19.96292920696796]
  Guardrails, which filter the inputs or outputs of LLMs, have emerged as a core safeguarding technology. This position paper takes a deep look at current open-source solutions (Llama Guard, Nvidia NeMo, Guardrails AI) and discusses the challenges and the road towards building more complete solutions.
  arXiv  Detail & Related papers  (2024-02-02T16:35:00Z)
• Automated Process Planning Based on a Semantic Capability Model and SMT [50.76251195257306]
  In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. We present an approach that combines these two topics: starting from a semantic capability model, an AI planning problem is automatically generated.
  arXiv  Detail & Related papers  (2023-12-14T10:37:34Z)
• Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety [11.933469815219544]
  We present the CREST framework that shows how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework.
  arXiv  Detail & Related papers  (2023-12-05T06:13:55Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.