Develop AI Agents for System Engineering in Factorio

• URL: http://arxiv.org/abs/2502.01492v1
• Date: Mon, 03 Feb 2025 16:26:17 GMT
• Title: Develop AI Agents for System Engineering in Factorio
• Authors: Neel Kant,
• Abstract summary: This position paper advocates for training and evaluating AI agents' system engineering abilities through automation-oriented sandbox games.<n>By directing research efforts in this direction, we can equip AI agents with the specialized reasoning and long-horizon planning necessary to design, maintain, and optimize tomorrow's most demanding engineering projects.
• Score: 1.5824959429406713
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Continuing advances in frontier model research are paving the way for widespread deployment of AI agents. Meanwhile, global interest in building large, complex systems in software, manufacturing, energy and logistics has never been greater. Although AI driven system engineering holds tremendous promise, the static benchmarks dominating agent evaluations today fail to capture the crucial skills required for implementing dynamic systems, such as managing uncertain trade-offs and ensuring proactive adaptability. This position paper advocates for training and evaluating AI agents' system engineering abilities through automation-oriented sandbox games-particularly Factorio. By directing research efforts in this direction, we can equip AI agents with the specialized reasoning and long-horizon planning necessary to design, maintain, and optimize tomorrow's most demanding engineering projects.

Related papers

• An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework [49.633199780510864]
  This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering. operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints. A fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control.
  arXiv  Detail & Related papers  (2025-04-20T16:57:45Z)
• Towards practicable Machine Learning development using AI Engineering Blueprints [0.8654896256058138]
  Small and medium-sized enterprises (SMEs) face challenges when implementing AI in their products or processes. This paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models.
  arXiv  Detail & Related papers  (2025-04-08T19:28:05Z)
• Challenges and Paths Towards AI for Software Engineering [55.95365538122656]
  We discuss progress in AI for software engineering in threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering. Second, we outline several key bottlenecks that limit current approaches.
  arXiv  Detail & Related papers  (2025-03-28T17:17:57Z)
• Speeding up design and making to reduce time-to-project and time-to-market: an AI-Enhanced approach in engineering education [0.0]
  This paper explores the integration of AI tools, such as ChatGPT and GitHub Copilot, in the Software Architecture for Embedded Systems course. Results demon-started enhanced problem-solving, faster development, and more sophisticated outcomes, with AI augmenting but not replacing human decision-making.
  arXiv  Detail & Related papers  (2025-03-20T16:32:13Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Generative AI Agents in Autonomous Machines: A Safety Perspective [9.02400798202199]
  generative AI agents provide unparalleled capabilities, but they also have unique safety concerns. This work investigates the evolving safety requirements when generative models are integrated as agents into physical autonomous machines. We recommend the development and implementation of comprehensive safety scorecards for the use of generative AI technologies in autonomous machines.
  arXiv  Detail & Related papers  (2024-10-20T20:07:08Z)
• Generative AI Application for Building Industry [10.154329382433213]
  This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
  arXiv  Detail & Related papers  (2024-10-01T21:59:08Z)
• Building AI Agents for Autonomous Clouds: Challenges and Design Principles [17.03870042416836]
  AI for IT Operations (AIOps) aims to automate complex operational tasks, like fault localization and root cause analysis, thereby reducing human intervention and customer impact. This vision paper lays the groundwork for such a framework by first framing the requirements and then discussing design decisions. We propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults.
  arXiv  Detail & Related papers  (2024-07-16T20:40:43Z)
• ProAgent: From Robotic Process Automation to Agentic Process Automation [87.0555252338361]
  Large Language Models (LLMs) have emerged human-like intelligence. This paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation. We then instantiate ProAgent, an agent designed to craft from human instructions and make intricate decisions by coordinating specialized agents.
  arXiv  Detail & Related papers  (2023-11-02T14:32:16Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation [47.37985501848305]
  We introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection. This is complemented by insights and lessons learned during this early evaluation activity.
  arXiv  Detail & Related papers  (2022-07-10T18:51:12Z)
• 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)
• Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022 [0.0]
  The RAISA workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level.
  arXiv  Detail & Related papers  (2022-02-10T01:15:50Z)

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.