面向工业配置的神经符号人工智能
Neuro-symbolic AI for Industrial Configuration
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中文总结 AI 辅助
本文提出神经符号AI方法用于工业配置，通过混合推理、微调和训练三种策略，构建可靠、可解释且可信的工业级配置器，并探讨了从学术演示扩展到工业规模的挑战。
中文摘要 AI 辅助
大型语言模型（LLMs）在广泛的生成任务上展现了令人瞩目的性能。然而，其概率性本质使得它们孤立地看，从根本上不适合工业产品配置，因为输出必须语法有效、与包含数百个特征和规则的知识库语义一致，并且可由现有制造链生产。我们认为，神经符号（NeSy）人工智能方法为构建设计上可靠、可解释且值得信赖的工业级配置器指明了一条有前景的路径。本文描述了一种包含三种NeSy集成策略的分类法，即混合推理、混合微调和混合训练，并探讨了它们在配置领域中的使用。我们报告了在工业配置副驾中实现NeSy概念的努力，并得出一套在工程环境中部署可信人工智能的实用设计选择。最后，我们讨论了我们认为最紧迫的开放研究挑战，特别是如何将NeSy方法从小型学术演示扩展到工业配置器的规模。
英文摘要
Large Language Models (LLMs) have shown impressive performance on a wide range of generative tasks. Yet their probabilistic nature makes them, in isolation, fundamentally unsuited for industrial product configuration, where outputs must be syntactically valid, semantically consistent with a knowledge base of hundreds of features and rules, and producible by an existing manufacturing chain. We argue that Neuro-symbolic (NeSy) AI methods lay out a promising path towards industrial-grade configurators that are reliable by design, explainable, and trustworthy. This paper describes a taxonomy of three NeSy integration strategies, namely hybrid inference, hybrid fine-tuning, and hybrid training, exploring their usage in the configuration domain. We report our effort to operationalize NeSy concepts in an industrial configuration copilot and derive a set of practical design choices for deploying trustworthy AI in engineering environments. We close with a discussion of open research challenges we consider most pressing, in particular how to scale NeSy methods from small academic demonstrators to the size of industrial configurators.
发表机构
- Siemens AG Österreich（西门子奥地利公司）
- Siemens AG（西门子股份公司）
机构由 AI 辅助整理，请以论文原文为准。
