Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing
- URL: http://arxiv.org/abs/2506.11180v1
- Date: Thu, 12 Jun 2025 13:02:16 GMT
- Title: Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing
- Authors: Luis Miguel Vieira da Silva, Aljosha Köcher, Felix Gehlhoff,
- Abstract summary: We present an alternative approach based on the recently introduced Model Context Protocol (MCP)<n>MCP allows systems to expose functionality through a standardized interface that is directly consumable by LLM-based agents.
- Score: 0.12289361708127876
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Explicit modeling of capabilities and skills -- whether based on ontologies, Asset Administration Shells, or other technologies -- requires considerable manual effort and often results in representations that are not easily accessible to Large Language Models (LLMs). In this work-in-progress paper, we present an alternative approach based on the recently introduced Model Context Protocol (MCP). MCP allows systems to expose functionality through a standardized interface that is directly consumable by LLM-based agents. We conduct a prototypical evaluation on a laboratory-scale manufacturing system, where resource functions are made available via MCP. A general-purpose LLM is then tasked with planning and executing a multi-step process, including constraint handling and the invocation of resource functions via MCP. The results indicate that such an approach can enable flexible industrial automation without relying on explicit semantic models. This work lays the basis for further exploration of external tool integration in LLM-driven production systems.
Related papers
- MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models [76.72220653705679]
We introduce MCPEval, an open-source framework that automates end-to-end task generation and deep evaluation of intelligent agents.<n> MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines.<n> Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance.
arXiv Detail & Related papers (2025-07-17T05:46:27Z) - Control Industrial Automation System with Large Language Model Agents [2.2369578015657954]
This paper introduces a framework for integrating large language models with industrial automation systems.<n>At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism.<n>Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets.
arXiv Detail & Related papers (2024-09-26T16:19:37Z) - Incorporating Large Language Models into Production Systems for Enhanced Task Automation and Flexibility [2.3999111269325266]
This paper introduces a novel approach to integrating large language model (LLM) agents into automated production systems.
We organize production operations within a hierarchical framework based on the automation pyramid.
This allows for a scalable and flexible foundation for orchestrating production processes.
arXiv Detail & Related papers (2024-07-11T14:34:43Z) - Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
arXiv Detail & Related papers (2024-05-26T11:40:58Z) - Process Modeling With Large Language Models [42.0652924091318]
This paper explores the integration of Large Language Models (LLMs) into process modeling.
We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models.
Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
arXiv Detail & Related papers (2024-03-12T11:27:47Z) - Model Composition for Multimodal Large Language Models [71.5729418523411]
We propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model.
Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters.
arXiv Detail & Related papers (2024-02-20T06:38:10Z) - Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs.
We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
arXiv Detail & Related papers (2024-01-14T16:17:07Z) - TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation.
Specifically, task decomposition, tool selection, and parameter prediction are assessed.
Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
arXiv Detail & Related papers (2023-11-30T18:02:44Z) - Recommender AI Agent: Integrating Large Language Models for Interactive
Recommendations [53.76682562935373]
We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools.
InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
arXiv Detail & Related papers (2023-08-31T07:36:44Z) - Towards autonomous system: flexible modular production system enhanced
with large language model agents [0.0]
We present a novel framework that combines large language models (LLMs), digital twins and industrial automation system.
We demonstrate how our implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations.
arXiv Detail & Related papers (2023-04-28T09:42:18Z) - MLTEing Models: Negotiating, Evaluating, and Documenting Model and
System Qualities [1.1352560842946413]
MLTE is a framework and implementation to evaluate machine learning models and systems.
It compiles state-of-the-art evaluation techniques into an organizational process.
MLTE tooling supports this process by providing a domain-specific language that teams can use to express model requirements.
arXiv Detail & Related papers (2023-03-03T15:10:38Z) - Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World
CPS/IoT Applications [0.5156484100374059]
We propose a novel approach to support domain-specific Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT)
We argue that the majority of available data in the nature for Artificial Intelligence (AI) are unlabeled. Hence, unsupervised and/or semi-supervised ML approaches are the practical choices.
Our proposed approach is fully implemented and integrated with an existing state-of-the-art MDSE tool to serve the CPS/IoT domain.
arXiv Detail & Related papers (2021-07-06T15:51:39Z)
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.