Modern production systems demand timely diagnostic and prognostic insights, yet the complexity of existing process intelligence (PI) tools creates a high technical barrier even for domain experts. This paper presents FIDES, a conversational neuro-symbolic tool designed to enable access to these analysis engines via natural language. Unlike approaches uniquely based on Large Language Models (LLMs) that often hallucinate operational results, FIDES implements a sound routing architecture: it uses LLMs strictly for understanding and translating the user’s intent into machine-readable encodings, while delegating the orchestration to a domain-independent automated planner. The planner autonomously decomposes complex queries and routes them to the appropriate PI engines, ensuring rigorous results. We demonstrate the tool’s maturity and usability through a lab-scale manufacturing case study, highlighting how its web-based interface enables non-technical users to perform faithful multi-perspective analysis of production processes.

FIDES: A Neuro-Symbolic Conversational Tool for Faithful Production Process Intelligence

Lestingi, Livia;Matta, Andrea
2026-01-01

Abstract

Modern production systems demand timely diagnostic and prognostic insights, yet the complexity of existing process intelligence (PI) tools creates a high technical barrier even for domain experts. This paper presents FIDES, a conversational neuro-symbolic tool designed to enable access to these analysis engines via natural language. Unlike approaches uniquely based on Large Language Models (LLMs) that often hallucinate operational results, FIDES implements a sound routing architecture: it uses LLMs strictly for understanding and translating the user’s intent into machine-readable encodings, while delegating the orchestration to a domain-independent automated planner. The planner autonomously decomposes complex queries and routes them to the appropriate PI engines, ensuring rigorous results. We demonstrate the tool’s maturity and usability through a lab-scale manufacturing case study, highlighting how its web-based interface enables non-technical users to perform faithful multi-perspective analysis of production processes.
2026
Lecture Notes in Business Information Processing
9783032279965
9783032279972
Formal Verification
Large Language Model
Process Intelligence
Process Mining
Production system
Simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1318854
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