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Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehicles

Hugo Math, Julian Lorentz, Stefan Oelsner, Rainer Lienhart

TL;DR

CARE P addresses the challenge of automating error-pattern (EP) rule discovery from high-dimensional DTC sequences in vehicles. It presents CAREP, a tri-agent system combining causal discovery, contextual information retrieval, and orchestrated reasoning to generate candidate Boolean EP rules with interpretable traces, using one-shot causal discovery and adaptive thresholding. The approach achieves superior semantic and structural accuracy compared with LLM-only baselines on a large automotive dataset (29,100 DTCs, 474 EPs) and provides transparent causal explanations, demonstrating scalable and cost-efficient fault diagnostics. This work advances automated, explainable fault diagnostics in automotive engineering and offers a framework adaptable to other high-dimensional, safety-critical domains.

Abstract

Modern vehicles generate thousands of different discrete events known as Diagnostic Trouble Codes (DTCs). Automotive manufacturers use Boolean combinations of these codes, called error patterns (EPs), to characterize system faults and ensure vehicle safety. Yet, EP rules are still manually handcrafted by domain experts, a process that is expensive and prone to errors as vehicle complexity grows. This paper introduces CAREP (Causal Automated Reasoning for Error Patterns), a multi-agent system that automatizes the generation of EP rules from high-dimensional event sequences of DTCs. CAREP combines a causal discovery agent that identifies potential DTC-EP relations, a contextual information agent that integrates metadata and descriptions, and an orchestrator agent that synthesizes candidate boolean rules together with interpretable reasoning traces. Evaluation on a large-scale automotive dataset with over 29,100 unique DTCs and 474 error patterns demonstrates that CAREP can automatically and accurately discover the unknown EP rules, outperforming LLM-only baselines while providing transparent causal explanations. By uniting practical causal discovery and agent-based reasoning, CAREP represents a step toward fully automated fault diagnostics, enabling scalable, interpretable, and cost-efficient vehicle maintenance.

Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehicles

TL;DR

CARE P addresses the challenge of automating error-pattern (EP) rule discovery from high-dimensional DTC sequences in vehicles. It presents CAREP, a tri-agent system combining causal discovery, contextual information retrieval, and orchestrated reasoning to generate candidate Boolean EP rules with interpretable traces, using one-shot causal discovery and adaptive thresholding. The approach achieves superior semantic and structural accuracy compared with LLM-only baselines on a large automotive dataset (29,100 DTCs, 474 EPs) and provides transparent causal explanations, demonstrating scalable and cost-efficient fault diagnostics. This work advances automated, explainable fault diagnostics in automotive engineering and offers a framework adaptable to other high-dimensional, safety-critical domains.

Abstract

Modern vehicles generate thousands of different discrete events known as Diagnostic Trouble Codes (DTCs). Automotive manufacturers use Boolean combinations of these codes, called error patterns (EPs), to characterize system faults and ensure vehicle safety. Yet, EP rules are still manually handcrafted by domain experts, a process that is expensive and prone to errors as vehicle complexity grows. This paper introduces CAREP (Causal Automated Reasoning for Error Patterns), a multi-agent system that automatizes the generation of EP rules from high-dimensional event sequences of DTCs. CAREP combines a causal discovery agent that identifies potential DTC-EP relations, a contextual information agent that integrates metadata and descriptions, and an orchestrator agent that synthesizes candidate boolean rules together with interpretable reasoning traces. Evaluation on a large-scale automotive dataset with over 29,100 unique DTCs and 474 error patterns demonstrates that CAREP can automatically and accurately discover the unknown EP rules, outperforming LLM-only baselines while providing transparent causal explanations. By uniting practical causal discovery and agent-based reasoning, CAREP represents a step toward fully automated fault diagnostics, enabling scalable, interpretable, and cost-efficient vehicle maintenance.
Paper Structure (23 sections, 14 equations, 4 figures, 2 tables)

This paper contains 23 sections, 14 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Event Sequence Modeling
  4. Error Pattern Rule Automation
  5. Multi-label Causal Discovery
  6. Notations
  7. Methodology
  8. Supervised Unknown Class Learning
  9. One-Shot Causal Discovery
  10. Causal Indicators
  11. Adaptive Threshold for Rule Aggregation
  12. Co-occurrence
  13. Contextual Informations
  14. Experiments
  15. Settings
  16. ...and 8 more sections

Figures (4)

  • Figure 1: Illustration of error pattern automation in modern vehicles. (a) A vehicle with an unknown defect generates a sequence of Diagnostic Trouble Codes (DTCs) over time. (b) A domain expert analyzes the DTCs and the metadata associated with the vehicle (model, descriptions, etc.). (c) Then, provide a Boolean rule to identify this error pattern.
  • Figure 2: CAREP: A causal reasoning agentic system for error pattern automation. (a) Represents the causal discovery phase where we extract candidates DTC causes for the unknown error pattern $Y_1$, with their causal indicators. It then feeds the causal reasoner agent. (b) The descriptions and metadata are extracted through a Titan V2 Embeddings and fed into the contextual information agent. (c) The orchestrator agent manages the two agents and provides a traceable reasoning to explain why the unknown error pattern potentially has these rules. Traceable reasoning is truncated to 3 rules.
  • Figure 3: Evaluation of CAREP against standalone LLMs. (a) Semantic evaluation represents how well the estimated error pattern rules fit the ground truth in terms of Boolean expression. (b) Structural evaluation or multi-label classification reveals if the estimated rules contain the correct DTCs.
  • Figure 4: Example of inputs given to the agentic system for the causal reasoning. The potential causes are output by the causal discovery algorithm. For instance, DTC3 is a cause of unknown EP and has an Average Causal Effect (ACE) of 0.7, i.e., it increases the likelihood of observing the EP on average by 70%. Example of defective vehicles with such error pattern are given in dtcs samples, metadata are included in it.