AutoGraph: A Knowledge-Graph Framework for Modeling Interface Interaction and Automating Procedure Execution in Digital Nuclear Control Rooms

TL;DR

AutoGraph introduces a knowledge-graph framework that formalizes and automates procedure execution in digital nuclear control rooms, reducing human error and supporting real-time decision-making.

Contribution

The paper presents AutoGraph, a novel knowledge-graph-based framework that integrates operator interactions, encodes interface properties, and automates procedure execution in digitalized NPP environments.

Findings

Significant reduction in task completion time in control scenarios

Enhanced support for real-time human reliability assessment

Demonstrated extensibility to dynamic decision support systems

Abstract

Digitalization in nuclear power plant (NPP) control rooms is reshaping how operators interact with procedures and interface elements. However, existing computer-based procedures (CBPs) often lack semantic integration with human-system interfaces (HSIs), limiting their capacity to support intelligent automation and increasing the risk of human error, particularly under dynamic or complex operating conditions. In this study, we present AutoGraph, a knowledge-graph-based framework designed to formalize and automate procedure execution in digitalized NPP environments.AutoGraph integrates (1) a proposed HTRPM tracking module to capture operator interactions and interface element locations; (2) an Interface Element Knowledge Graph (IE-KG) encoding spatial, semantic, and structural properties of HSIs; (3) automatic mapping from textual procedures to executable interface paths; and (4) an execution engine that maps textual procedures to executable interface paths. This enables the identification of cognitively demanding multi-action steps and supports fully automated execution with minimal operator input. We validate the framework through representative control room scenarios, demonstrating significant reductions in task completion time and the potential to support real-time human reliability assessment. Further integration into dynamic HRA frameworks (e.g., COGMIF) and real-time decision support systems (e.g., DRIF) illustrates AutoGraph extensibility in enhancing procedural safety and cognitive performance in complex socio-technical systems.