Adding a motor control component to the operator function model expert system to investigate air traffic management concepts using simulation
DOI: 10.1109/icsmc.2004.1398415
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the need for efficient simulation tools to evaluate NASA’s Distributed Air/Ground Traffic Management (DAG-TM) concepts, where flight crews assume primary responsibility for aircraft separation. Developing these operational concepts using human pilots is expensive and potentially biased by existing procedures. To mitigate this, the authors enhance the Operator Function Model Expert System (OFMspert) by adding a motor control component. This enhancement allows automated agents to simulate pilot behavior within the Aircraft Simulation for Traffic Operation Research (ASTOR) environment, enabling the testing of decision support tools like the Autonomous Operations Planner (AOP) without human intervention. The enhanced architecture integrates four new modules into OFMspert: simulation control, a task manager, strategies and operational heuristics, and shared memory. The simulation control module emulates user inputs, such as mouse clicks and keystrokes, to interact with ASTOR’s avionics interfaces. The task manager handles dynamic prioritization of concurrent tasks based on a modified version of Cockpit Task Management (CTM). This module utilizes a blackboard to track initialization events and termination conditions, allowing for flexible prioritization schemes. The strategies and operational heuristics module provides the logic for prioritizing tasks and selecting actions for sequential, AND, OR, and XOR sub-tasks, often relying on context-sensitive variables from the simulation state. Communication between the Java-based OFMspert and the C++-based ASTOR is facilitated by a shared memory module using the Java Native Interface to simulate an ARINC 429 data bus. The study also introduces a specialized data visualization and analysis tool to evaluate the performance of the automated agents. This tool parses ASTOR output data to generate time-aligned plots displaying alert stimuli, pilot responses via navigation and control displays, and resulting changes in aircraft state (altitude, heading, airspeed, and vertical speed). The visualization allows researchers to compare actual aircraft states against AOP-predicted optimal values and analyze the timing and sequence of pilot actions relative to conflict alerts. The tool supports detailed inspection of specific time domains and provides tooltips and action queues for granular analysis of agent behavior. The significance of this work lies in providing a robust framework for developing normative operator function models and analyzing interface designs for autonomous air traffic management. By coupling the control component with the analysis tool, researchers can run scenarios without human subjects while still assessing how well simulated pilots perform tasks and conform to predicted optimal states. The authors conclude that while the simulation control and shared memory components are domain-specific, careful design is required to ensure the task manager and heuristics modules remain adaptable. This approach facilitates the identification of potential design issues in emerging air traffic management concepts through scalable, automated simulation testing.
Key finding
The integration of a motor control component into the OFMspert architecture successfully enables automated agents to prioritize tasks and execute simulated pilot actions for investigating air traffic management concepts.
Methodology
simulation_modeling
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: computational model