AI Artificial Intelligence in Airport Simulation
AI in the context of airport simulation is characterized by the following points, among others:
- Autonomous decision making:
AI agents: The software includes autonomous AI agents that are capable of making decisions and executing actions independently within the context of the simulation.
Adaptive behaviors: These agents adapt to dynamic environments and events in the simulation without explicit programming for each situation.
- Learning Ability:
Data integration: the software is able to integrate data from different sources (e.g. flight plans, sensor data, passenger behavior) and use this data to train the AI agents.
- Realistic Modeling:
Complex system interactions: The software can realistically model complex interactions between different elements of the airport system (e.g. aircraft, passengers, ground staff).
Emergent behavior: The simulation should enable emergent behavior at the system level that arises from the interactions of individual agents.
- Explainability and Transparency:
Traceable decisions: The software can make the agents’ decision-making transparent and comprehensible.Explanation of the reasons for decisions: Users should understand the reasons for the AI agents’ actions and be able to question and influence them if necessary.
With this glossary we would like to give short, basic explanations and definitions for important and frequently used terms in the fields of Analysis, Runway and Terminal Capacity Assessment and Airport Simulation/Allocation. These are based on our experience of practical application in the industry. We would be happy to provide you with more detailed definitions and further explanations. Please feel free to contact us.