Name
Abstract | ||
|---|---|---|
Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernisation of large infrastructure problems. This is especially true in the modernisation of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behaviour due to complex human/machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviours. However, such models are difficult to produce, especially to show unexpected emergent behaviour coming from many human operators interacting simultaneously within a complex system. Instead, we introduce an alternate approach. Instead of engineering complex human models, we directly model the emergent behaviour with relatively simple goal-directed agents. In this model, each autonomous agent in a system pursues individual goals, and the high-level behaviour of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method is capable of reflecting the integration of new technologies in a historical case, and apply the same methodology for a possible future technology. Finally, we show how these high-level simulated behaviours compare to actual deployed air traffic control mechanisms in use today. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1080/09540091.2015.1031083 | Connection Science |
Keywords | Field | DocType |
distributed artificial intelligence,emergent behaviour,multiagent learning | Autonomous agent,Air traffic control,Computer science,Air traffic management,Automation,Emerging technologies,Multiagent learning,Operator (computer programming),Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 3 | 0954-0091 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Logan Michael Yliniemi | 1 | 25 | 5.98 |
| Adrian K. Agogino | 2 | 12 | 2.99 |
| kagan tumer | 3 | 1632 | 168.61 |