English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

A key element to meet the continuing growth in air traffic is the increased use of automation. Decision support systems, computer-based information acquisition, trajectory planning systems, high-level graphic display systems, and all advisory systems are considered to be automation components related to next generation (NextGen) air space. Given a set of goals represented as reward functions, the actions of the players may be predicted. However, several challenges need to be overcome. First, determining how a player can attempt to maximize their reward function can be a difficult inverse problem. Second, players may not be able to perfectly maximize their reward functions. ADS-B technology can provide pilots the information, position, velocity, etc. of other aircraft. However, a pilot has limited ability to use all this information for his/her decision making. For this scenario, the authors model these pilot limitations by assuming that pilots can observe a limited section of the grid in front of them

Predicting Pilot Behavior in Medium-Scale Scenarios Using Game Theory and Reinforcement Learning