Shortcomings of Part 1’s system:
#1: Over vast areas, this system begins to bog down. Each EQS query is extremely expensive given the ever-increasing number of data nodes that are being used as context points. If your EQS query favors close points, the system is able to generate many more points without struggling. If it favors far away points, the system will begin lagging due to the massive quantity of nodes that the EQS queries are checking.
The difference in the results:
The above picture is what happens whenever you prefer short range locations.
The above picture is what happens whenever you prefer long range locations. You get a much wider breadth of targets hit, but you also don’t search much of the town.
If you are willing to trade off some accuracy, I found that 1000 range with 250 distance between the points on the EQS’ pathing grid generator allows for many more data nodes to exist before the system starts bogging down.
I have not found an optimal solution to this problem at this stage.
#2: The trace on the EQS runs for infinity. This can cause issues going between areas that are separated by large swaths of open land.
#3: It does not factor in distance from any AI agents. If you are willing to factor in the distance from AI agents, you may be able to make it slightly more coordinated.