May 28, 2020
Architecture AI Project
Varied Agent Affinities with Varied Pathing
Demonstration of Varied Agent Affinities with Varied Pathing
Vimeo Link – My Demo of Agents Pathing with Varied Affinities
Explaining Simple but Unclear Heatmap Coloring System
Just to help explain since I don’t have great UI in to explain everything currently, the first thing was that I was testing a really rough “heat map” visual for the architectural values for now. When you turn on the option to show the node gizmos now, instead of uniform black cubes showing all the nodes, the cubes are colored according to the architectural value of the node (for this test case, the window value) Unfortunately this color system isn’t great, especially without a legend, but you can at least see it’s working (I just need to pick a better color gradient). Red is unfortunately both values at/near 0 (min) as well as at/near 100 (max) (but the value range here is only from 0 – 80, so all red in this demo is 0).
Agent Affinity/Architectural Value Interaction for Pathing
The more exciting part is that the basis of the agent affinity and architectural value interactions seem to be working and affecting their pathing in a way that at least makes sense. Again just for demo purposes so far, as can be seen in the video (albeit a bit blurry), I added a quick slider on the inspector for the Spawn Manager to determine the “Window Affinity” for the next agent it spawns (for clarity I also added it as a text UI element that can be seen at the top of the Game View window). Just to rehash, this has a set range between 0 and 100, where 0 means they “hate” windows and avoid them and 100 means they “adore” windows and gravitate towards them.
Test #1: Spawn Position 1
As can be seen in the first quick part of the demo, I spawn 2 agents from the same position 1 but with different affinities. The first has an affinity of 0, and the second has an affinity of 100. Here you can already see the 0 affinity agent steers towards the left side of the large wide obstacle to avoid the blue (relatively high) window area, where as the 100 affinity agent goes around the right side of the same obstacle, preferring the high window valued areas in the blue marked zone.
Test #2: Spawn Position 2
Both the 0 affinity and 100 affinity agents take very similar path, differing by only a couple node deviations here and there. This makes sense as routing around the large high window value area would take a lot of effort, so even the window avoidant agent decides to take the relatively straight forward path over rerouting.
Test #3: Spawn Position 3
This test demonstrated similar results to that of Test #1. The 100 affinity agent moved up and over the large obstacle in the southern part of the area (preferring the high window value area in the middle again), where as the 0 affinity agent moved below the same obstacle and even routed a bit more south just to avoid some of the smaller window afflicted areas as well.
Summary
I did some testing of values in between 0 and 100 and with the low complexity of the area so far, most agents ended up taking one of the same paths as the 0 or 100 affinity agents from what I saw. This will require more testing to see if there already exists some variance, but if not, this suggests that some of the hardcoded behind the scenes values or calculations for additional cost may need tweaked (as is expected). Overall though, the results came out pretty well and seem to make sense. The agents don’t circle around objects in a very odd way, but they also do not go extremely out of there way to avoid areas even when they don’t like them.