Heuristics modules are primarily used by 
Pathfinding nodes, such as 
Edges Pathfinding and 
Plot Edges Pathfinding
Heuristics are basically some under-the-hood maths used by 
⊚ Search Algorithms to gauge whether one path is better than another. Different algorithms use heuristics differently, but their values is computed consistently.
Heuristic nodes support dynamic weighting – e.g, using a point or edge attribute to modulate their weight based on context. While it’s nice on paper, there is an overhead associated to it, and can deteriorate scoring quality in certain scenarios. They require some trial and error to get nice results.
Modules
🝰 Heuristic Attribute
Attribute-driven heuristics
The Attribute heuristics uses custom point or edge value as raw score.
🝰 Feedback
Favor uncharted points & edges.
The Feedback heuristic add/remove score value to points & edges that are “in use” by other previously computed paths.
🝰 Inertia
Favor active direction preservation.
The Inertia heuristic uses the ongoing traversal data to try and maintain a consistent direction, as if the algorithm had “inertia”.
🝰 Steepness
Favor flat trajectories.
The Steepness heuristic uses the edge angle against an up vector to compute a dot product that is used to determine whether the edge should be considered flat or not.
🝰 Azimuth
Favor edges directed toward the goal.
The Azimuth heuristic attempt to force the path to always aim toward the goal.
🝰 Least Nodes
Favor traversing the least amount of nodes.
The Least Nodes heuristic favor node count traversal over anything else.