HeuristicsA single heuristics definition
Table of content
The Inertia heuristic samples the search stack of the algorithm to favor the averaged direction of the last few samples, adding “inertia” to the search algorithm.
Inertia can be hard to work with depending on the type of pathfinding you’re working with, and may not produce results as “smooth” as one may expect given the name. However it does a good job when combined with other heuristics to overshoot the path from its starting point.
Properties
| Property | Description |
|---|---|
| Basics | |
| Weight Factor | Weight of this heuristic against other concurrent heuristics. The higher the value, the more important it is during resolution. |
| Invert | Whether the score of this heuristic should be inverted. This effectively samples the score curve backwards. |
| Score Curve | Curve over which the heuristic values will be remapped. |
| Inertia Settings | |
| Samples | THe number of samples that will be averaged from the current search location. |
| Ignore If Not Enough Samples | Whether to use the fallback weight if there’s not enough samples available. |
| Fallback values | |
| Global Inertia Score | Since inertia requires a search history, it cannot be used to compute global score some algorithms rely on (A*). The specified value will be used as fallback instead. |
| Fallback Inertia Score | This fallback score value is the one used if there is no or not enough samples. |
| Local Weight | |
| Use Local Weight Multiplier | If enabled, this heuristic will be using a dynamic, per-point weight factor. |
| Local Weight Multiplier Source | Whether to read the weight from Vtx or Edges points. |
| Local Weight Multiplier Attribute | Attribute to read the local weight from. |
| Roaming | |
| UVW Seed | Bounds-relative roaming seed point |
| UVW Goal | Bounds-relative roaming goal point |
Roaming seed/goal points are used as fallback in contexts that are using heuristics but don’t have explicit seed/goals; such as Cluster Refine‘MST or Score-based refinements.