1. Positioning and Spatial Evaluation
Influence Maps
Positioning and Spatial Evaluation
Damián Isla, Director of Tech. Moonshot Games

2. Where do I stand right now?
Spatial Evaluation
Spreading influence is just one form of spatial function.
Spatial decisions can be made on second-to-second timescales
Use spatial functions to answer fundamental question:
Where do I stand right now?

3. Spatial Evaluation Algorithm:
Score each [accessible] position with F(x)
Pick the best one
Go there

4. Spatial Evaluation F(x) 42 Breakfast Threat Line of sight Distance
Range
F(x) 42
The Apples-to-Oranges problem.

5. Apples and Oranges Crops up in a host of AI problems. Best strategies
Perception
Attention / target selection
Behavior-selection
etc.
Best strategies
visualization
iteration
How do you take 2 completely different factors and weight them relative to each other?

6. Spatial Function Inputs A(x) = range from x to target
B(x) = path distance from AI to x
C(x) = line of sight (1.0 = 100% clear)
...

7. Spatial Function Layer Simplest form
F(x) = k1A(x) + k2B(x) + k3C(x) + ...
With user functions:
F(x) = f1(A) + f2(B) + f3(C) + ...
Our own idiosyncratic form:
F(x) = (((f1(A) + f2(B)) + f3(C)) * f4(D)) + f5(E) ...
Layer

8. Implementation Engine-specified (code) Designer-specified (data)
Layers
Input source
range
los
path-distance
etc.
Combination method
Additive
Multiplicative
User-Function
output = F(input)
Global modifications
Blur factor
Normalization
Engine-specified
(code)
Designer-specified
(data)

9. Demo

10. Spreading Influence “Gather phase” Distance maps
create list of evaluatable locations
assumes we’re interested in moving to that spot
Uses A*
Distance maps
Each pixel contains distance to an occupied cell
Pre-computed

11. Line of Sight By far the greatest perf cost.
Solution: Batch batch batch.
pre-search for los-blocking objects
build optimized structure at runtime
evaluate on the GPU
In this case: rasterization
O(n) (n is # of pixels)

12. Interference Destructive interference is a [the] big problem
Pre-combine inputs
Range

13. Grid Resolution Grid resolution < body radius Solutions:
Good: high res decision-making
Bad: rounding corners
Bad: rubbing against walls
Solutions:
Distance map can influence steering
A* in “configuration space”
Distance-map layers in position selection
Penalty function in A*
In some sense, the penalty function helps us create a body-radius-agnostic “configuration space”

14. All Behavior is Spatial
Spatial functions can be used for more than just position evaluation
A* penalty
path speed
aim on/off
target bias
weapon choice
...
Remember: Input sources are expensive, but recombining them is cheap
(share inputs across layers, functions and AIs)

15. Spatial Behavior Target bias A* Penalty Weapon choice
Position selection

16. Spatial Behavior
Aiming
Shooting
Positioning
Speed
Targeting

17. Conclusions
Apples-to-oranges is defeated through great visualization and iteration tools
Respect the code/data boundary
Distance maps are sweet
Batch yer rays
Pre-combine yer inputs
Spatial functions for many aspects of behavior

18. Thanks!