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Table of Contents
ML techniques for learning the Objective Function
Performance (RSME):
- Linear Regression: 0.322
- Neural Nets: 0.37
- K-Nearest Neighbor: 0.356
Attributes
Objective function Attributes:
- numAllies
- numEnemies
- health
- healthRoot
- healthSquare
- attackPotential
- distance
- TV
- enemyHealth
- enemyHealthRoot
- enemyHealthSquare
- enemyAttackPotential
- enemyDistance
- enemyTV
TV(u) = (0.001*health)*(0.5 + 0.1*armor(u))*(min(1.0), damage(u))*(0.01*MaxHealth)
Linear Regression
Attribute weights
- + 0.079 * numAllies
- - 0.060 * numEnemies
- + 0.791 * health
- - 0.232 * healthRoot
- - 0.372 * healthSquare
- + 0.030 * attackPotential
- - 0.083 * distance
- + 0.383 * TV
- - 0.731 * enemyHealth
- + 0.139 * enemyHealthRoot
- + 0.386 * enemyHealthSquare
- - 0.031 * enemyAttackPotential
- + 0.082 * enemyDistance
- - 0.359 * enemyTV
- + 0.012
Unit Values
K-Nearest Neighbors
Too slow!
Configuration
- k: 15
- measure: CamberraNumericalDistance
- weighted vote