Multi-objective evolution for 3D RTS Micro

TL;DR

This paper presents a multi-objective evolutionary approach using influence maps and potential fields to control 3D team behaviors in RTS games, achieving complex tactics through evolved parameters.

Contribution

It introduces a novel method combining influence maps and potential fields with multi-objective evolution for 3D RTS team control, improving on prior parameter-based approaches.

Findings

Evolved potential field parameters produce complex team tactics.

The approach effectively balances damage dealt and received.

Preliminary results show promising 3D team behavior control.

Abstract

We attack the problem of controlling teams of autonomous units during skirmishes in real-time strategy games. Earlier work had shown promise in evolving control algorithm parameters that lead to high performance team behaviors similar to those favored by good human players in real-time strategy games like Starcraft. This algorithm specifically encoded parameterized kiting and fleeing behaviors and the genetic algorithm evolved these parameter values. In this paper we investigate using influence maps and potential fields alone to compactly represent and control real-time team behavior for entities that can maneuver in three dimensions. A two-objective fitness function that maximizes damage done and minimizes damage taken guides our multi-objective evolutionary algorithm. Preliminary results indicate that evolving friend and enemy unit potential field parameters for distance, weapon characteristics, and entity health suffice to produce complex, high performing, three-dimensional, team tactics.