Evolution and Prioritization of Survival Strategies for a Simulated Robot in Xpilot

Abstract

Simulated evolution by the use of genetic algorithms (GA) is presented as the solution to a two-faceted problem: the challenge for an autonomous agent to learn the reactive component of multiple survival strategies, while simultaneously determining the relative importance of these strategies as the agent encounters changing multivariate obstacles. The agent's ultimate purpose is to prolong its survival; it must learn to navigate its space avoiding obstacles while engaged in combat with an opposing agent. The GA learned rule-based controller significantly improved the agent's survivability in the hostile Xpilot environment.