THe University of Georgia Genetic Algorithm BOT Thuggabot THe University of Georgia Genetic Algorithm BOT

Thuggabot Half-Life Game World Thuggabot Concepts Combat Strategy Genetic Algorithm Learning Test Results Demo

Half-Life Game World First-Person Shooter (3D Environment) Objective: Maximize kills, Minimize Deaths Upon dying, players re-spawn with minimal equipment. Throughout the game, players gather items to help them accomplish goals.

Thuggabot Concepts AI Combat Agent Acts to simulate human player Goal Oriented Utilizes Genetic Algorithm Based on the HPB Bot Framework by Botman

Combat Strategy Each bot has preferences regarding possible actions Bots choose goals based on preferences Bots which make good choices are more effective in combat Bots adapt to their environment through evolution.

Genetic Algorithms Representation Array of weights that correspond to actions and weapon preferences Proportional Fitness Tournament Selection Uniform Crossover Random Index Mutation

Test Results Roughly monotone increasing performance Some goals clearly become favored over others Some preferences fluctuate due to dynamic nature of the environment. Tested against TheFatal’s “Jumbot,” Thuggabot achieved long-term domination