1 / 7

Thuggabot: Evolving AI Combat Agent for Half-Life Using Genetic Algorithms

Thuggabot is an advanced AI combat agent developed at the University of Georgia, leveraging genetic algorithms to simulate human players in the Half-Life game world. Designed with the objective to maximize kills while minimizing deaths, Thuggabot adapts its strategies based on environmental feedback and player interactions. Utilizing the principles of the HPB Bot Framework by Botman, this AI agent evolves through mechanisms like tournament selection, crossover, and mutation. Testing results show its effectiveness, demonstrating significant performance improvements and long-term dominance over competitors.

hestia
Download Presentation

Thuggabot: Evolving AI Combat Agent for Half-Life Using Genetic Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Thuggabot THe University of Georgia Genetic Algorithm BOT

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

  3. 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.

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

  5. 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.

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

  7. 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

More Related