Designing and Evolving an Unreal Tournament 2004 Expert Bot
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The document discusses the design and development of an expert bot (e-bot) for Unreal Tournament 2004 using a finite state machine and evolutionary algorithms. The e-bot demonstrated superior performance compared to standard bots and posed a significant challenge for human players. Various approaches to enhance the bot's capabilities were tested, revealing that shorter chromosomes and a complex fitness function yielded better results in evolutionary processes.
Designing and Evolving an Unreal Tournament 2004 Expert Bot
- 1. Designing and Evolving an Unreal Tournament 2004 Expert Bot A.M. Mora, F. Aisa, R. Caballero, P. García-Sánchez, J.J. Merelo, P.A. Castillo, R. Lara-Cabrera International Work-Conference on Artificial Neural Networks
- 2. INDEX • Unreal (game, environment) • Pogamut tool • Unreal Expert Bot (objectives, features) • Evolutionary Algorithms • Unreal Expert Bot Evolution (description, approaches, results) • E-BOT vs GE-BOT (results, demo) • Conclusions
- 3. Environment description UNREAL Unreal is a first person shooter (FPS). Famous due to the excelent AI of the enemies (bots), which makes it an amazing multiplayer game. Unreal Tournament series is widely extended. It offers an editor (UnrealEd) which lets us change almost anything in the game even the behaviour of the bots. It uses the language UnrealScript.
- 4. General description POGAMUT A java middleware for Unreal Tournament series games and Defcon games. The architecture is as follows: It is possible to interact with the game from a java program, getting higher independence (avoiding Unrealscript restrictions) and increasing the possibilities (java libraries). On the contrary, the structures, classes, functions and workflows defined in the Unreal engine, cannot be accessed, nor used.
- 5. Objectives UNREAL EXPERT BOT • Create an autonomous agent for playing Unreal Tournament deathmatch championship. • Considering the constraints of this competition: - 1 vs 1 matches - Small arenas - Weapons are not respawned - Some forbidden items (U-Damage, for instance) - 15 minutes per match instead of a number of frags (kills) • Human-like behaviour is desired. • Modelling Expert player knowledge (and tricks). - High control in timing (items respawn time) - Deep knowledge about weapons and their advantages and disadvantages - Deep knowledge about items
- 6. Features UNREAL EXPERT BOT • Defined by means of a Finite State Machine based AI with two state levels. • Translated into a set of rules which determine its behaviour. • Database which models the bot’s memory, since it is uploaded with data about locations of items and weapons in the map.
- 7. Features UNREAL EXPERT BOT • Defined by means of a Finite State Machine based AI with two state levels. • Translated into a set of rules which determine its behaviour. • Database which models the bot’s memory, since it is uploaded with data about locations of items and weapons in the map.
- 8. Bot performance UNREAL EXPERT BOT • Expert Bot (E-Bot) outperformed the standard bots in the game (considering the number of frags), even in the maximum difficulty level. • This difficulty level is quite hard for a medium level player. • E-Bot is hard to beat for humans, even for the expert. • Medium level players usually lose against it.
- 9. Evolutionary Algorithms EXPERT BOT EVOLUTION by Johann Dréo i -> initial population f -> evaluation function (fitness) ? -> stop condition Se -> selection Cr -> crossover Mu -> mutation Re -> replacement
- 10. Evolutionary Process in Unreal game EXPERT BOT EVOLUTION GE-BOT Expert Bot based in a Genetic Algorithm Evolutionary process population FITNESS EVALUATION • Analyze Expert bot’s FSM • Identify parameters • Optimize them Expert Bot’s AI
- 11. Approaches EXPERT BOT EVOLUTION • Generic Fitness Just considers frags/deads and damage produced/received • Generational scheme • 4-elitism • Complex Fitness - considers frags/deads - damage produced/received - time using the best or more versatile weapons: Lightning Gun and Shock Rifle - getting the best items: Shield and Super Shield • Stationary scheme • Chromosome 143 • Uniform Crossover • Random mutation • 4 Random individuals • Chromosome 26
- 12. Approach 1: Chromosome 143 - Generic Fitness EXPERT BOT EVOLUTION • Generic Fitness Just considers frags/deads and damage produced/received • Generational scheme • 4-elitism • Chromosome 143 • Uniform Crossover • Random mutation • 4 Random individuals
- 13. Approach 1. Results EXPERT BOT EVOLUTION • 30 generations • 30 individuals • 1 evaluation (left) • 3 evaluations (right) in order to avoid the noisy nature of the fitness function • 15 minutes per evaluation • 10 days per run (left) • One month (right) • Lightly improvement tendency • Too many oscillations, i.e. noise • 143 genes are too much
- 14. EXPERT BOT EVOLUTION • Generic Fitness Just considers frags/deads and damage produced/received • Generational scheme • 4-elitism • Uniform Crossover • Random mutation • 4 Random individuals • Chromosome 26 Approach 2: Chromosome 26 - Generic Fitness
- 15. Approach 2. Results EXPERT BOT EVOLUTION • 50 generations • 30 individuals • 5 minutes per evaluation • Results of 2 different runs • 5 days per run • Again lightly improvement tendency • Too much noise • Too much diversity
- 16. EXPERT BOT EVOLUTION • Complex Fitness - considers frags/deads - damage produced/received - time using the best or more versatile weapons: Lightning Gun and Shock Rifle - getting the best items: Shield and Super Shield • Stationary scheme• Uniform Crossover • Random mutation • 4 Random individuals • Chromosome 26 Approach 3: Chromosome 26 - Complex Fitness
- 17. Approach 3. Results EXPERT BOT EVOLUTION • 40 generations • 30 individuals • 5 minutes per evaluation • Stationary scheme to increase the exploitation factor • Results of 2 different runs • 5 days per run • Quite good fitness tendency • Noise still remains, but in a lower factor
- 18. Numerical results E-BOT vs GE-BOT • Expert Bot (E-Bot) and the best Genetic Expert Bots (GE-BOT) have been fighting in four battles (in two maps). • The average results of these matches are: • The approach with 143 genes per chromosome is defeated • GE-Bot with 26 genes outperforms E-Bot. • The approach with the complex fitness function gets the best results. Due to its lower noisy factor, and the higher exploitation component.
- 20. CONCLUSIONS • We have designed a human-like Expert Bot (E-Bot) which outperforms the standard Unreal Tournament 2K4 bots in the hardest difficulty. • It is also a hard rival against human players. • We have tested three different approaches for improving this bot by means of Genetic Algorithms. • Too long chromosomes population performs worse than small length one. • These algorithms are affected by a high noisy factor regarding the generic (and easier) fitness function. • We have defined a complex fitness function which performs better, with a softer noisy effect. • The bots obtained after evolution outperform the E-Bot.
- 21. END THE Questions?!?! Contact: amorag@geneura.ugr.es Source Code: https://github.com/franaisa/ExpertAgent