Designing and Evolving an Unreal Tournament 2004 Expert Bot

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This presentation describes the design of a bot for the first person shooter Unreal Tournament— 2004, which behaves as a human expert player in 1 vs. 1 death matches. This has been implemented modelling the actions (and tricks) of this player, using a state-based AI, and supplemented by a database for ‘learning’ the arena. The expert bot yields excellent results, beating the game default bots in the hardest difficulty, and even being a very hard opponent for the human players (including our expert). The AI of this bot is then improved by means of three different approaches of evolutionary algorithms, optimizing a wide set of parameters (weights and probabilities) which the expert bot considers when playing. The result of this process yields an even better rival; however the noisy nature of the fitness function (due to the pseudostochasticity of the battles) makes the evolution slower than usual.

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Designing and Evolving an Unreal Tournament 2004 Expert Bot

  1. 1. Designing and Evolving anUnreal Tournament 2004Expert BotA.M. Mora, F. Aisa, R. Caballero, P. García-Sánchez, J.J. Merelo, P.A. Castillo, R. Lara-CabreraInternational Work-Conference on Artificial Neural Networks
  2. 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. 3. Environment descriptionUNREALUnreal is a first person shooter (FPS).Famous due to the excelent AI of the enemies (bots), which makes it anamazing multiplayer game. Unreal Tournament series is widely extended.It offers an editor (UnrealEd) which lets us change almost anything inthe game even the behaviour of the bots. It uses the languageUnrealScript.
  4. 4. General descriptionPOGAMUTA java middleware for Unreal Tournament series games andDefcon 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 andworkflows defined in the Unreal engine, cannot be accessed, norused.
  5. 5. ObjectivesUNREAL EXPERT BOT• Create an autonomous agent for playing UnrealTournament 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 anddisadvantages- Deep knowledge about items
  6. 6. FeaturesUNREAL EXPERT BOT• Defined by means of a Finite State Machine based AI with twostate levels.• Translated into a set of rules which determine its behaviour.• Database which models the bot’s memory, since it is uploadedwith data about locations of items and weapons in the map.
  7. 7. FeaturesUNREAL EXPERT BOT• Defined by means of a Finite State Machine based AI with twostate levels.• Translated into a set of rules which determine its behaviour.• Database which models the bot’s memory, since it is uploadedwith data about locations of items and weapons in the map.
  8. 8. Bot performanceUNREAL EXPERT BOT• Expert Bot (E-Bot) outperformed the standard bots in the game(considering the number of frags), even in the maximumdifficulty level.• This difficulty level is quite hard for a medium levelplayer.• E-Bot is hard to beat for humans, even for the expert.• Medium level players usually lose against it.
  9. 9. Evolutionary AlgorithmsEXPERT BOT EVOLUTIONby Johann Dréoi -> initialpopulationf -> evaluationfunction (fitness)? -> stop conditionSe -> selectionCr -> crossoverMu -> mutationRe -> replacement
  10. 10. Evolutionary Process in Unreal gameEXPERT BOT EVOLUTIONGE-BOTExpert Bot based in aGenetic AlgorithmEvolutionaryprocesspopulationFITNESS EVALUATION• Analyze Expert bot’s FSM• Identify parameters• Optimize themExpertBot’sAI
  11. 11. ApproachesEXPERT BOT EVOLUTION• Generic FitnessJust considers frags/deadsand damage produced/received• Generational scheme• 4-elitism• Complex Fitness- considers frags/deads- damage produced/received- time using the best or moreversatile weapons: LightningGun and Shock Rifle- getting the best items: Shieldand Super Shield• Stationary scheme• Chromosome 143• Uniform Crossover• Random mutation• 4 Random individuals• Chromosome 26
  12. 12. Approach 1: Chromosome 143 - Generic FitnessEXPERT BOT EVOLUTION• Generic FitnessJust considers frags/deadsand damage produced/received• Generational scheme• 4-elitism• Chromosome 143• Uniform Crossover• Random mutation• 4 Random individuals
  13. 13. Approach 1. ResultsEXPERT BOT EVOLUTION• 30 generations• 30 individuals• 1 evaluation (left)• 3 evaluations (right)in order to avoid thenoisy nature of thefitness function• 15 minutes perevaluation• 10 days per run (left)• One month (right)• Lightly improvementtendency• Too many oscillations,i.e. noise• 143 genes are too much
  14. 14. EXPERT BOT EVOLUTION• Generic FitnessJust considers frags/deadsand damage produced/received• Generational scheme• 4-elitism• Uniform Crossover• Random mutation• 4 Random individuals• Chromosome 26Approach 2: Chromosome 26 - Generic Fitness
  15. 15. Approach 2. ResultsEXPERT BOT EVOLUTION• 50 generations• 30 individuals• 5 minutes perevaluation• Results of 2 differentruns• 5 days per run• Again lightlyimprovement tendency• Too much noise• Too much diversity
  16. 16. EXPERT BOT EVOLUTION• Complex Fitness- considers frags/deads- damage produced/received- time using the best or moreversatile weapons: LightningGun and Shock Rifle- getting the best items: Shieldand Super Shield• Stationary scheme• Uniform Crossover• Random mutation• 4 Random individuals• Chromosome 26Approach 3: Chromosome 26 - Complex Fitness
  17. 17. Approach 3. ResultsEXPERT BOT EVOLUTION• 40 generations• 30 individuals• 5 minutes perevaluation• Stationary scheme toincrease theexploitation factor• Results of 2 differentruns• 5 days per run• Quite good fitnesstendency• Noise still remains,but in a lower factor
  18. 18. Numerical resultsE-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 bestresults. Due to its lower noisy factor, and the higherexploitation component.
  19. 19. DEMOE-BOT vs GE-BOThttp://www.youtube.com/watch?v=ktcXHZ-nAfw
  20. 20. CONCLUSIONS• We have designed a human-like Expert Bot (E-Bot) whichoutperforms the standard Unreal Tournament 2K4 bots in thehardest difficulty.• It is also a hard rival against human players.• We have tested three different approaches for improving thisbot by means of Genetic Algorithms.• Too long chromosomes population performs worse than smalllength one.• These algorithms are affected by a high noisy factorregarding the generic (and easier) fitness function.• We have defined a complex fitness function which performsbetter, with a softer noisy effect.• The bots obtained after evolution outperform the E-Bot.
  21. 21. ENDTHEQuestions?!?!Contact: amorag@geneura.ugr.esSource Code: https://github.com/franaisa/ExpertAgent

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