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Antonio Mora, Antonio Fernández-Ares, Juan-J Merelo-Guervós, Pablo García-Sánchez …

Antonio Mora, Antonio Fernández-Ares, Juan-J Merelo-Guervós, Pablo García-Sánchez

This work describes an evolutionary algorithm (EA) for evolving the constants, weights and probabilities of a rule-based decision engine of a bot designed to play the Planet Wars game. The evaluation of the individuals is based on the result of some non-deterministic combats, whose outcome depends on random draws as well as the enemy action, and is thus noisy. This noisy fitness is addressed in the EA and then, its effects are deeply analysed in the experimental section. The conclusions shows that reducing randomness via repeated combats and re-evaluations reduces the effect of the noisy fitness, making then the EA an effective approach for solving the problem.

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  • 1. Dealing with Noisy Fitness in the Design of a RTS Game BotA.M. Mora , A. Fernández-Ares, J.J. Merelo,P. García-Sánchez, y C.M. Fernandes
  • 2. IndexProblem descriptionBaseline: GoogleBotFirst Bot: AresBotGeneBotExperimentsNoise fitness studyConclusions
  • 3. IntroductionGoogle AI Challenge 2010. Design a bot that can play to: Planet Wars
  • 4. Simple view of Planet Wars Game
  • 5. Problem Description Actual List of Bot State actionsRestrictions RTS->Real time strategy with pseudo-turns 1 turn = 1 second Forbidden use of memory between turns. Actual state: planets and fleets. List of Actions: un simple file text witch the movement of fleets from a planets belongs to player and another planet.
  • 6. Baseline: GoogleBot It was include in the Initial Kit of the contest How it works? Choose the BASE planet (the one with most of its starship) Choose the TARGET planet (calculating the ratio between the grow rate and the number of ships) It wastes the rest of time until the attack has finished
  • 7. First Bot: AresBotFor each turn: Choose the BASE planet (the one with most starships). The rest of planet are called COLONIES. Choose the TARGET (who are not already being attacked): EXPANSION: if TARGET is neutral. CONQUEST: if TARGET belongs to enemy. Extra actions for each COLONY: TITHE? SUPPORT ATTACK?
  • 8. Diagram of states
  • 9. Parameters of AresBottitheperc: percentage of fleets (of the stored) thatCOLONIE sent to the BASE.titheprob: probability of the TITHE for each COLONIE.ωNS−DIS: weight of the number of starship hosted at theplanet and the distance form the BASE to the TARGET(it’s used to the score function of target planet). ωGR: weight of the planet growth rate (it’s used to the scorefunction of target planet).
  • 10. Parameters of AresBot IIpoolperc: proportion of extra starships that the bot sendsfrom the base planet to the target planet.supportperc: percentage of extra starships that the botsends from each COLONIE to TARGET.supportprob: probability of sending extra fleets from oneCOLONIE to TARGET (if COLONIE is closer to the TARGET thanBASE are).
  • 11. Operation “GeneBot”Using intergalactic techniques (GA) improves AresBotto create the powerful Genebot. Gen: array of parameters (standardized values) 2-Tournament BLX-alpha crossover Re-evaluation
  • 12. FitnessFor each “bot” fight against the GoogleBot in 5characteristic maps.One bot is better than other if: Wins in more maps. Needs less turns, in case of tie.
  • 13. Noise Fitness Study Unpromising Bot Fitness assigned on first execution: 2057 3005 2505 2005fitness 1505 1005 505 5 0 10 20 30 40 50 60 70 80 90 100 Evaluation # Wins in 5 maps Loses on any of the 5 maps
  • 14. Noise Fitness Study II Promising Bot Fitness assigned on firts execution: 578 3005 2505 2005fitness 1505 1005 505 5 0 10 20 30 40 50 60 70 80 90 100 Evaluation # Wins in 5 maps Loses on any of the 5 maps
  • 15. ExperimentsNum. Generations: 100Num. Individuals: 200Crossover prob: 0.6α : 0.5Mutation prob: 0.02Replacement Policy: 2-elitims
  • 16. Experiments II Each single “evaluation” takes 40 seconds.40 seconds * 200 * 100 ≈ 9.25 days of execution• SOLUTION → Parallel algorithm • N evaluation at same time. (N = 4 in experiments)• Make 15 executions, getting 15 “best bots”.
  • 17. Experiments III GA Evaluation Bot Bot Bot Bot Fitness Fitness Fitness Fitness Simulator Simulator Simulator SimulatorGoogleBot GeneBot GoogleBot GeneBot GoogleBot GeneBot GoogleBot GeneBot
  • 18. Results Relationship of fitness of best individual of each execution 900 857 800 700 687Fitness (less is better) 600 590 559 500 527 531 528 528 540 525 528 525 458 470 400 425 300 200 100 0 E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E11 E12 E13 E14 E15 Executions
  • 19. Results II Relationship of the best bot wins each execution 100% 98% 98% 99% 98% 98% 90% 94% 90% 91% 90% 89% 87% 85% 84% 80% 82% 83%Winning percentage in 100 maps 70% 60% 50% 40% 30% 20% 10% 0% E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E11 E12 E13 E14 E15
  • 20. Results III titheperc titheprob ωNS−DIS ωGR poolperc supportperc supportprobAresBot 0,1 0,5 1 1 0,25 0,5 0,9GeneBot 0,0179 0,00823 0,50954 0,23273 0,73321 0,58946 0,97405Average 0,17386 0,09702 0,47252 0,36409 0,65732 0,59987 0,59987
  • 21. Result IV Fitness Evolution 700 Average 650 Best execution The best of all executionsAggregated number of turns 600 550 500 450 400 350 G0 G5 G10 G15 G20 G25 G30 G35 G40 G45 G50 G55 G60 G65 G70 G75 G80 G85 G90 G95 G100
  • 22. Conclusions and future workThe First Genebot finished in the 1454th position in the contest (36%), butit had executed just once (with less individuals and generations).Genebot wins 99% of battle versus AresBotOur fitness is a good measure of the bot.To DO: Distributed algorithm. Multi-objective. Co-evolution. Recognition of environment and specialist bots Macro and micro evolution…
  • 23. Thanks for your time…Any question?