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COEVOLVING ROBUST STRATEGIES FOR Real-Time Strategy Games

Christopher Ballinger caballinger@cse.unr.edu http://www.cse.unr.edu/~caballinger. COEVOLVING ROBUST STRATEGIES FOR Real-Time Strategy Games. Outline. Artificial Intelligence Game AI Board Games RTS Games StarCraft WaterCraft Motivation Prior Work Methodology Evolutionary Methods

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COEVOLVING ROBUST STRATEGIES FOR Real-Time Strategy Games

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  1. Christopher Ballinger caballinger@cse.unr.edu http://www.cse.unr.edu/~caballinger COEVOLVING ROBUST STRATEGIES FOR Real-Time Strategy Games Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  2. Outline • Artificial Intelligence • Game AI • Board Games • RTS Games • StarCraft • WaterCraft • Motivation • Prior Work • Methodology • Evolutionary Methods • Representation • Encoding • AI Behavior • Current Progress • Conclusions • Future Work Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  3. Artificial Intelligence • (Broadly) understanding and building intelligent agents. (Russell & Norvig) • Intelligent Agent • An autonomous entity which observes through sensors, acts upon an environment using actuators, and directs its activity towards achieving goals. (Russell & Norvig) • Computational Intelligence • A set of nature-inspired computational methodologies and approaches to address complex problems. (Kahraman) • Game AI • Decision-making process of computer-controlled opponents/NPCs (Ponsen et. al.) Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  4. Board Games • Present challenging problems • Complex state space • Adversarial planning • Checkers • State Space - (1020) • Chess • State Space - (1050) • Go • State Space - (10170) • A lot of AI research in the past used board games • Board Game AIs play competitively against humans • RTS games present even more difficult challenges Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  5. Real-Time Strategy • Much more complex than board games • State space is orders of magnitude larger • (1050)36,000 to (10200)36,000 for an entire game match • MUCH more than the number of protons in the observable universe Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  6. Real-Time Strategy • Several categories of challenges for Game AI • Resource Management • Decisions under uncertainty • Spatial/Temporal reasoning • Collaboration • Opponent modeling/learning • Adversarial real-time planning • Remains a challenge for AI, but not an impossible problem • Human players are capable of overcoming these challenges • Humans can adapt to these difficult challenges so well, professional RTS players can make a living playing “e-sports” • Most well known pro-league is for StarCraft Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  7. StarCraft • Objectives: • Manage economy • To build army • Many types of units • Each type has strengths and weaknesses • Getting the right mix is key • Research upgrades/abilities • To destroy enemy Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  8. StarCraft • Development Problems • StarCraft • 3rd-Party API can be used for AI development • Runs (relatively) slow • Hard to run multiple instances in parallel • StarCraft II • No API StarCraft II Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  9. WaterCraft • WaterCraft† • Modeled after StarCraft II • Easy to run in parallel • Runs quicker by disabling graphics † Source code can be found on Christopher Ballinger’s website Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  10. Motivation • RTS games are good testbeds for AI research • Present many challenging aspects • Intransitive relationships between strategies, similar to rock-paper-scissors • No one optimal strategy • Robustness of strategies • We believe designing a good RTS game player will advance AI research significantly (like chess and checkers did) S1 S3 S2 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  11. Previous Work • What we’ve done • Focus on build-orders (our ‘strategy’) • Robustness against multiple opponents • Defeat known/common strategies • Case-injection • Compare two evolutionary methods • Genetic Algorithm (GA) • Coevolutionary Algorithm (CA) • Case-based reasoning (Ontanion, 2006) • Genetic Algorithms + Case-based Reasoning (Miles, 2005) • Reinforcement Learning (Spronck, 2007) • Studies on specific aspects • Combat (Churchill, 2012) • Economy (Chan, 2007) • Coordination (Keaveney, 2011) • Case-Injection into population (Miles, Sushil 2005) Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  12. Evolutionary Methods C0 Population • Terminology • Chromosome • A possible solution • Population • A set of chromosomes • Typically, initial population contains completely random chromosomes • Fitness • A measure of how well a solution/chromosome solves a problem • Generation • The number of iterations we repeat the process C0 C1 1 0 0 1 0 C2 . . . Cn Alleles = 0,1 Gene Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  13. Evaluation • Assign a fitness to all chromosomes in the population Population Evaluate Chromosome Evaluator C0 F0 Assign Fitness C1 F1 C2 F2 . . . … Cn Fn Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  14. Selection • A method for selecting which chromosomes we should select for crossover, and how often. • Roulette Wheel Selection Fn F0 F2 F1 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  15. Crossover • A method to exchange information between two chromosomes (parents), attempting to produce more effective chromosomes (children) Randomly Select Index 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 Parent 1 Parent 2 Child 1 Child 2 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  16. Mutation • A method to make sure certain patterns/capabilities do not permanently go extinct in the entire population 1 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 1 1 1 0 1 1 0 1 1 0 … … … Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  17. New Population • All Children become the Parents for the start of the next generation Old Population (Parents) New Population (Children) C0 C0 F0 C1 F1 C1 C2 F2 C2 . . . … . . . Cn Fn Cn Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  18. Evolutionary Methods • Differences between our GA and CA? • GA: Population plays against the same hand-tuned baselines every generation Generation 1 Teachset (Evaluators) Population C0 Baseline 1 C1 Baseline 2 Cn Baseline 3 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  19. Evolutionary Methods • Differences between our GA and CA? • GA: Population plays against the same hand-tuned baselines every generation Generation 2 Teachset (Evaluators) Population C0 Baseline 1 C1 Baseline 2 Cn Baseline 3 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  20. Evolutionary Methods • Differences between our GA and CA? • GA: Population plays against the same hand-tuned baselines every generation Generation 3 Teachset (Evaluators) Population C0 Baseline 1 C1 Baseline 2 Cn Baseline 3 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  21. Evolutionary Methods • Differences between our GA and CA? • GA: Population plays against the same hand-tuned baselines every generation • CA: Population plays against chromosomes from previous generations Generation 1 Teachset (Evaluators) Population C0 Parent 1 C1 Parent 2 Cn Parent 3 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  22. Evolutionary Methods • Differences between our GA and CA? • GA: Population plays against the same hand-tuned baselines every generation • CA: Population plays against chromosomes from previous generations Generation 2 Teachset (Evaluators) Population C0 Parent 1 C1 Parent 2 Cn Parent 3 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  23. Evolutionary Methods • Differences between our GA and CA? • GA: Population plays against the same hand-tuned baselines every generation • CA: Population plays against chromosomes from previous generations Generation 3 Teachset (Evaluators) Population C0 Parent 1 C1 Parent 2 Cn Parent 3 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  24. å å = + + F SR 2 UC 3 BC ij i k Î Î k UD k BD j j é ù å shared f æ ö 1 = F ê ú ij j è ø ë û l i Î j D i Evolutionary Methods • Identical parameters • Pop. Size 50, 50 generations • Scaled Fitness, CHC selection, Uniform Crossover, Mutation • Teachset • Shared Fitness • CA • Teachset(8 Opponents) • Hall of Fame (HOF) • Shared Selection • GA • Teachset(3 Opponents) • Opponents never change • Baseline strategies k Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  25. Metric - Baseline Build-Orders • Provide a diverse set of challenges • Fast Build • Quickly build 5 Marines and attacks • Doesn’t need much infrastructure • Medium Build • Build 10 Marines and attack • Slow Build • Build 5 Vultures and attacks • Slow, requires a lot of infrastructure • Encoded as a chromosome Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  26. Representation - Encoding • Bitstring • 3-bits per action • Decoded sequentially • Inserts required prerequisites 0 1 0 1 0 0 Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  27. Representation - AI Behavior • Execute actions in the queue as quickly as possible • Do not skip ahead in the queue • “Attack” action • All Marines, Firebats, and Vultures move to attack opponents Command Center • Attack any other opponent units/buildings along the way • If nearby ally-unit is attacked, assist it by attacking opponent’s unit • If Command Center is attacked, send SCVs to defend • Once all threats have been eliminated, SCVs return to their tasks Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  28. Experiment #1 • Want to show that GAs and CAs find good build-orders • Ran GA and CA 10 times • Evolved 15-bit(5 action) build-orders • CA never trained against the baselines • Ran multiple times to see if results could be repeated reliably • GA always found the same two build-orders • CA always found the same single build-order • Exhaustive Search Ballinger, C.; Louis, S., "Comparing Heuristic Search Methods for Finding Effective Real-Time Strategy Game Plans“ Ballinger, C.; Louis, S., "Comparing Coevolution, Genetic Algorithms, and Hill-Climbers for Finding Real-Time Strategy Game Plans" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  29. Exhaustive Search Solution 1 Baseline 1 Baseline 2 • Exhaustive Search against all three baselines • 15-bits was the maximum solution length we could exhaustively search. • Takes 20hrs to do all evaluations • Baselines encoded in 24-39bits, providing them with a large advantage • Shows how frequently the best solutions occur • Ranks all solutions by how many baselines they defeat Baseline 3 Solution 2 Baseline 1 Baseline 2 Baseline 3 Solution N Baseline 1 Baseline 2 Baseline 3 Ballinger, C.; Louis, S., "Comparing Heuristic Search Methods for Finding Effective Real-Time Strategy Game Plans“ Ballinger, C.; Louis, S., "Comparing Coevolution, Genetic Algorithms, and Hill-Climbers for Finding Real-Time Strategy Game Plans" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  30. Results • Exhaustive Search • 32,768(215) possible solutions • 80% of possible solutions lose to all three baselines • 19.9% of possible solutions beat only one of the three baselines • Only 30 solutions (0.1% of possible solutions) can defeat two baselines • Zero solutions could beat all three Ballinger, C.; Louis, S., "Comparing Heuristic Search Methods for Finding Effective Real-Time Strategy Game Plans“ Ballinger, C.; Louis, S., "Comparing Coevolution, Genetic Algorithms, and Hill-Climbers for Finding Real-Time Strategy Game Plans" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  31. Results • CA • Always found the same solution • Four Vultures and a Firebat • Never defeats any baselines • Doesn’t plan for opponents that take more than 5 actions • Still improves score • Beats many other 15-bit strategies Ballinger, C.; Louis, S., "Comparing Heuristic Search Methods for Finding Effective Real-Time Strategy Game Plans“ Ballinger, C.; Louis, S., "Comparing Coevolution, Genetic Algorithms, and Hill-Climbers for Finding Real-Time Strategy Game Plans" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  32. Results • GA • Found solutions that could beat two baselines 100% of the time • Strategy 1 • Two SCVs, Two Firebats, One Vulture • Quick but weak defense • Strategy 2 • Four Firebats, One Vulture • Strong but slow defense Ballinger, C.; Louis, S., "Comparing Heuristic Search Methods for Finding Effective Real-Time Strategy Game Plans“ Ballinger, C.; Louis, S., "Comparing Coevolution, Genetic Algorithms, and Hill-Climbers for Finding Real-Time Strategy Game Plans" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  33. Discussion #1 • GA reliably produces high-quality solutions • CA improves against baselines not seen during training • 15-bits is very limited • Huge disadvantage against the baselines Ballinger, C.; Louis, S., "Comparing Heuristic Search Methods for Finding Effective Real-Time Strategy Game Plans“ Ballinger, C.; Louis, S., "Comparing Coevolution, Genetic Algorithms, and Hill-Climbers for Finding Real-Time Strategy Game Plans" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  34. Experiment #2 • Increased bit-string length to 39 • Same length as our longest baseline • Will CA perform better on a level playing field? • Ran GA and CA 10 times • GA found one build-order • CA found 3 build-orders • Selected 3 random Hall of Fame (HOF) build-orders • Generated 10 random build-orders • All GA, CA, HOF, Random, and Baseline build-orders competed against each other Ballinger, C.; Louis, S., "Robustness of Coevolved Strategies in a Real-Time Strategy Game" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  35. Results - Score • GA fitness highest against Baselines • CA fitness highest against all other build-orders Ballinger, C.; Louis, S., "Robustness of Coevolved Strategies in a Real-Time Strategy Game" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  36. Results - Wins • GA always wins against the baselines • CA beats two of the three baselines • Never appeared during training Ballinger, C.; Louis, S., "Robustness of Coevolved Strategies in a Real-Time Strategy Game" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  37. Results – Command Centers • Percent of C.C. destroyed were very similar • Only two of the three CA build-orders attack Ballinger, C.; Louis, S., "Robustness of Coevolved Strategies in a Real-Time Strategy Game" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  38. Discussion #2 • GA produces high-quality solutions for known opponents • Highest score against the opponents used for training • CA produces more robust solutions • Defeats opponents not seen during training • How difficult are these strategies to a human player? • Can we bias a CA to defeat a human? Ballinger, C.; Louis, S., "Robustness of Coevolved Strategies in a Real-Time Strategy Game" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  39. Experiment #3 • Recorded actions of a human player against a previously coevolved strategy. • Coevolved strategy was 39-bits (13 actions) • Human (me) selected which units to build in real-time • Unit actions were determined by the same rules used by the GA and CA • Human strategies took 75-bits (25 actions) to encode • Very hard to find winning 39-bit strategies without “peeking” • 39-bit strategies can still defeat 75-bit strategies Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  40. Metric – Human Cases • We used two strategies for picking actions • Easy Human (EH) Strategy (75-bits, 25 actions) • Quickly build 2 Marines, attack, repeat • Slows down opponent and chips away at the base • Hard Human (HH) Strategy (75-bits , 25 actions) • Build 9 SCVs, then build Firebats and Vultures in parallel until opponent sends attack force • Defend Command Center and send remaining units to destroy opponents defenseless base • Slow, requires a lot of infrastructure Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  41. Case-Injection • Injected human replays into CA teachset • 2 of the 8 teachset spaces are permanently replaced with the human cases • Not injecting human cases into the population (yet) • GA only trains against the human cases Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  42. Results • Ran GA and CA 10 times • GA always found one build-order • CA always found one build-order • Averaged the GA’s and CA’s population performance against each human strategy for each generation Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  43. Results – Score • GA got the highest scores against the EH strategy • CA got the highest scores against the HH strategy Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  44. Results - Wins • Trivial to beat the EH strategy • GA never learns to defeat HH • Over specializes against EH strategy • CA quickly learns to defeat HH • Still defeats the EH strategy as often as the GA, though the score isn’t as high Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  45. Discussion #3 • GA with fitness sharing can be mislead by large difficulty gap • CA produces high-quality robust solutions • Can be biased towards known opponents • Less prone to being mislead Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution" Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  46. Conclusion • Conclusion • CAs are suitable for finding RTS strategies • Produces robust strategies • Can defeat multiple opponents • Can defeat opponents not seen during training • Can learn to defeat known opponents without becoming over specialized Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  47. Future Work • Future Work • Case-Injection into population • Learn to play like a known player/strategy • Strategy identification and counter-strategy selection • What strategies might the current opponent be using? • What strategies in my case database might be useful to learn from to defeat the current opponent? • System for perpetual Coevolution and Case-Injection • The more people play, the more new and useful strategies we can coevolve • Future-Future Work • More Flexible Encoding • Complete game player • Better opponent modeling Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  48. Acknowledgements • This research is supported by ONR grant N000014-12-c-0522. • More information (papers, movies) • caballinger@cse.unr.edu (http://www.cse.unr.edu/~caballinger) • sushil@cse.unr.edu (http://www.cse.unr.edu/~sushil) Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

  49. Publications • Liu, S.; Ballinger, C.; Louis, S.; "Player Identification from RTS Game Replays", Computers and Their Applications (CATA), 2013 28th International Conference on, 4-6 March 2013 • Ballinger, C.; Louis, S., "Comparing Heuristic Search Methods for Finding Effective Real-Time Strategy Game Plans", IEEE Symposium Series on Computational Intelligence (SSCI) 2013, 16-19 April 2013 • Ballinger, C.; Louis, S., "Comparing Coevolution, Genetic Algorithms, and Hill-Climbers for Finding Real-Time Strategy Game Plans", Genetic and Evolutionary Computation Conference (GECCO) 2013, 6-10 July 2013 • Ballinger, C.; Louis, S., "Robustness of Coevolved Strategies in a Real-Time Strategy Game", IEEE Congress on Evolutionary Computation (CEC) 2013, 20-23 June 2013 • Ballinger, C.; Louis, S., "Finding Robust Strategies to Defeat Specific Opponents Using Case-Injected Coevolution", IEEE IEEE Conference on Computational Intelligence and Games (CIG) 2013, 11-13 August 2013 • In Preparation: • Identifying Pro StarCraft II players and strategies (IEEE T-CIAIG) Evolutionary Computing Systems Lab (ECSL), University of Nevada, Reno

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