Survey of Biologically-inspired Algorithms in Game A/I

1. Survey of Biologically-inspired Algorithms in Game A/I Clint Jeffery University of Idaho

2. Outline • Preliminary thoughts • AIGPW Chapters • EvoGames Papers • Conclusions

3. Preliminary Thoughts • ANN and related technologies are rare in commercial games • Behavior of ANN-based agents often perceived as bizarre or unrealistic • Biologically inspired algorithms (ANNs, GAs, and relatives) are nevertheless used in a surprising range of roles in games and simulations • Personal interest: want self-balancing dynamic MMOs

4. AI Game Programming Wisdom • 4 anthologies • Not technical / academic / detailed • Selected for today • Imitating Random Variations in Behavior Using a Neural Network, John Manslow • Genetic Algorithms: Evolving the Perfect Troll, F. Laramee • Constructing Adaptive AI Using Knowledge-Based NeuroEvolution, R. Cornelius et al

5. Imitating Random Variations in Behavior Using a Neural Network • Tank battle, human vs. computer • “although neural networks can be taught to imitate human players…they are able to reproduce only the deterministic aspects of their behavior” • Chapter is really about augmenting ANN with random sampling

6. Imitating Random Variations…Unconditional Distribution • Log difference between human error and ANN calculated optimal angle for 5000 samples • Partition 5000 samples into bins, assign probabilities to each bin • Generate new shots by selecting bin based on probability, and picking random value from the interval range of the bin

7. Imitating Random Variations…Conditional Distribution • Human error events not independent: error of current shot depends on error of previous shot • Assign probabilities to bins using a standard classifier multilayer perceptron (MLP) neural network • Record 5000 samples of error + previous shot’s error

8. Genetic Algorithms: Evolving the Perfect Troll • Hand-coded behavior/strategy is time-consuming, limits monster thinking • GAs to the rescue: • Initialize population • Test population, rank fitness • Mate best performers using crossover and mutation • Add new random organisms • Rinse and repeat

9. Genetic Algorithms: Evolving the Perfect Troll • Complex fitness criteria • Individual vs. group performance vs. co-evolution with other species • Chapter considers only individual fitness • Gene representation uses array of reals to represent troll’s bias towards 5 possible goals • Fitness determined by simulation

10. Genetic Algorithms: Evolving the Perfect Troll • Reproduction rights could be reserved exclusively for “fittest” ranked individuals, or by stochastic sampling • Cross-over: many possible methods, author prefers “uniform crossover” • Mutation: probability .001 or less • NextGen=top 20%, 70% children, 10% new • Population size: 100-250

11. Genetic Algorithms: Evolving the Perfect Troll • 5 Troll Goals: eat Sheep, kill/chase Knight, Flee from harm, Heal, Explore • Each goal gets a behavior function that is “sensible” in-game • Genome: 0.0 – 1.0 for each goal serve as weights (priority = G[goal]*need) • 30x30 squares contain: havens, traps, sheep, knights, towers

12. Genetic Algorithms: Evolving the Perfect Troll • Score=8*K+10*S+1.5*Age-1*Capt-2.5*Dam • After 50 generations…you get trolls who spend all their time trying to eat

13. Constructing Adaptive AI Using Knowledge-Based NeuroEvolution • Use Neural Networks to make NPC’s less predictable/exploitable • Preinitialize ANNs with “normal” NPC AI • Convert FSM to ANN

14. Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

15. Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

16. Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

17. Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

18. Constructing Adaptive AI Using Knowledge-Based NeuroEvolution

19. EvoGames • Workshop on Biologically-Inspired Algorithms in Games • 2011 is the 3rd year • Part of Evostar.org • UI CS faculty Terence Soule has been on their program committee • Criterion for mention today: • Selected interesting papers available on web

20. From EvoGames 2009 • Coevolution of Competing Agent Species in a Game-like Environment. TelmoMenezes, Ernesto Costa • Swarming for Games---Emergence as a Gaming Principle. Sebastian von Mammen, Christian Jacob • Evolving Teams of Cooperating Agents for Real-Time Strategy Game. PawelLichocki, Krzysztof Krawiec, WojciechJaskowski

21. Telmo Menezes • http://telmomenezes.com/curriculum-vitae/phd/, Coimbra, Portugal • evoGames paper not on web, but his whole Ph.D. dissertation is… • Gridbrain, a sequentialized, von-Neumann-inspired, evolutionary computation model

22. Telmo Menezes

23. Swarming for Games • http://www.vonmammen.org/science/SwarmGames.pdf • 2 kinds of play • indirectly guide a swarm system • optimize flocking parameters • Flocking formations widely used in RTS games, e.g. Lord of Magic • Leading vs. Herding

24. Swarming for Games • Flocking • Alignment • Cohesion • Separation

25. From EvoGames 2010 • Evolving Bot's AI in Unreal
Antonio Mora, Juan Julián Merelo, et al • Towards a Generic Framework for Automated Video Game Level Creation
Nathan Sorenson, Philippe Pasquier • Evolution of Artificial Terrains for Video Games Based on Accessibility
Miguel Frade, F. F. de Vega, Carlos Cotta • Evolving Behaviour Trees for the Commercial Game DEFCON
Chong-U Lim, Robin Baumgarten, Simon Colton • Evolving 3D Buildings for the Prototype Video Game Subversion
Andy Martin, Andrew Lim, Simon Colton, Cameron Browne

26. From EvoGames 2011 • Towards Procedural Strategy Game Generation: Evolving Complementary Unit TypesTobiasMahlmann, Julian Togelius, Georgios N. Yannakakis

27. A Plug for Dr. Soule • One of our department faculty is a specialist in this area • Check out: • http://www2.cs.uidaho.edu/~tsoule/ladybug

28. A Plug for Dr. Soule • http://www2.cs.uidaho.edu/~tsoule/ladybug