1. Artificial Intelligence in Video Games Jason Fuller 1

2. What is Game AI?  Imitate intelligence in the actions of non-player characters (NPCs).  Make the game “feel” real.  Obey laws of the game  Show decision making and planning 2

3. Goals of Game AI  Be fun!  Be challenging but not overwhelming (unless the hardest difficulty is selected)  Make sure the AI does not cheat! (At least do not get caught)  AI often get bonuses when difficulty increases  Do not hog all the resources! (CPU time) 3

4. Types of Games 1.Action games  Shooters (FPS and Third-Person)  Racing, Sports 2.RPG games (Role Playing Game)  Often include many action game aspects of AI 3.RTS games (Real Time Strategy) 4

5. Game AI Types  Action and RPG AI tend to work better with Finite State Machine based AI  RTS AI used Finite State Machines in the early years of AI development.  RTS AI work best with Artificial Neural Networks and Fuzzy Logic  Both contain path finding components 5

6. AI Path Finding Dijkstra’s Algorithm A* Algorithm  Most commonly used  Finds the shortest path  The world or map of the game is represented by a grid of points 6

7. A* Algorithm  Allows for high optimization  Either by changing the search algorithm to better suit the game or by changing the data structures.  Very similar to how people move between locations in a city. 7

8. Finite State Machines (FSM)  Simplest and most basic AI model.  Consists of:  States  State Transitions  Most common for Action games!  Not many different actions for NPCs 8

9. Finite State Machines  Among the States and State Transitions there are 4 components:  States which define behavior  State transitions which are the movement from one state to another  Conditions which must be met for state transition  Events/Actions which are internally or externally generated which may lead to a state transition 9

10. FSM Example 10

11. FSM Disadvantages  Very predictable  Too many states get tough to organize  Since there are such crisp rules between states, NPC does not feel natural 11

12. FSM within a State  States have a FSM within them 12

13. Modern FSM Example 13

14. History of Finite State Machines  In 1952, the game Nim used AI to play against an opponent.  1960’s & 1970’s  Spacewar!  Pong  Space Invaders  1980’s  Simcity 14

15. History Continued  1990’s  Dragon Quest IV  Warcraft  Half-Life 15

16. Artificial Neural Networks (ANN)  No agreed definition, most common one is “a network of simple processing elements, which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters.”  Mathematical model inspired by biological neural networks.  An adaptive structure that can learn. 16

17. ANN Structure  Very similar to the structure of our brain.  Input layer, processing (hidden) layer, output layer  Learns by example 17

18. ANN Structure 18  The hidden layer is not just a straight line of nodes  Each node in the hidden layer will contain just a small part of the overall calculation  The nodes have connections between each other with certain weights

19. ANN Structure  The weight of the connections between the nodes determine the outcomes of the calculations  If a node is triggered by 2 different nodes it can then determine which one is more important 19

20. ANN Learning 20

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23. Black & White  Came out in 2001  First to effectively use Artificial Neural Networks 23

24. Halo Wars  Came out in 2009  Featured a “Custom” difficulty level that used Artificial Neural Networks 24

25. Fuzzy Logic  Introduced in 1965 for use in Artificial Intelligence research  Present problems to computers in a way similar to how humans solve problems and that everything is a matter of degree (or preference or context). 25

26. Example Problem A store owner needs to decide how much produce to order. Elements to take in to consideration:  Time of year?  What is the weather like?  Is there a Holiday coming up? 26

27. Video  Fuzzy Logic: An Introduction Fuzzy Logic: An Introduction 27

28. Games Now that the major types of AI have been covered, I will go into more detail about what games they are used in. 28

29. Racing Game AI  Large-scale cheating!  AI already know the track and optimal path  AI already has complete behavior determined before the start of the race 29

30. Racing AI graphs 30 General Path Optimal Path

31. Racing Game AI  In its basic form, it is the most basic of game AI but in some of the racing simulators, the AI are more complicated  If the player is using the optimal path, the AI will actively try to push them off of it.  The AI will also use tricks such as spinning out opponents by making their back tires lose grip. 31

32. FPS Game AI  Implemented with a layered structure  Bottom layers control the path finding tasks and animation selection  Higher layers control the tactical reasoning which is where the Finite State Machine would be. 32

33. A* Graph of FPS or RPG World 33 Unplayable Zone Playable Zone General path

34. FPS Continued  F.E.A.R. series has revolutionary AI  AIs have knowledge of map elements and will flank the player  AI will break through walls and windows to get to the player  AI will rush when they heavily outnumber the player 34

35. RPG Game AI  Many encounters with AI are unscripted  GTA IV and Far Cry 2 made great leaps in “friendly” AI 35

36. Elder Scrolls IV: Oblivion  Released in 2006  During testing, a story important NPC kept being found dead.  A mechanic of the AI was the cause. 36

37. Bioshock Infinite  The player companion, Elizabeth (who is an AI), is almost entirely unscripted. 37

38. RTS Game AI  Started out using Finite State Machines to control AI  Too many options to cover  AI was “dumb”  AI would build up in a strict way  Once the player found a strategy that worked against the AI, it would always work.  RTS AI switched to a combination of Fuzzy Logic and Artificial Neural Networks 38

39. RTS Game AI  By changing to Fuzzy Logic and Artificial Neural Networks (ANN):  Fuzzy Logic led to smarter responses to attacks  ANN led to smarter development of base and better long term decisions 39

40. A* Graph of RTS Map 40

41. RTS Continued  Maxis is again changing the simulation landscape with the new Simcity  Every “Sim” is a full AI  Have there own agenda  Have specific wants and needs 41

42. Video  SimCity: Economics AI SimCity: Economics AI 42

43. Future of Game AI  Game AI have made great leaps forward since they were first developed.  An AI that can learn how you play a game would be a great opponent 43

44. References  Champandard, Alex. "Top 10 Most Influential AI Games." Aigamedev.com. N.p., 12 Sept. 2007. Web. 25 Feb. 2013..  Grant, Eugene, and Rex Lardner. "The Talk of the Town." TheNewYorker.com. The New Yorker, 02 Aug. 1952. Web. 25 Feb. 2013..  Grzyb, Janusz. "Artificial Intelligence in Games." - CodeProject. Software Developer's Journal, n.d. Web. 25 Feb. 2013..  "Neural Networks: A Requirement for Intelligent Systems." N.p., 2007. Web. 25 Feb. 2013..  "Short Term Decision Making with Fuzzy Logic And Long Term Decision Making with Neural Networks In Real-Time Strategy Games." Hevi.info. N.p., n.d. Web. 25 Feb. 2013.. 44

45. Videos  Fuzzy Logic http://www.youtube.com/watch?feature=player_detailpage&v=P8wY6mi1vV8# t=117s  SimCity http://www.youtube.com/watch?feature=player_detailpage&list=UUnje_8ilXP7 KB2vdssyAWug&v=MxTcm1YFKcU#t=37s 45

46. QUESTIONS? 46