Sports Games AI

1. Sports Games AI CSE 348 Daniel Phillips Dan Ruthrauff

2. Intro • Video game AI primarily focuses on RTS and FPS genres • Sports games offer unique challenges • Meeting player's expectations • Minimizing cheating • Larger scale coordination

3. Traditional Sports Games • Object-oriented architecture • Set number of players and a ball • Create teams and behave within a set of rules • Players have predetermined behaviors • Distinct starting and ending points, with clear transitions between behaviors

4. Traditional Sports Games • Entirely event driven • No need for complex pathfinding etc. • Every traditional sport has a "starting event" • Baseball = Ball Hit • Football = Snap • Hockey = Puck Dropped • For these reasons, AI can be controlled by Finite-State Machines and Transition Tables

5. Baseball • Good study in AI • Based on percentages, risks, and expected behavior • Divided into three areas: • Fielding • Baserunning • Batting / Pitching

6. Baserunning • Mapped into more complex behaviors • Go - head to next base • Go Back - complement of Go • Watch - wait for conditions to be met • Freeze - Stop moving • Go Halfway - Default assumption for fly ball • Turn and Go - Round the base and continue to next • Turn and Look - Round the base but don't Go • Only two basic behaviors • Running or not running

7. Fielding • Behaviors triggered by one of two events: • Ball Hit • Ball Fielded • On Ball Hit, players are assigned one of four behaviors based on ball's trajectory • Field Ball • Cover Base • Cutoff • Backup • Seems simple, but fielding is more complicated to implement than baserunning

8. Fielding • On Ball Fielded Event • Players without the ball naturally transition to other behaviors if needed • Player with the ball decides what to do with it • ex - Throw Ball or Run With Ball

9. Calculate hit type and zone based on the flight path Map a set of behaviors to each hit type and zone pair The rules get each teammate working towards a common goal How the rules are implemented determines how "smart" the AI appears Initial Behavior Assignment - Fielders • Ball's flight is 100% predetermined • Only need the initial velocity vector

10. Cheating • Defined as giving the agents access to information that would be unavailable to the player in the same situation • ex - Madden knowing your plays • Can also apply to when the computer follows different "rules" than the player • ex - Unlimited boost in racing games • Does not apply to computer's inherent advantages

12. Cheating • Common in older games, but is generally discouraged now and recent advances in AI attempt to minimize cheating • Some degree of cheating can be acceptable if it's not painfully obvious to the player • Obvious cheating = bad reviews! "0/10 WOULD NOT PLAY"

13. Rubber Band AI • The more you're winning at a game, the harder the game gets to maintain a challenge • Accomplished most often in sports AI by cheating to catch up • Computer controls the outcome of "random" events • FIFA • Computer violates rules that user must follow • NBA Jam • Most common in racing games • Losing cars get a speed boost • Winning cars get a penalty

15. Rubber Band AI example code

16. History of AI in Madden • Original Madden - Madden '93 • Predictable and limited play calling • Madden '95 • Play calling overhauled and now mimicked actual decisions made in the NFL • Madden '96 • Introduction of multiple skill levels • Passing and running still highly unbalanced • Poor defensive AI made it easy to pass • Poor offensive blocking AI made it difficult to run

17. History of AI in Madden • Madden '97 • Running issue resolved, but AI still lacking • Possible to win by only using one or two plays • Madden '98 • Defense now counters the same play if it is called multiple times in a row • Madden '99 • Huge improvement in defensive AI • Fans of the series complained the AI was "too good" • Madden '00 • Customizable AI • Most significant update out of any Madden release

18. Cheating in Madden • Unexplainable comebacks in 4th quarter • Fumble when taking a knee to run the clock • Computer opponent breaking five or more tackles for the game winning touchdown • Bad luck or forced events? • No good way to prove either argument • Highly debated until producers of Madden debunked the claims of "comeback cheating" • More aggressive play calling • Controversy surrounding this demonstrates the importance of fair AI to the players

19. Cheating in Madden • One undeniable method of cheating is Madden's "quick and dirty" difficulty levels • Harder difficulties increase the computer player's physical attributes while decreasing the user's attributes Generally players would rather be defeated by a smarter AI at higher difficulties than a physically superior team

20. Cheating in Madden • Official response from EA Sports concerning Madden's difficulty mechanics • Increases CPU's reaction time, awareness, and frequency of recalculating factors • Admit to increasing the CPU team's attributes • EA claims that "even NFL players feel the game provides an excellent approximation of reality"

21. Innovations • Change individual aspects of the game's AI by adjusting sliders • Design a playing experience that they enjoy • Cheating is easier to detect in sports games, so developers were forced to build strong AI with minimal cheating • Put the AI at the center of development • Listen to the users' feedback

22. Artificial Intelligence in Racing Games • The first racing game was Gran Trak 10 for the Atari • Player races alone against the clock • No AI

23. Artificial Intelligence in Racing Games • The first racing game with AI was Super Speed Race • Scrolling graphics • The AI was very simplistic

24. Pole Position • First realistic racing game • AI opponents that raced against the player

25. Spline • Pole Position used Spline to control computer racers. • The opponent follows a preset line and only deviates from the path to pass • This preset line is hard coded by developers • This leads to predictable and formulaic behavior • Video Example

26. Super Mario Kart • First game to implement dynamic AI behavior into the genre • AI racers had to navigate complex tracks and pick up powerups • Used path following and obstacle avoidance to create lifelike, competitive opponents • Video Example

27. Path Following • Path following allows the AI to follow a series of preset nodes • Path following navigates to each node by using the steer method “seek” (this will be explained in a few slides) • Path following allows for dynamic changes to the path • A racer may see a powerup and decide to go out of their way to get it instead of following the track. • Creates fluid movement • Video Example

28. Obstacle Avoidance • AI racers alter their direction if an obstacle is blocking their path • The first step is finding out if an obstacle will be in the way if the racer continues on its current trajectory • If there are multiple objects in the racers path, the closest object will take precedence and corrective steering will be applied

29. Obstacle Avoidance • In this figure we see three obstacles: A, B, and C • We can easily see that the racer will collide with both B and C if left on its current path • Because obstacle B is closest, corrective steering is applied to the left to avoid it

30. Obstacle Avoidance • Finding the amount to correctively steer is a multi step process • First we must compute the amount of time it is going to take with the current velocity to reach the obstacle • Then we must decide how much we wish to avoid the obstacle by • Once we know these two values, we can determine a point in space to navigate to • This point can then be navigated using a basic steering method called “Seek”

31. How Seek Works • Seek allows a racer to steer and reach a point desired_velocity = normalize (position – target) * speed steering_amount = desired_velocity – current_velocity

32. Colin Macrae Rally • Released in 2001, this Colin Macrae Rally (later known as “Dirt”) was the first racing game to use machine learning to develop AI behavior • Previous racing games implemented sets of rules and techniques to control racers like the previous discussed splines, path following, and obstacle avoidance

33. Colin Macrae Rally • Because Dirt featured racing on complex terrain (such as muddy roads, gravel, and other slippery surfaces), the AI designer found it difficult to implement standard car control techniques • Video Example

34. Colin Macrae Rally • He decided to try a neural network to model the behavior of the car • The final result was a feed forward multilayer perceptron

35. Colin Macrae Rally • The inputs were various values that described the cars movement, direction, position, and track terrain • The outputs were various values that determined how well the car was following the track

36. Colin Macrae Rally • The Neural Network was successfully able to design the behavior of the racer despite the complex terrain • Video Example

37. Problems with Machine Learning in Racing Games • The computer learns too well leading to “perfect” racing • Racing maneuvers such as overtaking and accident recovery are hard to implement with this strategy

38. Forza Motorsport • Took machine learning in the racing genre to the next level with Drivatar • Developed by Microsoft’s advanced AI Research team in Cambridge, England • AI behavior is trained by humans • Leads to very realistic racing behavior

39. Drivatar • Uses a neural network • The training sets for the neural network are created by actual people controlling the cars • Drivatar allows the AI to learn not only car movement behavior, but also race strategies • Drivatar succeeds where Dirt’s neural network failed • Unfortunately, the nitty gritty details of Drivatar are kept a secret as the algorithm is still being used in current versions of Forza

40. What we do know about Drivatar • Splits racing behavior into three categories • Turning Control • Speed Control • Racing Strategy • Each “training lesson” teaches the computer a different category • Video of Drivatar Turn Training

41. Commentary AI in Sports Games • A sports game should be as realistic as possible • Commentary helps immerse the player in the game • One of the first games to include in game commentary is Madden Football

42. Commentary AI in Sports Games • Segments of dialog are split up into different categories: • Different combinations of these segments are threaded together to create coherent sentences that accurately describe the events of the game

43. Commentary AI in Sports Games • An event driven algorithm controls dialog • Each event should have one or more options of dialog attached to it • The goal is to give the announcer enough choices for each event so that the commentary doesn’t get stale, even after you play the game many times

44. Commentary Examples • Set of all possible events that can happen in a corner kick during a soccer game 1 2 3

45. Commentary Examples Possible Options For <Time on clock> <Formation or Strategy> 1 1 2 3

46. Commentary Examples Possible Options For <Play by Play> 2 1 2 3

47. Commentary Examples Possible Options For <Play by Play> <Player Statistic> <Team Statistic> 3 1 2 3

48. AI Cooperation • In sports games, often times the behavior of computer controlled teammates can be just as important as the opponents • Teammates must cooperate with the player • Cooperation is a challenge because every player has a different style of play

49. Options for Modeling Teammate Behavior • Maintain a static behavior, forcing the player to conform to the style of the computer • Static behavior leads to static (and boring) game play • It can be frustrating if the computer plays a different way then the player • Example: NBA Jam • On defense: CPU plays man to man defense at all times (never zone defense or any other strategy) • On offense: CPU goes towards the basket if you are outside the three point line and vice versa

50. Options for Modeling Teammate Behavior • Allows the player to choose various options that let them customize how the AI behaves • Allows the player to feel like a part of the team • Less frustrating gameplay • Example: NHL 12 • Players can choose offensive and defensive formations for the team • Players can choose various strategies to be used in different situations