Artificial Intelligence in Games

1. Artificial Intelligence in Games Ryan Donnelly donnelry@uwplatt.edu

2. What is AI in Games? • Techniques used in computer and video games to produce the illusion of intelligence in the behavior of non-player characters • A game must ‘feel’ natural • Obey laws of the game • Characters aware of the environment • Path finding (A*) • Decision making • Planning • Game ‘bookkeeping’, scoring • ~50% of project time building AI

3. Computer Game Types • Strategy games • Real Time Strategy (RTS) • Helicopter view • Role Playing Games (RPG) • Action games • First person shooter (FPS) • Sports games

4. Goals of Game AI • Be ‘fun’ • Reasonable challenge with natural behavior • No Cheating! • AI has bonuses over human players such as: • Giving more damage • Having more health • Driving faster • Etc. • Used to increase difficulty • Draws away focus to program more human-like bots. • Run fast • Use minimal memory

5. Game AI History -1980 • 1960’s • First computer games (SpaceWar) • Board games against the computer (Chess) • 1970’s • Atari (1972) • Nolan Bushnell • Pong • First AI implemented into games • Stored patterns • Space Invaders (1978) • Distinct moving patterns • Galaxian (1979) • More complex and varied enemy movements • 1-2% of CPU time spent on AI

6. Game AI History 1980- • 1980’s • Fighting games • Karate Champ (1984) • AI defeated a human player in chess for the first time (1983) • Pac-Man (1980)

7. Game AI History 1980- • 1990’s • Sports games • Madden Football • FPS and RTS games • RTS games had problems • Path finding • Decisions • Many more • Dune II: Enemy attacked in a bee line and used cheats • RTS games did get better • WarCraft • First game to implement path-finding at such a large scale

8. Game AI History 1980- • 1990’s (cont.) • Finite state machines • Neural networks • Battlecruiser 3000AD (1996) • Deep Blue defeats chess champ Gary Kasparov (1997) • Chess playing computer developed by IBM • Inspires AI developers • http://www.research.ibm.com/deepblue/games/game6/html/c.2.shtml • Graphic cards allowing for more CPU time • 10-35% of CPU time spent on AI

9. Game AI History 1980- • 2000’s • More games using neural networks • Black & White (2001) • Collin McRae Rally 2 (2001) • Hyperthreading • More sophisticated AI engines while simultaneously creating a more realistic 3D environment • Core Duo • Even more complex AI engines

10. AI in Different Game Types • FPS & RPG • AI is in opponents, teammates, and extra characters • RTS • AI on all sides • Sports Games • AI is in opponents and teammates

11. AI in FPS-type Games • Layered AI Structure • Bottom layers = trivial • Determine paths • Top layers = non-trivial • Reasoning and behavior • Event Driven Engine • Action based on events • Good Idea to use leaking buckets to make more flexible • Leaking Buckets • Buckets leak contents over time • Script with the most filled bucket gets executed

12. AI in FPS-type Games Cont. • Path-Finding • Based on graphs describing the world • A(*) • Most commonly used • Guaranteed to find shortest path • Animation System • Play appropriate sequence of animation at the chosen speed • Play different animation sequences for different body parts (i.e. run and aim, and shoot and reload weapon while still running) • Inverted kinematics • Process of computing the pose of a human body from a set of constraints • i.e. An IK animation system can appropriately calculate the parameters of arm positioning animation so that the hand can grab an object located on a table or shelf

13. Representation of the World in an FPS-type Game

14. AI in RTS-type Games • Path-Finding • Handle collisions • A(*) • Event Driven Engine • Maps Represented by a Rectangular Grid • Module that analyzes the game map uses a goal driven engine • Take highest rank goal and process it. • Smaller sub-goals are created as needed and are processed until the goal has been fulfilled. • Analyzes terrain and a settlement is built based on evaluation of the terrain • Decides when cities should be built and how reinforcements should be placed

15. AI in RTS-type Games Cont. • Interaction between event driven and goal driven engine example: • A building gets blown up by an air strike • This sparks the event based engine to give a new goal to the goal based engine to increase air defenses • The goal based engine responds by moving units that are capable of air defense into position.

16. Representation of the World in an RTS-type Game

17. AI in RPG-type Games • Little AI • Random encounters • More common in games where fighting and gaining levels is more important • Scripted behavior • Often coupled with some minor AI • Common in games depending more on their story line than other things • Often a combination of both

18. AI in Sports Games • Cheating • Racing games • Segmentation • Track gets split into small sectors. • Each element gets its length calculated • Fragments used to obtain characteristics of the road in the vehicle’s closest vicinity • In effect, the computer knows it should slow down because it’s approaching a curve or an intersection • Optimization • Two curves are marked on the track • First represents the optimal driving track • Second represents the track used when overtaking opponents • AI system must analyze terrain • Detect obstacles lying on the road

19. AI in Sports Games Cont. • Racing games (cont.) • Strict co-operation with physics module • Physics module provides information such as when the car is skidding • The AI system, having received the information that the car is skidding, should react appropriately and try to get the vehicle’s traction back under control

20. Popular AI Algorithms Used In Computer Games • A(*) • Finite State Machines • Artificial Neural Networks

21. A(*) Algorithm • Goal: Find shortest path • Prerequisites • Graph • Method to estimate distance between points (heuristic) • Basic Method • Try all paths? • Takes time • Orient search towards target • Minimizes areas of the map to be examined • Uses heuristics that indicate the estimated cost of getting to the destination • Main advantage

22. A(*) Algorithm • Algorithm • Open list • Nodes that need to be considered as possible starts for further extensions of the path • Closed list • Nodes that have had all their neighbors added to the open list • G score • Contains the length or weight of the path from the current node to the start node • Low lengths are better • Every node has a G score • H score • Heuristic • Resembles G score except it represents an estimate of the distance from the current node to the endpoint • To find shortest path, this score must underestimate the distance

23. A(*) Algorithm • Algorithm (cont.) • Start with an empty closed list and just the starting point in the open list • Every node has a G score and the node that was used to arrive at this node (Parent node)

24. A(*) Algorithm • Algorithm (cont.) • Extend the path • Calculate the H scores of the nodes in the open list using a heuristic method. • Pick the node (P) in the open list for which the sum of the G and H scores is the lowest. Note: If the open list is empty then no path • For every point adjacent to P not in the closed or open list, add it to the open list. The previous nodes for these new nodes is P, and their G score is the G score of P plus the distance between the new node and P. If it was already in the open list, check it’s current G score, and if the new G score would be less than the current one update the G score and previous node, otherwise leave it alone. • If the new point is the destination point, you have found your path. • Move P to the closed list and start over

25. A(*) Algorithm • Example: • Manhattan method • Calculate total # of squares moved horizontally and vertically to reach target, ignoring diagonal movement and obstacles.

26. A(*) Algorithm • Example (cont.):

27. A(*) Algorithm • Example (cont.): Notice: 2 squares = 54 • Can be faster to choose last one added to the open list • This biases the search in favor of squares that get found later on in the search, when you have gotten closer to the target

28. A(*) Algorithm • Example (cont.):

29. A(*) Algorithm • Example (cont.):

30. A(*) Algorithm • Example (cont.):

31. Finite State Machines • Each object in a game can have a number of states during its life. • i.e. patrolling, attacking, resting, etc. • Model of behavior composed of: • States • Stores information about the past, i.e. it reflects the input changes from the system start to the present moment • Transitions • Indicates a state change and is described by a condition that would need to be fulfilled to enable the transition • Actions • Entry action • executed when entering the state • Exit action • executed when exiting the state • Input action • executed depending on present state and input conditions • Transition action • executed when performing a certain transition

32. Finite State Machines Cont. • Advantage: Can divide implementation of each games object’s behavior into smaller fragments • Easier to debug and extend

33. Finite State Machines • State Diagram

34. Finite State Machines Example: Pacman Ghost

35. Artificial Neural Networks • Brain • Receives input • Processes input • Communicates the output • Relies on the cooperation of the individual neurons within the network to operate • If some neurons are not functioning, the network can still perform its overall function • Trainable • Learn to solve complex problems from a set of examples • Generalizes the “acquired knowledge” to solve unforseen problems

36. Artificial Neural Networks

37. Artificial Neural Networks • Neural Networks in Games? • Trendy topic in the late 90’s into 00’s • Huge potential in computer games • Collin McRae Rally 2 (2001) • Total success • The trained artificial neural network is responsible for keeping the computer player’s car on the track while letting it negotiate the track as quickly as possible • Input parameters: curvature of the road’s bend, distance from the bend, type of surface, speed, or the vehicles properties • Output: selected in a way so that the car travels and negotiates obstacles or curves at a speed optimal for the given conditions.

38. Artificial Neural Networks • Obstacles Limiting Neural Networks’ Application in Games • Problems choosing appropriate input • Neural network’s sensitivity to changes in a game’s action logic, and the need for re-training the network whenever such a situation occurs • Rather complicated theory, and difficulties with debugging in case of problems • Time-consuming and complicated process of training the network

39. Artificial Neural Networks • How to take advantage of an artificial neural network in a simple game? • Need to know what kinds of information the neural network should provide to help solve the problem • Choose input parameters • Choose in a way that its different combinations will let the neural network learn to solve problems which haven’t appeared in the example set of signals • Should represent as much information about the game world as possible • i.e. vectors of relative positions of the nearest obstacle or opponent, the enemies strength, etc. • Acquire set of input data for training • Significant effort • Train the neural network • Mixed with simultaneous testing to make sure the game is not too difficult, or if too easy and in need of further training

40. Artificial Neural Networks • Fuzzy logic • Often used with neural networks • Conversion from computer’s reasoning into something more strongly resembling the way a human thinks • Usually in the form: • IF variable IS set THEN action • i.e. • IF road IS dry THEN maintain normal speed • IF road IS wet THEN slow down

41. Conclusion • Games With No AI? • Not possible! • Every game with computer controlled characters/opponents uses some sore of AI • Game AI has come a long way since the 1970s • Future looks bright • Neural networks are the future of computer games and a future that is not that distant anymore

42. References • Grzyb, Janusz. Artificial Intelligence in Games. Software Developer’s Journal. June 2005. • Game Artificial Intelligence. Wikipedia Ecyclopedia. September 7, 2006. http://en.wikipedia.org/wiki/Game_artificial_intelligence • Petersson, Anders. Artificial Intelligence in Games. WorldForge Newsletter. August 2001. http://worldforge.org/project/newsletters/August2001/AI/#SECTION00020000000000000000 • Popovic, Zoran; Martin, Steven; Hertzmann, Aaron; Grochow, Keith. Style-Based Inverse Kinematics. 2004. http://grail.cs.washington.edu/projects/styleik/styleik.pdf • A*. The Game Programming Wiki. September 15, 2006. http://gpwiki.org/index.php/A_star