1. Artificial Intelligence in Games
Ryan Donnelly

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 Role Playing Games (RPG)
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’ No Cheating! Run fast Use minimal memory
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 1970’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 FPS and RTS 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
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 Event Driven Engine
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 Event Driven Engine
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 Scripted behavior
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 Optimization
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
Finite State Machines
Artificial Neural Networks

21. A(*) Algorithm Goal: Find shortest path Prerequisites Basic Method
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 Closed list G score H score
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,
Petersson, Anders. Artificial Intelligence in Games. WorldForge Newsletter. August
Popovic, Zoran; Martin, Steven; Hertzmann, Aaron; Grochow, Keith. Style-Based Inverse Kinematics
A*. The Game Programming Wiki. September 15,