Artificial Intelligence in Games

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Artificial Intelligence in Games
Ryan Donnelly donnelry_at_uwplatt.edu
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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

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Computer Game Types

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

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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

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Game AI History -1980

• 1960s
• First computer games (SpaceWar)
• Board games against the computer (Chess)
• 1970s
• 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

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Game AI History 1980-

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

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Game AI History 1980-

• 1990s
• 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

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Game AI History 1980-

• 1990s (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/h
  tml/c.2.shtml
• Graphic cards allowing for more CPU time
• 10-35 of CPU time spent on AI

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Game AI History 1980-

• 2000s
• 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

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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

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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

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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

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Representation of the World in an FPS-type Game
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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

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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.

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Representation of the World in an RTS-type Game
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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

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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 vehicles closest vicinity
• In effect, the computer knows it should slow down
  because its 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

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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 vehicles
  traction back under control

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Popular AI Algorithms Used In Computer Games

• A()
• Finite State Machines
• Artificial Neural Networks

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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

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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

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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)

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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 its 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

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A() Algorithm

• Example
• Manhattan method
• Calculate total of squares moved horizontally
  and vertically to reach target, ignoring diagonal
  movement and obstacles.

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A() Algorithm

• Example (cont.)

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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

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A() Algorithm

• Example (cont.)

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A() Algorithm

• Example (cont.)

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A() Algorithm

• Example (cont.)

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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

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Finite State Machines Cont.

• Advantage Can divide implementation of each
  games objects behavior into smaller fragments
• Easier to debug and extend

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Finite State Machines

• State Diagram

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Finite State Machines Example Pacman Ghost
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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

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Artificial Neural Networks
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Artificial Neural Networks

• Neural Networks in Games?
• Trendy topic in the late 90s into 00s
• Huge potential in computer games
• Collin McRae Rally 2 (2001)
• Total success
• The trained artificial neural network is
  responsible for keeping the computer players car
  on the track while letting it negotiate the track
  as quickly as possible
• Input parameters curvature of the roads 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.

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Artificial Neural Networks

• Obstacles Limiting Neural Networks Application
  in Games
• Problems choosing appropriate input
• Neural networks sensitivity to changes in a
  games 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

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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 havent 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

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Artificial Neural Networks

• Fuzzy logic
• Often used with neural networks
• Conversion from computers 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

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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

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References

• Grzyb, Janusz. Artificial Intelligence in Games.
  Software Developers Journal. June 2005.
• Game Artificial Intelligence. Wikipedia
  Ecyclopedia. September 7, 2006.
  http//en.wikipedia.org/wiki/Game_artificial_intel
  ligence
• Petersson, Anders. Artificial Intelligence in
  Games. WorldForge Newsletter. August 2001.
  http//worldforge.org/project/newsletters/August20
  01/AI/SECTION00020000000000000000
• Popovic, Zoran Martin, Steven Hertzmann, Aaron
  Grochow, Keith. Style-Based Inverse Kinematics.
  2004. http//grail.cs.washington.edu/projects/sty
  leik/styleik.pdf
• A. The Game Programming Wiki. September 15,
  2006. http//gpwiki.org/index.php/A_star