Dead Reckoning in Sports and Strategy Games

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Dead Reckoning in Sports and Strategy Games
8.4

• Francois Dominic Laramee

Building a Sports AI Architecture
8.5
Terry Wellmann
Dayne Mickelson AI Game Programming November 4,
2005
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Dead Reckoning in Sports and Strategy Games
Dead Reckoning (DR)

• PREDICT FUTURE MOTIONEstimation the position of
  an object given
• its original position
• intended course
• Speed
• amount of time passed

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Dead Reckoning in Sports and Strategy Games
Origins in Real Life

• Developed for Navigators / Sailors
• Not taking wind or current into consideration
• The Stronger the effect of these outside factors
  the less reliable an estimate
• For Heavy fog without GPS System
• DR Provides enough info to steer clear of
  dangerous obstacles estimate position

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Dead Reckoning in Sports and Strategy Games
Real Life Usage

• Used in Military Exercises
• Effectively know where enemy
• will be at time of attack
• Estimate movement of enemy fleets / troops
• Between instances of -
• Visual Contact
• Radar
• Spy Satellite Visualization

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Dead Reckoning in Sports and Strategy Games
DR Use in Video Games

• Military Simulation
• Attack effectively if know where enemy will be
  located at time of attack
• Sports Games
• Exchange the ball/puck every few seconds between
  players.
• Know where teammate / opponent will be
• Offset Latency of Online Games

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Dead Reckoning in Sports and Strategy Games
Implementation of Dead Reckoning

• PSEUDO-BROWNIAN MOTION
• KINEMATICS

• INERTIAL MOTION

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Dead Reckoning in Sports and Strategy Games
Implementation of Dead Reckoning

• INERTIAL MOTION

• MOST Basic Level of Dead Reckoning
• Uses Newtons First Law of Motion
• Knowing objects position and speed we can assume
  it will continue to travel in straight line
• Pt P0 vt
• This simple model is good for objects free of
  outside influence
• Asteroid, Spaceship, hockey puck

• Px,t1 Px,t vx
• Py,t1 Py,t vy
• Pz,t1 Pz,t vz

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Dead Reckoning in Sports and Strategy Games
Implementation of Dead Reckoning

• INERTIAL MOTION

• Human Players cant do much better
• Model might be TOO GOOD
• EX.) Shooting at enemy player
• May insert evaluation errors into calculation
• Add random variable with mean actual velocity.
  Dependent on Difficulty Level.

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Dead Reckoning in Sports and Strategy Games
Implementation of Dead Reckoning

• PSEUDO-BROWNIAN MOTION

• Object that is Extremely maneuverable and
  impossible to predict its velocity vector over
  lengthy periods
• UFO or Mosquito
• Dominated by overwhelming outside factors
• Assume that object's initial position and
  magnitude of velocity vector is known.
• Compute average displacement

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Dead Reckoning in Sports and Strategy Games
Implementation of Dead Reckoning

• PSEUDO-BROWNIAN MOTION

• Initial Position
• After Time t Passed
• Calculate Spherical region of space in which it
  could have moved

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Dead Reckoning in Sports and Strategy Games
Implementation of Dead Reckoning

• KINEMATICS

• If objects initial velocity is unknown it can
  be compute by 1st derivative of plotted position
• Estimate future trajectory by using its
  acceleration vector
• Acceleration, Initial Position, Velocity
• Pt P0v0t0.5at2

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Dead Reckoning in Sports and Strategy Games
Implementation of Dead Reckoning

• USES

• KINEMATICS

• Ballistic Missile
• or Spaceship
• Enemy Ship Water current vector applied to
  enemy ship is same as AIs own ship, so they
  actually cancel out
• Human Player from accel. buttons pressed
• Can compute acceleration of any object in game

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Dead Reckoning in Sports and Strategy Games
Dead Reckoning in Sports Games

• AI is trying to shoot ball or puck past an active
  obstruction or at goal (goalie, defensemen,
  cornerback, hole...)
• AI is trying to pass the ball or puck to human
  player (FOOTBALL 3rd dimension)
• ALL cases AI will apply dead reckoning to
  computer most likely trajectory.
• Determine weather the humans current trajectory
  will take him to an open spot or if another
  player will intercept pass

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Dead Reckoning in Sports and Strategy Games
Dead Reckoning in Sports Games - Soccer
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Dead Reckoning in Sports and Strategy Games
Dead Reckoning in Sports Games - Football
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Dead Reckoning in Sports and Strategy Games
Dead Reckoning in Military Simulation

• Reconnaissance plane over ENEMY FLEET Provides
  Fleets
• 1.) position 2.) velocity 3.) heading
• BOMBING RAID CAN BE PLANNED
• Determine targets of incoming missile attacks so
  can guide anti-missile defenses
• Submarine Simulation
• Floating mines blink in and out of radar contact
• Use DR to determine volumes where mines likely

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Dead Reckoning in Sports and Strategy Games
PREDICT ENEMY MOVEMENT with FOG OF WAR
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Dead Reckoning in Sports and Strategy Games
Dead Reckoning in Online Games

• Use DR to subdue the effects of network latency
  in multiplayer online games

• Each player periodically broadcasts a packet
  containing his location, velocity, and
  acceleration
• During intervals between packets each machine
  uses DR to compute approximate positions of all
  other players
• When new incoming packet is received, the local
  state of the world is updated accordingly

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Dead Reckoning in Sports and Strategy Games
Dead Reckoning other Applications

• CAN INFER AN AGENTS INTENTIONS GOALS

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Building a Sports AI Architecture
8.5
Terry Wellmann
NBA Inside Drive
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Building a Sports AI Architecture
THE GAME OF BASKETBALL

• GAME AI
• Straightforward to solve
• Simulating cohesive group decision making is more
  difficult

• IN THE REAL WORLD TEAMS SPEND
• HOURS PRACTICING TO
• Improve the skills of individual athletes
• Train a group of independently thinking
  individuals how to function as a cohesive unit

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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
Goals to Keep in Mind

• KISS No need to add extra complexity if doesnt
  add to users experience
• Break decisions down to various levels of
  responsibility
• Plan out the architecture
• Dont be afraid to make a mistake
• Dont underestimate the power or randomness
  allows user to observe behaviors that are more
  complex and realistic than they actually are.

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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
INDIVIDUAL AGENT (Player) PLANS

• Identify high level decisions the player will
  make and arrange like decisions together
• Offensive
• Defensive
• Shared Plans

• Pass
• Shoot
• Drive
• Run Play
• Rescue
• Teammate

• Position defender
• Double team ball
• Steal ball
• Steal pass
• Block Shot

• Take a Charge
• Rebound
• Inbound ball
• Free Throw
• ETC.

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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
INDIVIDUAL AGENT (Player) PLANS

• AgentPlan class serves as base class for all
  plans
• class AgentPlan
• ..
• float EvaluateInitiation()
• float EvaluateContinuation()
• void Initiate()
• void Update()

Evaluate how Desirable it is for a plan to
continue being used if it is currently executing
Called every iteration and is responsible for
carrying out plan
Evaluates how Desirable it is for a plan to
execute
Performs 1-Time decision making each time the
plan is initiated
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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
AGENT (Player) PLANS
float EvaluateInitiation() float
EvaluateContinuation() Returns number (-1.0
1.0) for each plan and allows you to build
complex system where plans can be compared Each
plan evaluates the current situation
independently and determines how appropriate it
is to be used Return Large number (gt1.0) if
strongly encouraged
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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
AGENT (Player) PLANS
Break logic into additional update function 1.)
Handle the ball handler decision-making 2.)
Handle the non ball handler decision-making PRIOR
ITY RANK ORDER - if equal evaluation values 1.)
BallHandler_Shoot 2.) BallHandler_Pass 3.)
BallHandler_Drive 4.) BallHandler_RunPlay Dont
want to be on fast break and pull up for
3-Pointer ASSUME drive plan only returns gt0 if
player can drive towards basket (aka. their not
well defended)
( .55 )
( .55 )
( gt 0 )
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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
ADDITIONAL AGENT (PLAYER) AI
Must now make high level decisions in
hierarchical system FOR THE CURRENT
PLAYER POTENTIAL SUCCESS OF SHOT FROM CURRENT
LOCATION - 3 Pointer (guards OK, big men NOT
OK) - BASED ON ATTRIBUTE POINTS TYPE OF
SHOT -guard (layup) -big men (dunk) TELL
PLAYER TO EXECUTE
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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
TEAM MANAGEMENT
Set of COMMON STATES, OFFENSIVE STATES, and
DEFENSIVE STATES with clear TRANSITION
POINTS Each STRATEGY plan evaluates the current
situation independently and determines how
appropriate it is to be used Use FINITE-STATE
Machine for framework of architecture OFFENSIVE
DEFENSIVE STATES complement each
other Inbound Transition Frontcourt Rebound Rec
over Ball Free-throw
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Building a Sports AI Architecture
PLANNING SPORTS AI ARCHITECTURE
TEAM MANAGEMENT
COMMON STATES neutral situation (ball not in
play) Pre-game Substitution Tip-Off Post
Game Time-out Halftime Halftime TRANSITIONS
triggered based on a game event Made Shot A
foul Missed Shot A timeout A
steal Halftime
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Building a Sports AI Architecture
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Building a Sports AI Architecture
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IN CONCLUSION

• Dead Reckoning is an easy way to predict the
  trajectory of objects for the game.
• Dead Reckoning can also be used to predict the
  behavior of a human player.
• Sports present a unique set of challenges to AI
  application.
• When user plays against AI, the game
• must CAPTURE the abilities, personalities,
• and decision making of that player.
• Agent plans, team management, agent AI, and agent
  mechanics can be applied to any sport game.
• AI development is all about good planning and
  trial and error.