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Reactive Behavior Modeling Decision Trees (GATE-561)

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Reactive Behavior Modeling Decision Trees (GATE-561) Dr. a atay NDE ER Instructor Middle East Technical University, GameTechnologies Bilkent University, Computer ... – PowerPoint PPT presentation

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Title: Reactive Behavior Modeling Decision Trees (GATE-561)


1
Reactive Behavior ModelingDecision
Trees(GATE-561)
Dr.Çagatay ÜNDEGER Instructor Middle East
Technical University, GameTechnologies Bilkent
University, Computer Engineering General
Manager SimBT Inc. e-mail cagatay_at_undeger.com
Game Technologies Program Middle East Technical
University Fall 2009
2
Outline
  • Decision Trees

3
Decision Trees
  • Rules are defined as a tree.
  • Conditions are non-leaf nodes.
  • Actions/decisions are leaf nodes.
  • Decision trees can be learned (e.g. ID3).

4
Sample Inputs Outputs
  • Inputs (State variables)
  • Safety (in danger, not safe, safe)
  • See something (yes, no)
  • Theat situation (firing at me, attacking me,
    escaping)
  • Outputs (Actions)
  • Fire at the threat
  • Lay down rapidly
  • Escape from threat
  • Crouch and wait silently
  • Walk around
  • Sleep somewhere

5
Sample Instances For Learning
  • If in danger, see something and threat firing at
    me
  • then Fire at the threat (count 2)
  • If in danger, see something and threat firing at
    me
  • then Lay down rapidly (count 1)
  • If in danger, see something and threat attacking
    me
  • then Escape from threat (count 1)
  • If in danger, see something and threat attacking
    me
  • then Fire at the threat (count 1)
  • If in danger, see something and threat escaping
  • then Fire at the threat (count 2)
  • If not safe and see something
  • then Crouch and wait silently (count 1)
  • If not safe and not see something
  • then Walk around (count 1)
  • If safe
  • then Sleep somewhere (count 1)

6
A Sample Decision Tree
Inputs (state variables)
Outputs (actions)
7
Advantages
  • A simple and compact representation.
  • Easy to create and understand
  • Can also be represented as rules
  • Decision trees can be learned.

8
Disadvantages
  • Decision tree learning algorithm is complex (hard
    to be coded).
  • Need as many examples as possible.
  • Sensitive to the errors in instances,
  • Learned decision trees may contain errors.

9
Manual Decision Trees
  • Weapon
  • WeaponUse Firing
  • Action WeaponUse Ready
  • WeaponUse Pointing
  • GunMunition Filled Almost Empty
  • WeaponRange In Fire Range
  • Query Visibility Not Do Treat See You?
  • WeaponUsageConstraint Dont Fire If Not
    Needed
  • Action WeaponUse Point
  • WeaponUsageConstraint Fire If See Any One
  • Action WeaponUse Fire
  • Query Visibility Do Treat See You?
  • Action WeaponUse Fire
  • WeaponRange Not In Fire Range
  • Action WeaponUse Point
  • GunMunition Empty
  • Action WeaponUse Ready
  • WeaponUse Ready Relax
  • GunMunition Filled
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