RECAP CSE 348 AI Game Programming

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RECAPCSE 348 AI Game Programming
Héctor Muñoz-Avila
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Course Goal
projects
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Controlling the AI Opponent FSMs

• FSM States, Events and Actions
• Stack Based FSMs
• Polymorphic FSM
• Multi-tier FSM

FSM
Monster In Sight
Robocode
Patrol
Fight
No Monster
A resulting plan
Monster in sight
No Monster
patrolled
Patrol
Fight
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Controlling the AI Opponent HFSMs
Attack
Wander
E
Chase
Pick-up Powerup
E
S
S
Spawn
Start
Turn Right
D
E
Go-through Door
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Controlling AI Opponent Scripting

• Autonomous agents calculate their action based on

(Nick Haynes)
Wargus
(Jon Martin)
Desires
Sensory Input
Proximity to items of interest
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1
1
Space reservation quasi-coordination
1
1
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Controlling AI Opponent Team
Unreal tournament
(Eric Lease)

• (Dayne Mickelson)
• Team sports
• Identify high-level decisions

• Multi-layered approach
• Line of sight (player, npcs)
• GOAP
• Agent can dynamically find alternate solutions to
  problems

• Dead Reckoning
• Predicting future state
• For games Newton physics
• Estimate future trajectory Kinematics

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Learning Adaptive Behavior

• Dynamic scripting Reinforcement learning
• But sometimes the problem resides in the scripts
  not the ordering
• Use evolutionary computation to improve scripts

(Megan Vasta)
Neural networks

• Evolve a population (each member is a candidate
  solution)

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Learning Adaptive Behavior (2)
Allegiance
(Jeff Storey)

• User model
• Flexibility beyond predefined difficulty levels
• When/what to update

Friendly
Enemy
Defense
-1.0
Weak
Strong
Medium
0.4
-0.3
0.1

• Induced from a collection of data
• Based on information gain formulas
• Assume discrete values

(Brigette Swan)
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Learning Adaptive Behavior (3)

• Pattern recognition
• Symbols
• Optimization balancing units in an RTS game
• 2. Curse of dimensionalit
• Analysis of Machine learning Usage
• 1. Cheap to recognize what to learn from?
• 2. Cheap to store the knowledge?
• 3. Cheap to use the knowledge?
• 4. Does game benefit from learning?

(Chris Kramer)
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Spatial Analysis

• Random map generator
• Location of players
• Map is generated step-wise by adding clumps

• Terrain analysis
• Concepts borders, corridors
• Selection of new colonies
• Spatial Analysis

Transport units in RTS games
(Russell Kuchar)
(Jay Shipper )
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Spatial Analysis (2)

• Wall generation
• Graph representation
• (tiles, connections)
• Greedy algorithm

Hierarchy in RTS games
(Rami Khouri)
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Path-Finding (1)
A minimize f(n) g(n) h(n)

• Grid
• Graphs
• Meshes

(Dan Bader) Rep. simplicity versus optimality
- Can be used to compute AI
(Tom Gianos) Navigation set hierarchy
String pulling

• Interface tables
• Reduction memory
• Increase performance

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Path-Finding (2)
(Owen Cummings)
(Tom Schaible)

• Path Look-Up tables
• Several times faster than A
• But memory consumption is high
• Solution Area-based Look-up tables
• Notion of portals
• Very fast

• Throwing a grenade is not so simple!
• Add information to nodes
• Add behavior info in edges

Flying Edge
Rappelling Edge
Flying Edge
Door Edge
Vault Edge
Jump Edge
Hunting players in a convincing manner
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Path-Finding (3)

• (Adam Balgach)
• Racing vehicle control
• Multi-layer system
• Each layer defines behavior
• Optimal racing line
• Use of Newton physics

• (Don DeLorenzo)
• Avoiding obstacles
• Should be smooth
• Crucial in dynamic worlds

a
Ra
Da
Va
Obstacle
Sidestep Repulsion

• Intelligent Steering
• Use error correction
• current error history error rate error

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Game theory
Declarative Knowledge
Spectimax kind of search
Initial state
Goals
A
C
B
A
B
C

• HTN approach for declarer play
• Use HTN planning to generate a game tree in which
  each move corresponds to a different strategy,
  not a different card
• Reduces average game-tree size to about 26,000
  leaf nodes

• Compute expectimax and expectimin
• Evaluation functions
• Pruning search space

poker
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Game Design

• (Peter Shankar)
• Meaningful play
• Outcome is discernable and integrated
• Elements for meaningful play
• Semiotics
• Systems
• Interactivity
• Choice

Cultural System
Experiential System
Formal System

• Sid Mier says
• personal touch is needed

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Hall of Fame

• Winners Project 1
• Tournament Adam Balgach, Tom Gianos. Bot
  Yankees
• Innovation Tom Shaible, Don Delorenzo. For
  "meta-level" FSM design of code.
• Winners Project 2
• Tournament Adam Balgach, Tom Gianos. Team
  Yankees (continuing champions!)
• Innovation Swan, Brigette L, Vasta, Megan E.,
  and Khouri, Rami H. For a number of interesting
  ideas predicting next place for firing,
  distributing battlefield, training examples.
• Winners Project 3
• Project 3 was no tournament.
• Winners Project 4
• Tournament Adam Balgach, Tom Gianos. Team
  Yankees (unbeatable!)
• Winners Project 5
• Tournament Tom Schaible, Don Delorenzo. Team
  DDTS (new champions!)
• Winners Project 6
• Tournament. Owen Cummings, Dayne Mickelson Team
  Tony Wonder (new champions!)
• Innovation Tom Shaible, Don Delorenzo. For
  decision trees and reinforcement learning

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Acknowledgements

• Jon Martin and Eric Lease
• All of you
• Presentations were very good
• Projects were worked well (despite difficulties)
• Changes
• 4 projects robocode, UT, MadRTS, poker
• UT 2 bots only
• Poker use downloadable version

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The End