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RECAP CSE 348 AI Game Programming

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RECAP CSE 348 AI Game Programming H ctor Mu oz-Avila – PowerPoint PPT presentation

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Title: RECAP CSE 348 AI Game Programming


1
RECAPCSE 348 AI Game Programming
Héctor Muñoz-Avila
2
Course Goal
projects
3
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
4
Controlling the AI Opponent HFSMs
Attack
Wander
E
Chase
Pick-up Powerup
E
S
S
Spawn
Start
Turn Right
D
E
Go-through Door
5
Controlling AI Opponent Scripting
  • Autonomous agents calculate their action based on

(Nick Haynes)
Wargus
(Jon Martin)
Desires
Sensory Input
Proximity to items of interest
1
1
1
Space reservation quasi-coordination
1
1
6
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

7
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)


8
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)
9
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)
10
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 )
11
Spatial Analysis (2)
  • Wall generation
  • Graph representation
  • (tiles, connections)
  • Greedy algorithm

Hierarchy in RTS games
(Rami Khouri)
12
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

13
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
14
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

15
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
16
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

17
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

18
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

19
The End
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