1. RECAP CSE 397/497 Topics on AI and Computer Game Programming
Prerequisites:
CSE 327 or CSE 340 or instructor consent
Instructor consent will be immediately granted for:
CSE Graduate Students
CS/CSE/CSB undergraduates that will be senior on the Fall/2005
All other cases will be granted depending on the particular case
Héctor Muñoz-Avila

2. Course Goal AI
research A C B
Our goal is to understand the connections and the misconceptions from both sides
“AI”
as game practitioners implemented it

3. Path-Finding A*: minimize f(n) = g(n) + h(n) (Moll)
Search Representations
Grid
Graphs
Meshes
D*: dynamic A*
(Hoang)
Path look-up matrix
Indexed path look-up matrix
Area-based look-up table
Precomputed Pathfinding

4. Navigation (Mansley) (Lindner) Qualitative Spatial Analysis
Navigation data can be used beyond navigation
Low level navigation
Jumping, climbing
Hunting the player
(Mansley)
Qualitative Spatial Analysis
Spatial databases
Terrain analysis
Search layer
Occupancy layer
Openness layer 1 2 3 4 5 6 7 8 9

5. Real-Time Strategy games
(Schmid)
Multi-tier AI
Maps and AI Control
Wall Building
(Lee-Urban)
Random map generation
AI transport Units
HTN planning T

6. Racing Games & Sports (Misra) (Gundevia) Contention system
Steering
Throttle
Braking
Brooks subsumption architecture
(Gundevia)
FSM implementation of offensive and defensive states (mirror)
Dead reckoning

7. Controlling The “AI” Opponent (1)
(Raim)
FSM: States, Events and Actions
Stack Based FSM’s
Polymorphic FSM
Multi-tier FSM
(Hogg)
Data-driven FSM
Goal: separate program control logic from FSM logic
Scripted FSM
Spawn D
Wander
~E,~S,~D ~E
Attack
E,~D E ~S
Chase
S,~E,~D S
Robocode
… ( Technologies )
Advanced Flight, 4,-2, Rad, Too, 3, 4 ;
Alphabet, , 1, nil, nil, 0, 3 ;
… ( Personalities / Goals )
Caesar, Livia, 0, 1, 1, Romans, Roman, 0, 1, 1
Montezuma, Nazca, 0, 4, 0, Aztecs, Aztec, 0,-1, 1
Soldier
Rifleman
Officer
British
Soviet
American
German
Machine Gunner
HTN planning

8. Controlling The “AI” Opponent (2)
(Hookway)
Ideal AI Behavior
Coordinating behavior
Blackboard:
Deal with obstruction
Synthetic Adversaries
Competence
Taskability
Observational fidelity
Behavior variability
(Grabowski )
Movement, fire coordination
Hierarchy of plan tactics
Finding an Available NPC
Availability = (1+N)(1+O)(1+P)+(Q∞) E A B 1 50
HTN planning
N = # of enemies in covering area
O = # of enemies within range
P = # of enemies threatening team

9. Controlling The “AI” Opponent (3)
(Grabowski )
Goal-Oriented Action Planning
Alternative to FSM
Define actions
Inter-relations are found dynamically (planning)
Various speed-up strategies are used
(Xu)
HTN: Plan on level of tasks not actions
HTNs can be used to encode game strategies
Multi-Tier AI
Explode the target
Plant the
bomb
Get a better
weapon
Secure the
plant site
Approach to
the plant site
Guard the
Task
A resulting plan:
Patrol
patrolled
Fight
No Monster
Monster in sight SI OI TI IU
Wargus
AI planning

10. Controlling the “AI” opponent (4)
(Warfield)
Several advantages of using scripts
Modularity
Entice players
Main drawback: developing time for the scripting system
Levels of scripting
Hardcoded (typical console game) …
Full modularity (Neverwinter nights)
Languages
Declarative
Imperative

11. Adaptive “AI” (Janneck) (Creswell)
Player modeling
Simple model
Model = {(attribute, value),…)}
Hierarchical model
Higher-level node value combination of children’s values
Abstract node combination of concrete traits
Issues
Model complexity-time tradeoff
Decouple model from game
(Creswell)
Decision trees are a simple representation form
Decision trees can be learned automatically (ID3)
One of the landmarks applications of Machine learning to Games

12. Adaptive “AI” (2) (Ponsen) (Chan) Limitations of machine learning
Reinforcement learning to find “right” script
But sometimes the problem resides in the scripts not the ordering
Use evolutionary computation to improve scripts
(Chan)
Limitations of machine learning
Information stored
Pattern recognition
During development time
Terrain analysis
Pattern recognition as optimization
Pattern recognition as adaptation
Dynamic environments
Combat
team controlled
by human player b
y computer A B
Evolve a population (each member is a candidate solution) …

13. Hall of Fame

14. Acknowledgements Jarret Raim: 5 programming projects
Marc Ponsen: last programming project
All of you:
Presentations were very good