application of ai techniques for computer games bsc computer games programming, 2006 julien...

Application of AI techniques for Computer Games

BSc Computer Games Programming, 2006

Julien Delezenne

GAMES ARTIFICIAL INTELLIGENCE

Overview of the demo

In-game editor

Pathfinding demo

Design

• The AI component is part of the game engine

• Contains a generic pathfinding system

• Object-oriented state machines

• Data-driven with the reflection system

AI and architecture

• AI can be divided in two parts– The game object system– The AI techniques

• The object system becomes very complex in commercial games

• AI techniques need to be integrated in a flexible way

Game system

-CellSize-Divisions

Map

-Position-Depth

Cell

-Type-FileName

Tile

-Type-FileName

ActorDef

-Position-Orientation

Actor

Placeable

-Next

WayPointSpawn Goal

-Name-Design

GameEntity

-TypeInfo

Object

Tileset

-Editor

World1

11

*

1

*

* 1

*1

-WalkSpeed-RunSpeed-ViewConeAngle-ViewDistance

Character

1

1

*

-Player

1

Reflection

• For each game entity:– Instantiation from its name– Exposition of its properties– (De)Serialization including with files such as

XML (used in the demos)– Editable attributes in the editor. The property

grid of the editor is generated automatically

Abstract controller

• Two types of controllers– Player– AI

• Only the input is different

• The game logic should only know about the abstract controller

AI techniques

• Game developers always have:– A* for pathfinding– FSM for decision-making

• They are the two techniques of the stealth demo in a game environnment

Pathfinding

F.E.A.R.

• Environnment represented as a graph

• A* is independent of the environnment

C&C Generals

Pathfinding

• A* is divided in three modules– Map– Goal– Storage

• The client application implement these objects

• The A* algorithm will send request to them

Pathfinding

-Goal-Heuristic-Fitness

AStarPathfinderNode

-ID

PathfinderNode

+GetNeighbourCount()+GetNeighbour()

PathfinderMap

+Run()

-SourceNode-DestinationNode-CurrentNode

Pathfinder

AStarPathfinderMap

+GetHeuristic()+GetCost()

-DestinationNode

AStarPathfinderGoal

+IsNodeValid()+IsSearchFinished()

PathfinderGoal

AStarPathfinder

+CreateNode()+DestroyNode()+Reset()+AddToOpenList()+AddToClosedList()+RemoveFromOpenList()+RemoveFromClosedList()+FindInOpenList()+FindInClosedList()+RemoveBestOpenNode()

AStarPathfinderStorage

1

1

1

1

11

-Parent

1

*

CustomPathfinderGoal

CustomPathfinderMap

CustomPathfinderStorage

State machine

• Object-oriented

• States are independent of each others

• State changes are requested

• In the demo, the state machine is mostly driven by the the field of view of the characters

State machine

Idle

Follow

CombatGoto

TargetLost

SearchGuard Patrol

In FOV

FOV = Player in the field of view

In FOVIn

FOV

In FOV

Timer

Collision

Has no waypoint

Has a waypoint

Not in FOV

At destination

Combat ended

Results

• Actual game demo achieved using the two most common AI tehcniques: A* and FSM in a game scenario

Conclusion

• Pros– Objectives achieved– Overall methodology– Reflection system

• Cons– Data-driven system not complete– Some performance issues– Need different behaviours– Need an event-system

Future enhancements

• Scripting

• More behaviours

• Agents’ memory

• Dynamic pathfinding

• Navigational meshes

• More advanced Toolsets

• …