applying blackboard systems to first person shooters

Applying Blackboard Systems to First Person Shooters

Jeff Orkin

Monolith Productions

http://www.jorkin.com

No One Lives Forever 2:A Spy in H.A.R.M.’s Way

aka NOLF2

A.I. Systems

re-used:– TRON 2.0– Contract J.A.C.K.

No One Lives Forever 2:A Spy in H.A.R.M.’s Way

Agenda

Blackboards are wicked cool. What is a blackboard? Inter-agent coordination Intra-agent coordination

What if there was an architecture that…

…was simple to implement. …was flexible & maintainable. …handled coordinated behavior:

– Coordinated timing of behaviors.– Coordinated pathfinding.– Coordinated tactics.

But wait! There’s more!

…simplifies agent architecture. …reduces code bloat. …facilitates AI LOD system. …facilitates variations, re-use, and sharing. …allows complex reasoning.

Blackboards: the magical animal

Homer: “What about bacon?”

Lisa: “No!”

Homer: “Ham?”

Lisa: “No!”

Homer: “Pork chops?!?”

Lisa: “Dad! Those all come from the same animal!”

Homer: “Yeah right Lisa. A wonderful magical animal.”

What is a blackboard?

A blackboard is a metaphor

Physical blackboard– Publicly read/writeable.– Possibly organized.

Maybe more like a bulletin board– Post requests and information.– Respond to items of interest.

A blackboard is shared memory

Read/write memory Working memory Like a hard-drive Like a database No processing (other than sorting)

A blackboard is a means of communication

Centralized communication Agents communicate Sub-systems of an agent communicate

A blackboard is an architecture

Changes how agents and/or sub-systems interact

Like an interface Reduces coupling of sub-systems

Blackboard implementation

There’s no wrong way to eat a blackboard.

Two flavors:– Static– Dynamic

Static blackboards

class CBlackboard{ private: Vector m_vPos; Vector m_vVelocity; int m_nHealth; // etc…

public: // access functions…};

Static blackboards (cont.)

Predetermined data to share. Static amount of data. Best for intra-agent coordination.

Dynamic blackboards

struct BBRECORD { … };typedef std::vector<BBRECORD*> BBRECORD_LIST;

class CBlackboard{ private: BBRECORD_LIST m_lstBBRecords;

public: // query functions…};

Dynamic blackboards (cont.)

struct BBRECORD

{

ENUM_BBRECORD_TYPE eType;

HANDLE hSubject;

HANDLE hTarget;

float fData;

};

Dynamic blackboards (cont.)

enum ENUM_BBRECORD_TYPE

{

kBB_Invalid = -1,

kBB_Attacking,

kBB_Crouching,

kBB_NextDisappearTime,

kBB_ReservedVolume,

// etc…

};

Dynamic blackboards (cont.)

// query functions

int CountRecords( ENUM_BBRECORD_TYPE eType );

int CountRecords( ENUM_BBRECORD_TYPE eType, HANDLE hTarget );

float GetRecordData( ENUM_BBRECORD_TYPE eType );

float GetRecordData( ENUM_BBRECORD_TYPE eType, HANDLE hTarget );

Dynamic blackboards (cont.)

Data to share is not predetermined. Dynamic amount of data. Best for inter-agent coordination. Also useful for intra-agent complex reasoning.

Inter-agent Coordination

Using a blackboard to solve coordination

problems on NOLF2.

Inter-agent Coordination Problems

1. Agents doing the same thing at the same time.

2. Agents doing things too often.

3. Special constraints for tactics.

4. Agents take same paths.

5. Agents clump at destinations.

NOLF2 Blackboard

Add Records:– Enumerated type– Subject ID– Optional Target ID– Optional float data

Remove Records:– Specific by type and Subject ID– All by type

Replace Records

NOLF2 Blackboard (cont.)

Query:– Count matching records– Retrieve data from matching records

Problem #1: Agents doing same thing at same time

Examples: Soldiers Crouching

– Random chance of crouch

– Dodge roll into crouch– Crouch to get out of

firing line

Ninja Lunging

Blackboard Solution: Agents doing same thing at same time

Should I crouch?

if( g_pAIBB->CountRecords( kBB_Crouching ) == 0 )

{

// Crouch…

g_pAIBB->AddRecord( kBB_Crouching, m_hObject );

}

Problem #2: Agents doing things too often

Examples: Soldiers going Prone Ninja Disappear-Reappear Combat/Search sounds

Blackboard Solution: Agents doing things too often

Should I go prone?

if( fCurTime > g_pAIBB->GetRecordFloat( kBB_NextProneTime ) )

{// Go prone…

g_pAIBB->ReplaceRecord( kBB_NextProneTime, m_hObject, fCurTime + fDelay );

}

Problem #3: Tactical behavior has special constraints

Example: Ninja only attacks from a rooftop if two other ninja are

already attacking on ground, and no one is on a roof.

Blackboard Solution: Tactical behavior has special constraints

Should I attack from the roof?

if( g_pAIBB->CountRecords( kBB_AttackingRoof, m_hTarget ) > 0 )

{

return false;

}

Blackboard Solution: Tactical behavior has special constraints (cont.)

if( g_pAIBB->CountRecords( kBB_Attacking, m_hTarget ) < 2 )

{return false;

}

// Attack from the roof…

g_pAIBB->AddRecord( kBB_Attacking, m_hObject, m_hTarget );

g_pAIBB->AddRecord( kBB_AttackingRoof, m_hObject, m_hTarget );

Problem #4: Agents take same paths

Example: Player runs around the corner, and characters

follow in a congo line and get killed one by one.

Problem #4: Agents take same paths (cont.)

NOLF2 AIVolume system:

Problem #4: Agents take same paths (cont.)

NOLF2 AIVolume system:

Problem #4: Agents take same paths (cont.)

NOLF2 AIVolume system:

P

AB

Problem #4: Agents take same paths (cont.)

NOLF2 AIVolume system:

P

AB

Problem #4: Agents take same paths (cont.)

NOLF2 AIVolume system:

P

AB

Blackboard Solution: Agents take same paths

Volume reservation system: Reserve the Volume before the destination. Reserved Volume Cost == Cost + 500

Blackboard Solution: Agents take same paths (cont.)

Volume reservation system:

P

AB6

1 1

1

1

1

1

1

1 1

Blackboard Solution: Agents take same paths (cont.)

Volume reservation system:

P

AB6

1 1

1

1

1

1

1

1 1

P

AB6

1 1

1

1

1

1

1

1 1

Blackboard Solution: Agents take same paths (cont.)

Volume reservation system:

P

AB6

1 1

1

1

1

1

1

1 1

P

AB6

1 501

1

1

1

1

1

1 1

Reserved!

Blackboard Solution: Agents take same paths (cont.)

// Pathfinding

if( g_pAIBB->CountRecords( kBB_ReservedVolume, hVolume ) > 0 )

{

fNodeCost += 500.f;

}

Blackboard Solution: Agents take same paths (cont.)

// Movement

g_pAIBB->RemoveRecord( kBB_ReservedVolume,

m_hObject );

g_pAIBB->AddRecord( kBB_ReservedVolume,

m_hObject,

hVolume );

Problem #5: Agents crowd at destination

Examples: Player knocks over a bottle. Characters

converge on bottle position. Characters discover dead body and converge.

Blackboard Solution: Agents crowd at destination

First agent claims volume for investigation. Other agents stop at edge of volume.

Blackboard Solution: Agents crowd at destination (cont.)

A

B

D

Blackboard Solution: Agents crowd at destination (cont.)

A

B

D

A

B

D

Blackboard Solution: Agents crowd at destination (cont.)

A

B

D

A

B

D

Blackboard Solution: Agents crowd at destination (cont.)

// AI reached the dest volume first.

if( g_pAIBB->CountRecords( kBB_InvestigatingVolume, hVolume ) == 0 )

{g_pAIBB->AddRecord( kBB_InvestigatingVolume,

m_hObject );}

// AI did not reach the dest volume first.

else { // Look at dest. }

Einstein says…

“Hang in there, we’re half-way done!”

Why use blackboards??

Why use blackboards??

“Less is more”: Less to debug Less to maintain Less to port Less to compile Less to document Less to learn Less data (per volume)

Why use blackboards??

Decouple data from game-specific purpose: Designs change Re-use systems in other games (other

genres?) OO design is not always the right choice.

What about performance?!

Problem: Pathfinder needs to look up Volume

Reservation status every iteration thru A*.

What about performance?! (cont.)

Solution:A* flags arraychar astarFlags[NUM_VOLUMES]; enum ASTAR_FLAGS{

kNone = 0x00,kOpen = 0x01,kClosed = 0x02,

};

What about performance?! (cont.)

Solution:A* flags arraychar astarFlags[NUM_VOLUMES]; enum ASTAR_FLAGS{

kNone = 0x00,kOpen = 0x01,kClosed = 0x02,kReserved = 0x04,

};

What about performance?! (cont.)

RunAStar(){ClearFlags();Search();

}

What about performance?! (cont.)

RunAStar(){ClearFlags();MarkReserved();Search();

} 

Intra-agent Coordination

Intra-agent Coordination

A character is an entire world. Sub-systems are characters in the world.

– Navigation– Movement– Target/Attention selection– Senses– Animation– Weapons– Decision-Making

NOLF2 Agent Architecture

Animation

Navigation

Movement

Sensory

Target Selection

W eapons

Decision-Making

NOLF2 Agent Architecture

Animation

Navigation

Movement

Sensory

Target Selection

W eapons

Decision-MakingAnimation

Navigation

Movement

Sensory

Target Selection

W eapons

Decision-Making

Blackboard Agent Architecture

Animation

Navigation

Movement

Sensory

Target Selection

W eapons

Decision-MakingAnimation

Navigation

Movement

Sensory

Target Selection

W eapons

Decision-Making

Blackboard

Blackboard Agent Architecture

class AgentBlackBoard{

private:Vector m_vDest;NAV_STATUS m_eNavStatus;HANDLE m_hTarget;AISenses m_aSenses[MAX_SENSES];// etc…

public:// Access functions… 

}

Benefits of Decoupling Sub-systems

Benefits of Decoupling:

1. Development/Maintenance

2. Flexibility

3. Performance

Benefit #1: Development/Maintenance Benefits

Problem: Difficult to upgrade or replace old systems.

Example: Re-writing navigation system

Benefit #1: Development/Maintenance (cont.)

Various calls to sub-system:

pAI->GetPathManager()->SetPath(vDest);

pAI->GetPathManager()->UpdatePath();

if( pAI->GetPathManager()->IsPathDone() )

...

AIVolume* GetNextVolume(AIVolume* pVolume, AIVolume::EnumVolumeType

eVolumeType);

Benefit #2: Flexibility

Problem: Different characters have

different needs.

Example: Humans plan paths to a

dest. Rats and Rabbits wander

randomly to a dest.

Benefit #2: Flexibility (cont.)

Example (cont.):

AIStatePatrol::Update( AI* pAI ){

pAI->GetPathManager()->SetPath(vDest);

if( pAI->GetPathManager()->IsPathComplete() ){

// etc…}

Benefit #2: Flexibility (cont.)

Blackboard Solution:

AIStatePatrol::Update( AI* pAI ){

pAI->GetAIBlackboard()->SetDest(vDest);

if( pAI->GetAIBlackboard()->GetNavStatus() == kNavStatus_Done )

{// etc…

}

Benefit #2: Flexibility (cont.)

Example: Humans need a lot of sensory information to

make complex goal-based decisions. Rats and Rabbits need very little info for

simplistic behavior.

Benefit #2: Flexibility (cont.)

Benefit #2: Flexibility (cont.)

Friend?

Benefit #2: Flexibility (cont.)

Benefit #3: Performance

Problem: All characters in NOLF2 are

active all of the time, regardless of player location.

Example: Characters are pathfinding,

moving, animating, and sensing as they work at desks, go to the bathroom, etc.

Benefit #3: Performance (cont.)

Blackboard Solution: Sub-systems communicate through the

blackboard. LOD system swaps sub-systems behind the

scenes.

Benefit #3: Performance (cont.)

LOD 5:

Benefit #3: Performance (cont.)

LOD 5:

Benefit #3: Performance (cont.)

LOD 5:

Benefit #3: Performance (cont.)

LOD 2:

Benefit #3: Performance (cont.)

LOD 2:

Don’t run away…

We’re almost done!

Intra-agent Dynamic Blackboard

MIT Media Lab

Synthetic Characters Group

C4

GDC 2001

Creature Smarts: The Art and Architecture of the

Virtual Brain

Intra-agent Dynamic Blackboard (cont.)

Intra-agent Dynamic Blackboard (cont.)

Intra-agent Dynamic Blackboard (cont.)

Percept Memory Records: Only form of knowledge representation.

– Game objects (characters, objects of interest, etc)

– Desires– Damage– AI hints (AINodes, AIVolumes)– Tasks

Can group multiple records for same object.

Intra-agent Dynamic Blackboard (cont.)

Benefits: Keep track of multiple types of information in a

consistent way. Open-ended architecture: different games may

use different data in different ways. Complex reasoning.

Intra-agent Dynamic Blackboard:Complex Reasoning

Queries: “Is there food near me?” Find the “red object that is making the most

noise.” “Find an object that is humanoid-shaped and

go to it.”

Intra-agent Dynamic Blackboard:Complex Reasoning (cont.)

Spatial Reasoning: Agent is more alarmed if multiple disturbances

are found near each other.

Intra-agent Dynamic Blackboard:Complex Reasoning (cont.)

Temporal Reasoning: Anticipation and surprise.

Intra-agent Dynamic Blackboard:Complex Reasoning (cont.)

Deductive Reasoning: Agent sees dead body. Agent sees player with a gun. Agent draws the conclusion that the player was

the killer.

Intra-agent Dynamic Blackboard:Complex Reasoning (cont.)

Multi-tasking: Agent targets enemyA. Agent targets enemyB. Agent kills enemyB. Agent is aware that he

was also fighting enemyA.

Take-away

Use blackboard systems!– Less is more.– Decouple your data from its game-specific purpose.– Decouple your subsystems.

More Information

December 2003:

AI Game Programming Wisdom 2“Simple Techniques for Coordinated Behavior”

More Information

NOLF2 Source, Toolkit & SDK:

http://nolf2.sierra.com

( AICentralKnowledgeMgr == Blackboard )

Slides:

http://www.jorkin.com/talks/UT_blackboards.zip

jeffo@lith.com

Questions?

NOLF2 Source, Toolkit & SDK:

http://nolf2.sierra.com

( AICentralKnowledgeMgr == Blackboard )

Slides:

http://www.jorkin.com