Applying Blackboard Systems to First Person Shooters
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Applying Blackboard Systems to First Person Shooters Applying Blackboard Systems to First Person Shooters Presentation Transcript

  • 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
    • Agents doing the same thing at the same time.
    • Agents doing things too often.
    • Special constraints for tactics.
    • Agents take same paths.
    • 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:
  • Problem #4: Agents take same paths (cont.)
    • NOLF2 AIVolume system:
  • Problem #4: Agents take same paths (cont.)
    • NOLF2 AIVolume system:
  • 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:
  • Blackboard Solution: Agents take same paths (cont.)
    • Volume reservation system:
  • Blackboard Solution: Agents take same paths (cont.)
    • Volume reservation system:
  • 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.)
  • Blackboard Solution: Agents crowd at destination (cont.)
  • Blackboard Solution: Agents crowd at destination (cont.)
  • 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 array
    • char astarFlags[NUM_VOLUMES];
    •  
    • enum ASTAR_FLAGS
    • {
    • kNone = 0x00,
    • kOpen = 0x01,
    • kClosed = 0x02,
    • };
  • What about performance?! (cont.)
    • Solution:
    • A* flags array
    • char 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
  • NOLF2 Agent Architecture
  • Blackboard Agent Architecture
  • 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:
    • Development/Maintenance
    • Flexibility
    • 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.)
  • 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
    • [email_address]
  • Questions?
    • NOLF2 Source, Toolkit & SDK:
    • http://nolf2.sierra.com
    • ( AICentralKnowledgeMgr == Blackboard )
    • Slides:
    • http://www.jorkin.com