Applying Blackboard Systems to First Person Shooters

Applying Blackboard Systems to First Person Shooters

• 1.
  Applying Blackboard Systemsto First Person Shooters Jeff Orkin Monolith Productions http://www.jorkin.com
• 2.
  No One LivesForever 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.
• 3.
  No One LivesForever 2: A Spy in H.A.R.M.’s Way
• 4.
  Agenda Blackboards arewicked cool. What is a blackboard? Inter -agent coordination Intra -agent coordination
• 5.
  What if therewas an architecture that… …was simple to implement. …was flexible & maintainable. …handled coordinated behavior: Coordinated timing of behaviors. Coordinated pathfinding. Coordinated tactics.
• 6.
  But wait! There’s more! …simplifies agent architecture. …reduces code bloat. …facilitates AI LOD system. …facilitates variations, re-use, and sharing. …allows complex reasoning.
• 7.
  Blackboards: the magicalanimal 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.”
• 8.
  What is a blackboard?
• 9.
  A blackboard isa metaphor Physical blackboard Publicly read/writeable. Possibly organized. Maybe more like a bulletin board Post requests and information. Respond to items of interest.
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  A blackboard isshared memory Read/write memory Working memory Like a hard-drive Like a database No processing (other than sorting)
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  A blackboard isa means of communication Centralized communication Agents communicate Sub-systems of an agent communicate
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  A blackboard isan architecture Changes how agents and/or sub-systems interact Like an interface Reduces coupling of sub-systems
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  Blackboard implementation There’sno wrong way to eat a blackboard. Two flavors: Static Dynamic
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  Static blackboards classCBlackboard { private: Vector m_vPos; Vector m_vVelocity; int m_nHealth; // etc… public: // access functions… };
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  Static blackboards (cont.)Predetermined data to share. Static amount of data. Best for intra -agent coordination.
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  Dynamic blackboards structBBRECORD { … }; typedef std::vector<BBRECORD*> BBRECORD_LIST; class CBlackboard { private: BBRECORD_LIST m_lstBBRecords; public: // query functions… };
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  Dynamic blackboards (cont.)struct BBRECORD { ENUM_BBRECORD_TYPE eType; HANDLE hSubject; HANDLE hTarget; float fData; };
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  Dynamic blackboards (cont.)enum ENUM_BBRECORD_TYPE { kBB_Invalid = -1, kBB_Attacking, kBB_Crouching, kBB_NextDisappearTime, kBB_ReservedVolume, // etc… };
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  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 );
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  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.
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  Inter-agent Coordination Usinga blackboard to solve coordination problems on NOLF2.
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  Inter-agent Coordination ProblemsAgents 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.
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  NOLF2 Blackboard AddRecords: Enumerated type Subject ID Optional Target ID Optional float data Remove Records: Specific by type and Subject ID All by type Replace Records
• 24.
  NOLF2 Blackboard (cont.)Query: Count matching records Retrieve data from matching records
• 25.
  Problem #1: Agentsdoing 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
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  Blackboard Solution: Agentsdoing same thing at same time Should I crouch? if( g_pAIBB->CountRecords( kBB_Crouching ) == 0 ) { // Crouch… g_pAIBB->AddRecord( kBB_Crouching, m_hObject ); }
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  Problem #2: Agentsdoing things too often Examples: Soldiers going Prone Ninja Disappear-Reappear Combat/Search sounds
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  Blackboard Solution: Agentsdoing 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 ); }
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  Problem #3: Tacticalbehavior 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.
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  Blackboard Solution: Tacticalbehavior has special constraints Should I attack from the roof? if( g_pAIBB->CountRecords( kBB_AttackingRoof, m_hTarget ) > 0 ) { return false; }
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  Blackboard Solution: Tacticalbehavior 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 );
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  Problem #4: Agentstake same paths Example: Player runs around the corner, and characters follow in a congo line and get killed one by one.
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  Problem #4: Agentstake same paths (cont.) NOLF2 AIVolume system:
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  Problem #4: Agentstake same paths (cont.) NOLF2 AIVolume system:
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  Problem #4: Agentstake same paths (cont.) NOLF2 AIVolume system:
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  Problem #4: Agentstake same paths (cont.) NOLF2 AIVolume system:
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  Problem #4: Agentstake same paths (cont.) NOLF2 AIVolume system:
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  Blackboard Solution: Agentstake same paths Volume reservation system: Reserve the Volume before the destination. Reserved Volume Cost == Cost + 500
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  Blackboard Solution: Agentstake same paths (cont.) Volume reservation system:
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  Blackboard Solution: Agentstake same paths (cont.) Volume reservation system:
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  Blackboard Solution: Agentstake same paths (cont.) Volume reservation system:
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  Blackboard Solution: Agentstake same paths (cont.) // Pathfinding if( g_pAIBB->CountRecords( kBB_ReservedVolume, hVolume ) > 0 ) { fNodeCost += 500.f; }
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  Blackboard Solution: Agentstake same paths (cont.) // Movement g_pAIBB->RemoveRecord( kBB_ReservedVolume, m_hObject ); g_pAIBB->AddRecord( kBB_ReservedVolume, m_hObject, hVolume );
• 44.
  Problem #5: Agentscrowd at destination Examples: Player knocks over a bottle. Characters converge on bottle position. Characters discover dead body and converge.
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  Blackboard Solution: Agentscrowd at destination First agent claims volume for investigation. Other agents stop at edge of volume.
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  Blackboard Solution: Agentscrowd at destination (cont.)
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  Blackboard Solution: Agentscrowd at destination (cont.)
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  Blackboard Solution: Agentscrowd at destination (cont.)
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  Blackboard Solution: Agentscrowd 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. }
• 50.
  Einstein says… “Hang in there, we’re half-way done!”
• 51.
  Why use blackboards??
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  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)
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  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.
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  What about performance?!Problem: Pathfinder needs to look up Volume Reservation status every iteration thru A*.
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  What about performance?!(cont.) Solution: A* flags array char astarFlags[NUM_VOLUMES];   enum ASTAR_FLAGS { kNone = 0x00, kOpen = 0x01, kClosed = 0x02, };
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  What about performance?!(cont.) Solution: A* flags array char astarFlags[NUM_VOLUMES];   enum ASTAR_FLAGS { kNone = 0x00, kOpen = 0x01, kClosed = 0x02, kReserved = 0x04, };
• 57.
  What about performance?!(cont.) RunAStar() { ClearFlags(); Search(); }  
• 58.
  What about performance?!(cont.) RunAStar() { ClearFlags(); MarkReserved(); Search(); }  
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  Intra-agent Coordination
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  Intra-agent Coordination Acharacter is an entire world. Sub-systems are characters in the world. Navigation Movement Target/Attention selection Senses Animation Weapons Decision-Making
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  NOLF2 Agent Architecture
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  NOLF2 Agent Architecture
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  Blackboard Agent Architecture
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  Blackboard Agent Architectureclass AgentBlackBoard { private: Vector m_vDest; NAV_STATUS m_eNavStatus; HANDLE m_hTarget; AISenses m_aSenses[MAX_SENSES]; // etc… public: // Access functions…  }
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  Benefits of Decoupling Sub-systems Benefits of Decoupling: Development/Maintenance Flexibility Performance
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  Benefit #1: Development/MaintenanceBenefits Problem: Difficult to upgrade or replace old systems.   Example: Re-writing navigation system
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  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);
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  Benefit #2: FlexibilityProblem: Different characters have different needs.   Example: Humans plan paths to a dest. Rats and Rabbits wander randomly to a dest.
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  Benefit #2: Flexibility(cont.) Example (cont.): AIStatePatrol::Update( AI* pAI ) { pAI->GetPathManager()->SetPath(vDest); if( pAI->GetPathManager()->IsPathComplete() ) { // etc… }
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  Benefit #2: Flexibility(cont.) Blackboard Solution: AIStatePatrol::Update( AI* pAI ) { pAI->GetAIBlackboard()->SetDest(vDest); if( pAI->GetAIBlackboard()->GetNavStatus() == kNavStatus_Done ) { // etc… }
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  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.
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  Benefit #2: Flexibility(cont.)
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  Benefit #2: Flexibility(cont.)
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  Benefit #2: Flexibility(cont.)
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  Benefit #3: PerformanceProblem: 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.
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  Benefit #3: Performance(cont.) Blackboard Solution: Sub-systems communicate through the blackboard. LOD system swaps sub-systems behind the scenes.
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  Benefit #3: Performance(cont.) LOD 5:
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  Benefit #3: Performance(cont.) LOD 5:
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  Benefit #3: Performance(cont.) LOD 5:
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  Benefit #3: Performance(cont.) LOD 2:
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  Benefit #3: Performance(cont.) LOD 2:
• 82.
  Don’t run away…We’re almost done!
• 83.
  Intra-agent Dynamic BlackboardMIT Media Lab Synthetic Characters Group C4 GDC 2001 Creature Smarts: The Art and Architecture of the Virtual Brain
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  Intra-agent Dynamic Blackboard(cont.)
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  Intra-agent Dynamic Blackboard(cont.)
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  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.
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  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.
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  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.”
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  Intra-agent Dynamic Blackboard:Complex Reasoning (cont.) Spatial Reasoning: Agent is more alarmed if multiple disturbances are found near each other.
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  Intra-agent Dynamic Blackboard:Complex Reasoning (cont.) Temporal Reasoning: Anticipation and surprise.
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  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.
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  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.
• 93.
  Take-away Use blackboardsystems! Less is more. Decouple your data from its game-specific purpose. Decouple your subsystems.
• 94.
  More Information December2003: AI Game Programming Wisdom 2 “ Simple Techniques for Coordinated Behavior”
• 95.
  More Information NOLF2Source, Toolkit & SDK: http://nolf2.sierra.com ( AICentralKnowledgeMgr == Blackboard ) Slides: http://www.jorkin.com/talks/UT_blackboards.zip [email_address]
• 96.
  Questions? NOLF2 Source,Toolkit & SDK: http://nolf2.sierra.com ( AICentralKnowledgeMgr == Blackboard ) Slides: http://www.jorkin.com