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From the Ground Up: A.I. Architecture and Design Patterns Dr Brett Laming Rockstar Leeds.

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Presentation on theme: "From the Ground Up: A.I. Architecture and Design Patterns Dr Brett Laming Rockstar Leeds."— Presentation transcript:

1 From the Ground Up: A.I. Architecture and Design Patterns Dr Brett Laming Rockstar Leeds

2 Contents Basic Principles Think/Act - The I/O Divide An Algorithmic Approach A Functional Approach Movement Animation Conclusion

3 Basic Principles

4 Why Do We Care? It’s a Competitive Market… –Higher Expectations –Tighter Schedules –Multiple Platforms –Simultaneous Titles …But Patterns Are Everywhere –Identify, generalise, reuse, evolve –  Reliability –  Production Speed  More Fun Stuff

5 Inspirations Marvin Minsky Different representations for different views Noam Chomsky Hierarchical decomposition Structure vs Meaning “Colorless green ideas sleep furiously” Daniel Dennet Behaviour can be viewed at the physical, design and intentional levels David Marr Computational, algorithmic and implementational Chris Hecker Style vs Structure [2008] What is the textured triangle of A.I. Craig Reynolds Simple rules Complex behaviour Damian Isla Cognitive Maps Spatial Relations Semantics

6 What Am I Looking For? Algorithms Best Practice Hierarchy Reusability Commutability Concepts Components

7 How Do I Find Them? Observation –How might it work? Introspection –What would I do? Generalisation –I’ve done this before –They do the same Bad Experience –Lets not do that again Background –I studied this once? –Could I apply this?

8 Best Practice

9 Prototype new ideas where possible –Get visual and design direction Mock-ups –Prove (or disprove) the concept Quick and dirty programming Play to peoples strengths –Maths  Physics Guys –Navigation Mesh  Collision Guys

10 Best Practice Program Defensively –Assert and Unit Test –Automated Scripts as soak tests –One co-ordinate system and S.I. units Maximise Workflow –Cater tools to their needs –Put new functionality on bypass Think of the man-hour cost! –Minimise potential for human error

11 Best Practice Build A Debugging Suite –Instant Pause –Flyable Camera –Layered Information –Action Histories Maintain Player Immersion –A.I. should not be too bad or too good –Limit ourselves to what the player would know –Constrain them to the same actions

12 The Algorithmic Approach

13 Key tenants –Use only what we might know –Mimic the player The Think-Act Loop Sensory Receptors Brain Controller Game Player Sensory Data Think Controller Act A.I.

14 Sensory Data Think Controller Act Sensory Input

15 Sensory Data Detail level –Depends on genre and perceived communication Thief vs Medieval Total War II General Model –Visual component an arc –Auditory component a radius Auditory targets less official –Occlusion too expensive? Shoot a weapon to get info –Theorise using ghost images

16 Blackboards Used To Share Information –Static blackboard stores defined types of info –Dynamic blackboard stores arbitrary data Agents write to the board –Generally read it as well Agent A Agen t B {10,20,15} {-30,20,13} {-10,15,12} {17,11,5} Scout {3, 17, 10} Cover {17, 11,5}

17 cSensorCone Think Controller Act Control Output

18 Virtual Controller (yoke) Purpose –Carries control instructions –Provides a strict I/O Divide Notice the const correctness –Unifies player and A.I. Control mapping for the player A.I. fills it in from Think() Key Properties –Never stored in it’s entirety –Created on the stack –Lifetime of a single Process() Process() Think(yoke&) const Controller Act(const yoke&) cVirtualYoke yoke

19 … So Actually Process() Think(yoke&) const Act(const yoke&) cVirtualYoke yoke cSensorCone cBlackboard

20 Virtual Yoke Composite –Collection of smaller yokes –Allows selective storage cVirtualYoke cLocomotionYoke mLocomotion; cWeaponYoke mWeapons; cAnimYoke mAnimation; cLocomotiveYoke fp32mGas; fp32mSteering; fp32mPitch; cEntity *mpDontAvoid; Context Based Controller Input Logic Flow Control cWeaponYoke enum eFire { F_PRESSED, F_WHEN_IN_CONE, F_WHEN_LOCKED }; eFire mFireButton; Conditional signals

21 Taking Action Process() Think(yoke&) const Act(const yoke&) cVirtualYoke yoke cSensorCone cBlackboard

22 Act Applies Yoke Commands –Composite yokes  Subsystems Object Models –Supply common interfaces Could be a turret mounted weapon or my pistol. Could be driving a car, a plane, a boat or myself! Act() yoke iWeaponMgr* iLocomotive*

23 Object Model Self Contained –Instructions for Think –Actions for Act –Commutable Plug and Play Downloadable Content –Maybe broadcast use Think Sims 2! iLocomotive // Fills in yoke ComputeMotion(const cTarget &, cLocomotiveYoke&) // Computes forces ApplyMotion(const cTarget &, const cLocomotiveYoke&, v3 &force, v3 &torque)

24 More Coolness ApplyMotion(const cTarget& targ, cLocomotiveYoke& yoke) { v3 force(Zero); v3 torque(Zero); if (Drivable()) { Drivable()->ApplyMotion( targ, yoke, force, torque); } if (Floatable()) { Floatable()->ApplyMotion( targ, yoke, force, torque); } ApplyForceTorque(force, torque); } Compute Apply Compute Apply Compute Apply Compute Apply

25 Frame Time Process() Think(yoke&, dt) const Act(const yoke&, dt) cVirtualYoke yoke cSensorCone Update(dt) cBlackboard dt()

26 Work into everything –Including fixed time steps –No more s+=v, s+=v*dt() Benefits –Integration Implicit forward Euler –Rough Smoothing –Closed Feedback –Pause dt=0 –Level Of Detail dt=2dt –Special Effects dt() smoothing feedback control

27 The Functional Approach

28 Problem Domain Examine the Terminology –Feeling, Knowledge, Goals, Beliefs, Needs Examine the Concepts –Decisions, Facts, Uncertainty, Exploration, Verb-Noun Actions, Repetition, Sequencing Needs a problem

29 What Are We Doing? Goal based reasoning –Working to solve a goal –Thinking about and realising smaller tasks –Taking a hierarchical approach –Using a limited number of short verb-noun pairings to form a plan We’ve seen this before –An old pattern Colossal Cave Adventure and MUDs Verb-noun actions like “get axe, wield axe” separated by movement “n, e, e, s, e” –We use a container object model

30 Applying The Pattern Know of “Mine, Smithy” 1.Goto “Mine” Know of “Wall” 2.Get Ore From “Wall” 3.Goto “Smithy” Know of “Door” 4.Use “Door” 5.Play “Open Door” 6.Warp Inside Know of “Owner”, “Forge” 7.Play “Close Door” 8. Goto “Owner” 9. Put 10 gp In “Owner” 10. Get Time From “Owner” 11. Goto “Forge” 12. Put Time In “Forge” 13. Put Iron In “Forge” 14. Use “Forge” 15. Use Enchant 16. Use “Forge” 17. Get Sword From “Anvil” Taking the computational to the algorithmic

31 Easy Questions Why did I choose to do this again? –Because we were driven to it by personality and need What happens when I get the treasure? –I’ll probably choose to do something else depending on my mood Why stop at get iron? –Because its reached the atomic level - there are no more questions, just results

32 Putting It Together Ambient Controller “Get treasure” Planning “Get magic sword” Explicit Order “Script here, Please greet the player”

33 Explicit Orders Script commands –Script on A.I. Autopilot –Player on Player Player instructions –Player on A.I. Squad Commander –A.I. on A.I. Ambient Controller “Get treasure” Planning “Get magic sword” Explicit Order “Script here, Please greet the player”

34 Scripting Notes Don’t mix styles –Script has immediate control –Script waits for an opportunity Keep common properties separate –No sharing memory locations A script global population density A code global population density –Maintain a set order of calculation Generally consistent with style

35 Ambient Controller Generates sensible actions autonomously –Maybe Idle –Maybe Full Daily Routine Daily Routines –Character properties –Needs/Drives –Scheduling –Time of day  nice emergent behaviour Ambient Controller “Get treasure” Planning “Get magic sword” Explicit Order “Script here, Please greet the player”

36 Daily Routine Sleep Goto Work Work Leave Work Relax Go Home Drive Model Time Of Day Schedule Housework5% Tavern50% Brothel30% Shopping10% Study5% Character Hunger Libido dt()

37 Planning Ambient Controller “Get treasure” Planning “Get magic sword” Explicit Order “Script here, Please greet the player” Goto Position “Goto Forge” Perform Action “Use Forge” Planning Continuous bar dt()  Defines Think()

38 Plan Components Play “Animation” –Waits on dt() Use “Object” –Object model again –Broadcast actions. –Change world based on state –May wait on dt() Get/Take “Object” From “Container” –Primary world manipulation –Contents determine state Goto “Location” –Waits on dt() –Key A.I. output –Complex –Warrants special attention later

39 Search Based Planning Traditional academic approach –See STRIPS, Hierarchical Task Networks, Bratko The Good –Mimics our introspective reasoning –Seeks to fully realise a plan to the goal Directed search for optimal solutions Post processing even more so The Bad –Knowledge representation Scalability - difficult for video games Lots of storage

40 Procedural Planning Industry preferred approach Hierarchical Easy to comprehend Limited Language –Goals –Sub-goals –Conditions –Actions Transitions –Sequential –Decision Based Powerful Results Get Object Short of money? Make Thief skills? Steal Buy Get OreGoto Mine

41 Procedural Planning - Issues Competing children –We have to make a best guess from the options –A* might help But we could still end up with case of a basic sword being bought but not affording the forge. Incomplete plan means no post process –Not good for player supporting A.I. –More action for generic A.I.

42 The Curve Ball Task Interruption –I’m returning to my gang hideout –I see an enemy I engage the enemy –I roll out of the way of a car –I recover to my feet I re-engage the enemy –I continue to return to my gang hideout … Is A Key Requirement of our A.I.

43 Finite State Machines No plan history  No idea of context  No generalised exit.  Hideous state history workarounds Don’t scale well –Many transitions Death Look At Gain Range Attack Kill ExamineDeath Look At Gain Range Attack Kill Examine HFSM came along –Eased transitions –But history still an issue

44 Behaviour Trees Goto Source Get Material Take Material Create Buy Steal Get Object Crime Allowed? Low money? Ok money? Thief Class? Sequencing Selection Preconditions Actions Decorators

45 Modelling Interruption? Goto Location Kill Enemy Return Home Locate Hideout Gain Range Fire Weapon Ambient Behaviour Parallel?

46 Behaviour Trees Simple and powerful –Limited vocabulary –Most situations handled Highly flexible –Plug and play –Customisable –Nice design tools –Handy child evaluation Lends itself to directed decision making Issues –Interruption handling Where to return –Amount of flexibility Trees get complicated

47 MARPO Goto Source Get Material Take Material CreateBuy Steal Get Object Not enough money? Money? Thief class? Thieving allowed?

48 ReactiveLong term MARPO Get Object Create Get Material Take Material Immediate

49 ReactiveLong term MARPO Get Object Take Material Kill Enemy Gain Distance Immediate Get MaterialCreate

50 ReactiveLong term MARPO Get ObjectKill Enemy Immediate Roll Gain Distance

51 Behavioural Bamboo Forest –Stack based tasks Suppression model –Multiple threads of execution Keeps only one stack in memory down to current task –Decision logic lies with the parent Higher level parameterised building blocks –Authoring is by script not tool. Winding Ability –Allows auto-recovery from any state –Respects script orders on an immediate basis MARPO

52 Movement

53 Goto Position What Is Position? –A world co-ordinate –An entity –A navigation point –An offset off an entity –A radius off an offset, off an entity Still World Positions! –So generalise cTarget void Set(…); void SetArrivalConditions(…); v3 WorldPos() const; v3 InterceptPos(…) const; bool HasArrived(…) const; Lots for free –Entity intercept –Completion checks

54 The Problem Dynamic obstacles … on the move Terrain type Usable surfaces Static geometry

55 Stage 1 - Navigation Navigation Mesh –Industry standard [Tozour, 2008] –Handles static geometry Searching with A* –Optimise search space, not A* Improve Data Format –Make it Hierarchical –Allow for reference spaces [Isla, 2005]

56 Navigation Quick To Search  Live Updates Zones  Spatial Reasoning [Isla, 2005]

57 cNavPos Components cNavId mPolyId; mPolySource; cNavPoly … indices … edge info cNavId mParent cNavId mFirstChild mChildCount cPolyCoords … local co-ords cNavigator Pos() FinalTarget() CurrentTarget() HasArrived() Update( cTarget ) cNavPos mFinal cNavPos mCurrent cNavPos mPos

58 Stage 2 - Optimisation String Pulling –Bevel uncrossable edges to a radius –Tighten points

59 Stage 3 - Dynamic Avoidance At rest –Mesh binding On move –Intercept calculation

60 Stage 4 - Locomotion Differing approach –Vehicle –Humanoid Can we generalise?

61 Locomotion iLocomotion ComputeMotion(…) ApplyMotion(…) Generalised by iLocomotion Same interface Different yoke instructions Vehicles –Vehicles supply gas, steering –Difficulty is in mapping target to gas and steering Actors –Actors supply ideal position, velocity, direction –Difficulty is in animation to hit position.

62 Locomotion - Vehicles Target Position Top Speed Speed For Deceleration Speed For Steering Min Speed to Gas Maths PID Controller Yoke Angle To Steering Maths PID Controller

63 Some Hints a d v max u v s1s1 s2s2 Speed For Deceleration Equations of Motion Speed to Gas Angle To Steering PID Response Curves Manual Shift

64 Animation

65 Locomotion - Character Animation  Position Challenges –Sensible manoeuvre choice Animation Graphs Navigation Mark Up –Natural fluidity Foot positioning Hand positioning Idle Walk Run Crouch Crouch Walk Crouch Run

66 Goal Based Directed Search of Animation Graph –Incorporates interesting A.I. along the way –Think Gears of War, A* Interested  Champandard Personal Issues –Need to find a full animation plan to goal This would need to sit with path finding Makes it more expensive –Painful Heuristic Balancing Switching to the time domain probably partially solves it But how do we prevent repeated actions

67 “Carrot On A Stick” Method Keep iLocomotion interface ComputeMotion() computes velocity ApplyMotion() animates for velocity dt() helps minimise error Want to use a cover point? –Explicit decision  a new target iLocomotion ComputeMotion(…) ApplyMotion(…) Given the animation properties We can solve by mathematics

68 Cover Under fire? Find cover? Route to cover Kill Nav. mesh Goto Set piece “Carrot On A Stick” Method Anchor Point

69 “Carrot On A Stick” Method s  Direction for Animation off=ƒ( ,s) Offset based on , s Changes our target –Hierarchical changes Changing over dt Smoothes our arrival –Think high jump! –Never perfect –But mistakes made are human –Constrained by navigation mesh

70 “Carrot On A Stick” Method s u V = 0? Equations of Motion –They’re back! Give us our aim velocity v aim What about a and d? v max a d u v s v aim dt

71 Animations –distance (s) in time (t)  an average velocity –Assume velocity bands –Look up v for animation –Dead zones indirectly determine blending a and d now character properties Actively correct for v –Use tricks like light I.K. Implicitly correct by dt() v t Crouch run Crouch walk v Crouch run “Carrot On A Stick” Method

72 Animation graph –Used for information query only Locomotive cycle driven by v aim Free things! –Foot positioning Obtained through cycle alteration and dt() –Blending –Character movement properties Obtained through a and d –Swim, crawl, crouch, walk Same procedure, different velocity bands –Human like arrival mistakes

73 Conclusion

74 –Best Practices –Strict interfaces –Multiple takes on problems –Concepts –Commutability –Components –Algorithms –Hierarchy –Pattern Recognition –Human Sciences Good reusable AI is about

75 Conclusion Style vs Structure - Hecker 2008 –“Texture Mapped A.I. Triangle” –Style No idea where it is – annoyingly –Structure Not one single triangle But many triangle like pieces Represented differently But all essentially the same piece

76 References Key References –Laming, B. [04] “The Art of Surviving A Simulation Title”, A.I. Wisdom 2, Charles River Media –Laming, B. [08] “The MARPO Methodology: Planning And Orders”, A.I. Wisdom 4, Charles River Media –Isla, D. [05], “Dude, where’s my Warthog?”,, –Tozour, P. [08] “Fixing Pathfinding Once and For All”,, 13.3.08 –Champandard, A.,, 13.3.08 –A.I. Wisdom Books in general Introduction –Reynolds, C. [87] “Boids”,, 13.3.08 Intercept Calculation and Dynamic Avoidance –Stein, N. [02] “Intercepting a Ball”, A.I. Wisdom 1, Charles River Media. [HINT ] –Tozour, P. [02] “Building a Near-Optimal Navigation Mesh”, A.I. Wisdom 1, Charles River Media Best Practice –Tozour, P. [02] “Building an AI Diagnostic Toolkit”, A.I. Wisdom 1, Charles River Media Sensory and Blackboards –Isla, D. and Blumberg, B. [02] “Blackboard Architectures”, A.I. Wisdom 1, Charles River Media Planning –Champandard, A [08], “Getting Started With Decision Making and Control Systems”, A.I. Wisdom 4, Charles River Media –Champandard, A., “Understanding Behaviour Trees”,, 13.3.08 –Yiskis, E. [04] “A Subsumption Architecture for Character-Based Games”, A.I.Wisdom 2, Charles River Media Navigation –Numerous references, almost all A.I. Wisdom books. Locomotion – Vehicle –Alexander, B. [02] “The Beauty of Response Curves”, A.I. Wisdom 1, Charles River Media –Forrester, E. [04] “Intelligent Steering Using PID Controllers”, A.I. Wisdom 2, Charles River Media Conclusion –Hecker, C. [09] “Structure vs Style”,, 13.3.08

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