Managing. Complexity in the. Damián Isla Bungie Studios

Transcription

1 Managing Complexity in the Halo2 AI Damián Isla Bungie Studios

2 Managing Scalability in the Halo2 AI Damián Isla Bungie Studios

3 Scalability? No! Scalability!

4 Why Scalable AI? More is BETTER The brute force approach to common sense: the more unique situations you can recognize and react to, the more common-sensical you are. (e.g. Cyc, OpenMind projects)

5 3 Dimensions of Scalability elite major heretic grunt hunters flood swarms hell-jumpers brute captain Variety Miranda sniper marine challenge story Variation DRAMA! pacing drive the warthog Volume fight Shoot the needler perch hide Strafe target melee attack boarding

6 The Ultimate Goal Designer I want X to do Y whenever Z! Engineer Okay. (To himself) Hmm and I know exactly where to put that Designer I m happy now. (Trundles back to design-bubble)

7 AI Design Requirements Transparency Facilitate the ongoing narrative in the player s head Coherence Focused action, right priorities Directability For the designer Workability For the engineer See [Butcher & Griesemer] GDC 2002

8 Managing Scalable Decision- Making in the Halo2 AI Damián Isla Bungie Studios

9 Level scripting Character definition Decision making Perception Memory

10 Level scripting Character definition Decision making Perception Memory

11 Behaviors Behavior: a program that takes temporary control of the actor in order to get something done Behaviors are action over time: Move to a point Orient Shoot Crouch Play dialogue Trigger actions World e.g., Perception Fight & Knowledge Model Vehicle entry Throw Target grenade Selection Find cover Cover friend Check body Wake up a sleeping grunt Etc. Behavior Halo2: ~130 in total Behaviors have notion of relevancy and duration Looking Aiming / Shooting Moving Talking Animation, Physics, Sound

12 The Combat Cycle

13 Decision-Making Behavior Tree Retreat Flee (or DAG) Cover Self-preservation Guard Good Intuitive Modular Scalable Root Engage Grenade Charge Vehicle fight Fight Vehicle strafe Melee Presearch Bad Never really that simple Debugging is hard Memory is hard Idle Search Guard Guard Uncover Investigate

14 Decision routines Parent custom Child-competitive

15 Decision routines Child-competitive strategies Analog Relevancy Binary Relevancy Prioritized-list (root, engage) Sequential Sequential-looping (search) Randomized One-off randomized (postcombat) Design Principle #1 : Given A, B or C, always choose D (all of the above)

16 Impulses Problem: What happens (with a prioritized list) when the priority is not constant? Unless the player is in vehicle, in which case...

17 Impulses Solution: Separate alternative trigger conditions out into separate impulse Two execution options In-place Redirect Design Principle #2 : Formalize complexity (don t hide it)

18 Impulses The other purpose of impulses: HACKS Or, localized code with ad-hoc Functionality e.g. crouch_on_danger impulse dive impulse

19 Behavior Metadata Problem: In determining relevancy, we check the same conditions over and over and over and over Actor s vehicle status (infantry, driver, passenger) Actor s alert status (idle, in combat, visible target) Solution: Use metadata to describe execution conditions Force all behaviors to declare their execution conditions Halo2: conditions bitvector compared to actual state bitvector Acts as a behavior/impulse mask (modifying the basic structure of the tree itself)

20 Custom Behaviors Problem: character-type specific behaviors Don t want all characters evaluating behaviors that only one type can do E.g. grunt s retreat from scary enemy impulse Solution: attach custom behaviors to tree Design Principle #4 : Variation from a stable base Retreat Flee Retreat Flee Self-preservation Self-preservation Retreat on scary enemy impulse Root Grenade Root Deploy turret impulse Retreat on danger impulse Grenade Charge Charge Vehicle fight Vehicle fight Engage Engage Fight Fight Search Search Guard Guard Generic Grunt

21 Stimulus Behaviors Problem: rare event-driven behaviors tested for every tick e.g., Grunts flee when their leader dies Actor Died 1 sec. Solution: Stimulus Behaviors Behaviors or impulses dynamically and asynchronously placed into tree at specified location

22 Joint Behaviors Blackboard Hey, anybody wanna do this? Sure, why not? Remember Design Principle #1! (always choose D, all of the above)

23 Behavior Summary Behavior DAG Binary decision routines Impulses Metadata Custom Behaviors Stimulus Behaviors Joint behaviors The behavior tree is fundamentally a DYNAMIC structure!

24 Level scripting Character definition Decision making Perception Memory

25 Memory and Memory Problem: Persistent behavior state is impossible (too much data to keep around!) Solution: Only keep around state for behaviors that are running Problem: What about state we need to keep around regardless of whether we re running the behavior or not? (e.g. last grenade-throw time)

26 The Anatomy of Memory memory is a complicated thing. Store memory as Behavior state (short-term) Persistent per-behavior Persistent per-target Per-target Per-target Persistent per-behavior Behavior Per-target Props are also Perception caches Our knowledge model Props Behavior-state (short-term)

27 The Anatomy of Memory e.g. Search Aquire target 1 Lose sight of target 1 Search target 1 Last known location inspected New search point selected Aquire target 2 Search deactivated, short-term state discarded Target 2 killed Switch to target 1 Search target 1 Where do we search? Existing search point used Per-target Per-target Per-target Remember Design Principle #2 (formalize complexity, don t hide it) Persistent per-behavior Behavior Behavior-state (short-term)

28 Level scripting Character definition Decision making Perception Memory

29 Designer Direction Variety Character definition Model, animation graph Behavior parameters Static control Level-scripting Dynamic Control Level scripting Perception Decision making Character definition Memory Variation

30 Static Control: Variety Character types Elites Grunts Marines Variants Gold elite Red elite Honor guard elite Jetpack elite A LOT of parameters to author and maintain! 64(variants) x 80 (behaviors/variant) x 3 (parameters/behavior) = 15,360 Each variant gets a.character definition file

31 Static Control: Parameters Character files divided into parameter blocks Vitality properties Perception properties Search properties Weapon usage Etc. Marine Grunt Generic character Elite Blocks can be instantiated or not Helljumper Heretic grunt Grunt major Elite major Hierarchy: children characters instantiate only significant variations from parent Root of tree is the generic character, which provides reasonable values for all parameters Jetpack elite Elite ultra Remember Design Principle #4! (Variation from a stable base)

32 Static Control: Styles Styles are cool: Behavior mask Bias control parameters Superposable (Another reason why Impulses are great)

33 Dynamic Control: Organization Halo1: Encounters Groups of actors Groups of areas Problem: moving actors from one encounter to the next was architecturally discouraged Halo2: Squads Groups of actors Zones Groups of areas Orders A mapping between the two

34 Dynamic Control: Orders The Metaphor: Take that hill! Hole up in that bunker! Or Occupy this space and behave this way! Position direction Behavior direction

35 Blah blah blah Order Forward

36 Orders and Behavior Behavior direction Vehicle behaviors enabled/disabled Active camouflage enabled/disabled Follow player enabled/diabled Rules of engagement Style Behavior mask Bias control parameter Alright, men, engage active camouflage and move forward cautiously. And don t open fire until the Master Chief does!

37 In summary Design Principle #5 : Work with your representations

38 Recap Design Principles: 1. Always choose D (all of the above) 2. Formalize complexity (don t hide it) 3. Embrace the hackery 4. Variation from stable base 5. Work with your representations

39 Technical Takeaways Dynamic behavior tree Binary relevancy Behavior masking Principled approach to memory Take smarts out of the behavior and put it in the knowledge model

40 Conclusions All systems need to play ball with the core decision mechanism Not looking for smart architecture, looking for expressive architecture

41 The Ultimate Goal Designer I want X to do Y whenever Z!

42 Questions? Damián Isla