1. Scaling Up To More Sophisticated AI Without Overburdening Developers - Lessons Learned Marc Atkin Irrational Games AGDC 4-Dec-2004

2. MotivationMotivation

3. Why Do We Want Better AI? Bad AI is always noticed AI often inconsistent between games – why can’t we build on previous work? The AI is a big part of what makes a game world immersive and fun

4. What’s Stopping Us? What makes AI complex? –AI has many possible reactions to many situations –Many behaviours are operating on the AI at any given time –Hard to account for all the possible interactions Impression that academic AI research isn’t applicable (too slow, too complex, too flaky)

5. The Basic Idea: Use Modularity It’s not just for coding anymore! Produces AI that is easier to understand and debug Produces AI that it reusable within and between projects The key point: Design the AI architecture so that it facilitates modularity

6. Tribes: Vengeance First-person shooter Emphasises speed and freedom of movement (jetpacks!) 3 rd in the series

7. AI Challenges AI works in service of the game: – AI characters shouldn’t act stupidly – yet still be fun to fight AI’s should exhibit diverse behaviour AI’s should jetpack and ski AI’s should react to their environment AI’s should have recognizable roles –Sniper, duelist, mortar user, etc.

8. System Overview Navigation System Physics System AI Game World Pathfinder Animation System Animation System

9. What makes our game characters “intelligent”? A large set of sophisticated, hand- designed behaviors Common sense reactions The ability to exploit opportunities A consistent long term view of tasks to be accomplished

10. What makes our game characters “intelligent”? A large set of sophisticated, hand- designed behaviors Common sense reactions The ability to exploit opportunities A consistent long term view of tasks to be accomplished Tyrion: An architecture for specifying and executing AI behaviour

11. How Tyrion Works

12. Resources, Goals, and Actions Goal: A description of a desired world state Action (or behaviour): A method for achieving a goal Resource: An entity required to perform an action

13. Resource Hierarchy Team Squads Vehicles Characters Driver/Gunners Legs/Arms/Head

14. Resource Hierarchy TeamStrategic level Tactical levelSquads Vehicles Characters Driver/Gunners Legs/Arms/Head Individual Units Motor Control

15. Action/Goal Hierarchy Attack From All Sides NormalAttack DuelistAttack MoveTo Squad Character Legs / Arms Attack Goals Fire Weapon MoveTo Goal FireAt Goal Grunt Duelist

16. Action Structure Goal satisfied by action Selection heuristic –Evaluates appropriateness of action in a given situation Message callback functions for child actions Message callback functions for sensors Body (Unreal script latent code) –Typically executes over a number of game ticks

17. Example Action: Search Satisfies: SearchGoal( target, searchDistance ) –Only action that satisfies this goal Child action callbacks: –Set errorCode Sensor callback: –Terminate action if target spotted

18. Example Action: Search (cont) Body: –Store location and rotation –ActivateSensor( TargetSensor, target ) –Loop over searchPositions: WaitForGoal( MoveToGoal, searchPosition) Fail() if MoveToGoal was not achieved Play LookingAround animation Pause AnimationTime seconds –WaitForGoal( TurnGoal, originalRotation ) –Succeed()

19. Sensor Hierarchy Sensors can build upon one another, too DodgeSensor: Warns a character about incoming projectiles EnemySensor: Maintains a list of visible hostiles TargetSensor: Triggers when a particular unit is visible Vision System

20. AI and Scripting High level behaviours look a lot like scripts A script is essentially a one- time use behaviour Tyrion could be used to drive the scripting system

21. Example Script Executes when player enters trigger Spawns AI’s Moves AI’s into position and has them attack player

22. Example Script Executes when player enters trigger Spawns AI’s Moves AI’s into position and has them attack player Sensor activation Posting sub-goals

23. Execution Model 10 times a second: –Iterate over every resource: Order unmatched goals by priority Find actions that achieve them Tick every running action Tick every active periodic sensor; send messages If an action completes, mark its goal as achieved/failed; send messages

24. A Day in the Life of a Grunt A Grunt AI with three goals: –PatrolGoal (priority 40) –AttackGoal (priority 50, dormant) –DodgeGoal (priority 90, dormant) AttackGoal will activate when an enemy is sighted DodgeGoal will activate when a visible projectile will hit the AI

25. A Day in the Life (cont) Patrol is executing 40 PatrolGoalAttackGoalDodgeGoal legs arms

26. A Day in the Life (cont) Patrol is executing 40 PatrolGoal Enemy spotted! Sensor wakes up AttackGoal Attack executes; posts subgoals for legs & arms 40 50 AttackGoalDodgeGoal legs arms 50 X

27. A Day in the Life (cont) Patrol is executing 40 PatrolGoal Enemy spotted! Sensor wakes up AttackGoal Attack executes; posts subgoals for legs & arms 40 50 AttackGoalDodgeGoal legs arms Projectile spotted! Sensor wakes up DodgeGoal Dodge executes; needs legs arms continue executing Attack! 90 40 50 arms 50 legs arms X X 50 X

28. A Day in the Life (cont) Patrol is executing 40 PatrolGoal Enemy spotted! Sensor wakes up AttackGoal Attack executes; posts subgoals for legs & arms 40 50 AttackGoalDodgeGoal legs arms Projectile spotted! Sensor wakes up DodgeGoal Dodge executes; needs legs arms continue executing Attack! 90 40 50 Dodge finishes: legs resume Attack subgoal 40 50 arms 50 legs arms legs arms 50 X X X X

29. Lessons Learned

30. Sophisticated AI != Expensive AI This is not an expensive system –No search due to scoring function on actions –Action to goal matching only occurs when goal list changes for the resource –Although there are many actions running simultaneously, at any given time a lot are sleeping –Most of high-level AI was written in Unreal Script (20x slower than C++) yet it still wasn’t a bottleneck.

31. Some AI is expensive Superficially simple systems that require large amounts of debugging and tweaking (e.g. emergent AI) Search (e.g. planning, pathfinding) Line checks (e.g. vision)

32. Analysis (pluses) Character actions were very useful in coordinating squads and chaining sub- actions Useful to think of legs and arms as separate resources Architecture no harder to use than others, but quite easily reusable for other projects Using squad goals worked well: helped reduce the amount of level design work

33. Analysis (minuses) Full potential of architecture not exploited: FPS opponents are only human-like to a degree; their required spectrum of behaviours is fairly limited Exposed too much detail to designers; editor could have been streamlined We didn’t use the AI architecture to drive the scripting system: duplication of effort

34. General Lessons Learned Create AI that’s required by the game Create debugging tools early Leave time for tweaking Make sure the player knows when the AI does something cool Does good AI make a game fun (or is it just that bad AI makes a game not fun)?; do games need scripted set-pieces?

35. Take-home Message powerful AI != expensive AI One AI description language can control control teams, squads, characters, and scripting A modular AI is a reusable AI – let the AI engine handle the interaction complexities

36. The End

38. Unused Slides Follow….

39. Oh No, Not Another Hierarchy! Control hierarchies are a dime a dozen in the AI/robotics literature Tyrion’s more interesting features: –No set number of levels –Uniform description language and execution model used across levels –Separate sensing system –Dormant goals –Supervenience: information is sent up, goals are sent down

40. Relationship to AI Planning Tyrion can be viewed as a Task Network (or Partial Hierarchical) Planner Goal A Possible Actions

41. Relationship to AI Planning Tyrion can be viewed as a Task Network (or Partial Hierarchical) Planner Goal A Possible Actions Sub-goals Possible Sub-Actions

42. Relationship to AI Planning Tyrion can be viewed as a Task Network (or Partial Hierarchical) Planner Goal A Possible Actions Sub-goals Possible Sub-Actions xOutcome for goal A