1. Finite State Machine for Games Fall 2012 Ref: Chenney, CS679 lectures AI Game Programming Wisdom 2

2. Fall 20122 Outline AI and Game Introduction/examples Design Intuition State-based Implementation Extending Stack-based Fuzzy-state machine

3. Fall 20123 What is AI? (and is NOT) AI is the control of non-human entities in a game The other cars in a car game The opponents and monsters in a shooter Your units, your enemy ’ s units and your enemy in a RTS game But, typically does not refer to passive things that just react to the player and never initiate action That ’ s physics or game logic For example, the blocks in Tetris are not AI, nor is a flag blowing in the wind

4. Fall 20124 AI in the Game Loop AI is updated as part of the game loop, after user input, and before rendering

5. Fall 20125 AI Module AI Update Step The sensing phase determines the state of the world May be very simple - state changes all come by messaging Or complex - figure out what is visible, where your team is, etc The thinking phase decides what to do given the world The core of AI The acting phase tells the animation what to do Game Engine Sensing Thinking Acting

6. Fall 20126 AI by Polling Senses: looks to see what has been changed in the world then acts on it Generally inefficient and complicated Different characters might require different polling rate AI gets called at a fixed rate

7. Event Driven AI Events triggered by a message basically, a function gets called when a message arrives, just like a user interface) Example messages: You have heard a sound Someone has entered your field of view A certain amount of time has passed, so update yourself Fall 20127 does everything in response to events in the world

8. Fall 20128 AI Techniques in Games Basic problem: Given the state of the world, what should I do? A wide range of solutions in games: Finite state machines, Decision trees, Rule based systems, Neural networks, Fuzzy logic A wider range of solutions in the academic world: Complex planning systems, logic programming, genetic algorithms, Bayes-nets Typically, too slow for games

9. Fall 20129 Expressiveness What behaviors can easily be defined, or defined at all? Propositional logic: Statements about specific objects in the world – no variables Jim is in room7, Jim has the rocket launcher, the rocket launcher does splash damage Go to room8 if you are in room7 through door14 Predicate Logic: Allows general statement – using variables All rooms have doors All splash damage weapons can be used around corners All rocket launchers do splash damage Go to a room connected to the current room

10. Fall 201210 Finite State Machines (FSMs) A set of states that the agent can be in Connected by transitions that are triggered by a change in the world Normally represented as a directed graph, with the edges labeled with the transition event Ubiquitous in computer game AI

11. Fall 201211 Formal Definitions (N. Philips) "An abstract machine consisting of a set of states (including the initial state), a set of input events, a set of output events, and a state transition function. The function takes the current state and an input event and returns the new set of output events and the next state. Some states may be designated as "terminal states". The state machine can also be viewed as a function which maps an ordered sequence of input events into a corresponding sequence of (sets of) output events. Finite State Automaton: the machine with no output

12. Fall 201212 FSM with Output: vending machines OJ & AJ for 30 cents State table

13. Fall 201213 Vending Machine: state diagram

14. Fall 201214 FSM and Game Game character behavior can be modeled (in most cases) as a sequence of different “ mental state ”, where change is driven by the actions of player/other characters, … Natural choice for defining AI in games

15. Fall 201215 FSM Examples

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19. PacMan State Machine Fall 201219 PacMan eats a Power Pill Timer <= 0 Collision with PacMan Timer <= 0 Collision with GhostBox

20. Actions of States Fall 201220 Roam; If PacMan gets close, PathTo (PacMan) Timer--; PathAwayFrom (PacMan) PathTo (GhostBox) Timer— Move back-and-forth

21. Fall 201221 Ex: predator vs. prey Prey (laalaa) Idle (stand,wave, … ) Flee (run) Sees predator No more threat captured Dead

22. Fall 201222 Predator (Raptor) Idle (stand) Hungry (wander) Pursuit (run) T idle > 5 Prey in sight T pursuit > 10 Dining Prey captured T dining >5

23. Fall 201223 Idling LaaLaa Stand Wave Wander (set random target) 50% 30% 20% Target arrived T wave >2 R T stand >4 This page illustrates: hierarchical state, Non-deterministic state transition This page illustrates: hierarchical state, Non-deterministic state transition

24. FSM Design

25. Fall 201225 Quake2 Examples Quake2 uses 9 different states: standing, walking, running, dodging, attacking, melee, seeing the enemy, idle and searching. Incomplete design Intuitive thinking: model the events and state changes

26. Fall 201226 Quake: Rocket

27. Fall 201227 Shambler monster

28. Fall 201228 FSM Advantages Very fast – one array access Expressive enough for simple behaviors or characters that are intended to be “ dumb ” Can be compiled into compact data structure Dynamic memory: current state Static memory: state diagram – array implementation Can create tools so non-programmer can build behavior Non-deterministic FSM can make behavior unpredictable

29. Fall 201229 FSM Disadvantages Number of states can grow very fast Exponentially with number of events: s=2 e Number of arcs can grow even faster: a=s 2 Propositional representation Difficult to put in “ pick up the better powerup ”, “ attack the closest enemy ” Expensive to count: Wait until the third time I see enemy, then attack  Need extra events: First time seen, second time seen, and extra states to take care of counting

30. FSM Control System Implementation

31. Fall 201231 FSM Implementation

32. Fall 201232 Previous Example

33. Fall 201233 Code 1 Ad hoc implementation

34. Fall 201234 Code 1p

35. Fall 201235 Code 2 Structure, Readable, maintainable

36. Fall 201236 Hierarchical …

37. FSM Extensions

38. Advanced Issues Hierarchical FSM Non-deterministic FSM Swapping FSM Fall 201238

39. Fall 201239 Hierarchical FSMs What if there is no simple action for a state? Expand a state into its own FSM, which explains what to do if in that state Some events move you around the same level in the hierarchy, some move you up a level When entering a state, have to choose a state for its child in the hierarchy Set a default, and always go to that Or, random choice Depends on the nature of the behavior

40. Fall 201240 Stack-based FSM History stack: Remember previous state; create characters with a memory … Pushdown automaton (PDA)

41. Fall 201241 Goal-based vs. State-based There is also a slight derivative to the state- based engine, but it used in more complicated games like flight simulators and games like MechWarrior. They use goal - based engines - each entity within the game is assigned a certain goal, be it 'protect base', 'attack bridge', 'fly in circles'. As the game world changes, so do the goals of the various entities.

42. Fall 201242 Processing Models Polling FSM update frequency Easy to implement and debug Inefficiency (Little Red example) Event-driven Publish-subscribe messaging system Game engine sends event messages to individual FSMs An FSM subscribes only to the events that have the potential to change the current state Higher efficiency, non- trivial implementation

43. Fall 201243 Efficiency and Optimization In AI, FSM is the most efficient technology available Yet, there is always room for improvement Level of Detail: depending on the condition (e.g., distance with player), use different FSM, or different update frequency

44. Fall 201244 References Web references: www.gamasutra.com/features/19970601/build_brains_into_ games.htm www.gamasutra.com/features/19970601/build_brains_into_ games.htm csr.uvic.ca/~mmania/machines/intro.htm www.erlang/se/documentation/doc- 4.7.3/doc/design_principles/fsm.html www.erlang/se/documentation/doc- 4.7.3/doc/design_principles/fsm.html www.microconsultants.com/tips/fsm/fsmartcl.htm http://www.angelfire.com/dragon/letstry/tutorials/dfa/ Game Programming Gems Sections 3.0 & 3.1 It ’ s very very detailed, but also some cute programming

45. Well, the sad news … Fall 201245 Behavior trees

46. Fall 201246

47. Additional Materials Fall 201247

48. Fall 201248 Intuitive Design Say, a simple teletube baby has three states: idle, run, and wave Scenario: When an idle laalaa sees a butterfly, it waves to it. When the butterfly flies away, it returns to idle When an idle laalaa sees a mouse, it flees away. When the mouse is no longer in sight, it returns to idle

49. Fall 201249 Laalaa idle flee wave mouse ~mouse ~butterfly butterfly How to make sure the design complete? I.e., all states and transitions are considered Is there any systematic way to develop an FSM?

50. Fall 201250 Quake Bot Example (first-cut) Types of behavior to capture: Wander randomly if don ’ t see or hear an enemy When see enemy, attack When not see enemy and hear an enemy, chase enemy When die, re-spawn (new a bot from start) Events: see enemy, hear enemy, die States: wander, attack, chase, spawn

51. Fall 201251 Remark With 3 events, potentially there should be 2 3 states: (E,S,D)=(0,0,0),(1,0,0),(0,1,0), …,(1,1,1) Some doesn ’ t make sense E.g., ESD = 111 Name and create a state for the ones that we want to consider Wander (ESD=000) Chase (ESD=010) Attack (ESD=1x0), x for dont-care Die (ESD=xx1)

52. Fall 201252 FSM (first-cut) Events: E: see an enemy S: hear a sound D: die States: E: enemy in sight S: sound audible D: dead D ~E E D ~S S D ESD Problem: Can’t go directly from attack to chase. Why not? Spawn xx1 Wander 000 Attack 1x0 Chase 010 start

53. Fall 201253 FSM (first-cut) Events: E: see an enemy S: hear a sound D: die States: E: enemy in sight S: sound audible D: dead D ~E E D ~S S D ESD Spawn xx1 Wander 000 Attack 100 Chase 010 start E Attack+S 110 ~E ~S S D Extra state needs to be defined

54. Fall 201254 Quake Bot Example (refined) Types of behavior to capture: Wander randomly if don ’ t see or hear an enemy When see enemy, attack When not see enemy and hear an enemy, chase enemy When die, respawn Extensions: When health is low and see an enemy, retreat When see power-ups during wandering, collect them [hierarchical FSM]

55. Fall 201255 Example FSM with Retreat Spawn D (-E,-S,-L) Wander -E,-D,-S,-L E -S Attack-E E,-D,-S,-L E Chase -E,-D,S,-L S D S D D Retreat-E E,-D,-S,L L -E Retreat-S -E,-D,S,L Wander-L -E,-D,-S,L Retreat-ES E,-D,S,L Attack-ES E,-D,S,-L E E -E -L S -S L -E E L -L L D States: –E: enemy in sight –S: sound audible –D: dead –L: Low health A lot more states got added

56. Fall 201256 Hierarchical FSM Example Note: This is not a complete FSM All links between top level states still exist Need more states for wander Start Turn Right Go-through Door Pick-up Powerup Wander Attack Chase Spawn ~E E ~S S D ~E

57. Fall 201257 Non-Deterministic Hierarchical FSM (Markov Model) Adds variety to actions Have multiple transitions for the same event Label each with a probability that it will be taken Randomly choose a transition at run-time Markov Model: New state only depends on the previous state Attack Start Approach Aim & Jump & Shoot Aim & Slide Left & Shoot Aim & Slide Right & Shoot.3.4.3.4