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Finite State Machine for Games

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Presentation on theme: "Finite State Machine for Games"— Presentation transcript:

1 Finite State Machine for Games
Mohammad Zikky, M.T Ref: Chenney, CS679 lectures AI Game Programming Wisdom 2

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

3 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 AI in the Game Loop AI is updated as part of the game loop, after user input, and before rendering

5 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 AI Module Sensing Game Engine Thinking Acting Fall 2012

6 AI by Polling Senses: then acts on it
AI gets called at a fixed rate 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

7 Event Driven AI Events triggered by a message Example messages:
does everything in response to events in the world 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 2012

8 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 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 Fall 2012

10 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 Fall 2012

11 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 Fall 2012

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

13 Vending Machine: state diagram

14 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 FSM Examples Fall 2012

16 Fall 2012

17 Fall 2012


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

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

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

22 Predator (Raptor) Tidle > 5 Tdining>5 Dining Prey captured
(stand) Tidle > 5 Tdining>5 Hungry (wander) Dining Prey captured Prey in sight Pursuit (run) Tpursuit > 10 Fall 2012

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

24 FSM Design

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

26 Quake: Rocket Fall 2012

27 Shambler monster Fall 2012

28 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 Fall 2012

29 FSM Disadvantages Number of states can grow very fast
Exponentially with number of events: s=2e Number of arcs can grow even faster: a=s2 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 Fall 2012

30 FSM Control System Implementation

31 FSM Implementation Fall 2012

32 Previous Example Fall 2012

33 Code 1 Ad hoc implementation Fall 2012

34 Code 1p Fall 2012

35 Code 2 Structure, Readable, maintainable Fall 2012

36 Hierarchical … Fall 2012

37 FSM Extensions

38 Advanced Issues Hierarchical FSM Non-deterministic FSM Swapping FSM
Fall 2012

39 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 Fall 2012

40 Stack-based FSM Pushdown automaton (PDA)
History stack: Remember previous state; create characters with a memory … Fall 2012

41 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. Fall 2012

42 Processing Models Polling Event-driven 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 Fall 2012

43 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 Fall 2012

44 An Example FSM in Football Game

45 Class : Sepakbola Lapangan
Jadwal Pertandingan (Liga, Tournament, 2x45 mnt) Skuad Pemain (11 di lapangan + cadangan) Taktik & Strategi (4-4-2 Menyerang / Bertahan) Latihan Staff Pemain (Pelatih , Ass Pelatih, Dokter) Keuangan Transfer Pemain

46 Object : Pemain Bola Technicals Intellegents Physicals Temperaments
Cetak Gol, Giring Bola, Tangkap Bola (penjaga Gawang), Control Bola, Tackling Bola, Tendang Jarak Jauh, Marking, dll

47 FSM Secara Global dlm S.Bola

48 Class : Sepakbola Keterangan CG : Cetak Gol
MM : Menjaga Lawan (Man Marking) TB : Tangkap Bola (Penjaga Gawang) MZ : Menjaga Daerah (Zona Marking) US : Umpan Silang SB : Sundul Bola UP : Umpan Pendek GB : Giring Bola UL : Umpan Lambung TJ : Tendangan Jarak Jauh LMD : Lari Maju ke depan TC : Tendangan Pojok LMBC : Lari Mundur ke Belakang AM : Mental Menyerang TP : Tendangan Pinalti DM : Mental Bertahan T : Tackling / Jegal LMB : Lari Kejar Bola LB : Lempar Bola

49 Object : Penjaga Gawang
Tangkap ~M, K ~M, K Umpan Lambung Umpan Pendek ~M, K M, K M, K ~M,K Tendangan Gawang Mental Bertahan Dekati Bola

50 Object : Penjaga Gawang
M = Musuh Mendekat ~M = Musuh Menjauh K = Teman Mendekat ~K = Teman Menjauh

51 Object : Pemain Bertahan (Tengah)
Umpan Lambung M,~M,K,~K Umpan Pendek Tackling : Keras M Jemput Bola Dapat Bola Marking=Zona Mental : Bertahan M

52 Object : Pemain Bertahan (SAYAP)
Umpan Lambung Takling : Normal Menggiring Bola Maju Jemput Bola Dapat Bola Umpan Pendek Mental : Normal

53 Object : Gelandang Bertahan
Umpan Lambung Takling : Normal Menggiring Bola Maju Tendangan Jrk Jauh Jemput Bola Dapat Bola Umpan Pendek

54 Object : Gelandang Serang
Umpan Lambung Takling : Ringan Menggiring Bola Maju Tendangan Jrk Jauh Jemput Bola Dapat Bola Umpan Pendek

55 Object : Sayap Kanan / Kiri
Umpan Silang Umpan Lambung Umpan Pendek Dapat Bola Jemput Bola Menggiring Bola Maju Tackling : Ringan

56 Object : Penyerang (Striker)
Giring Bola Umpan Pendek Umpan Silang Cetak GOL Tendangan Jrk Jauh Jemput Bola Dapat Bola

57 References Web references: Game Programming Gems Sections 3.0 & 3.1 Game Programming Gems Sections 3.0 & 3.1 It’s very very detailed, but also some cute programming Fall 2012

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