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Antigravity Agent
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---
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id: wiki-2026-0508-finite-state-machines-fsm
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title: Finite State Machines (FSM)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [FSM, state machine, statechart, XState, hierarchical FSM, behavior tree]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.98
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verification_status: applied
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tags: [computer-science, fsm, state-machine, xstate, statechart, game-ai, control]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: TypeScript / Python
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framework: XState / Stateless / SCXML
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---
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# Finite State Machines (FSM)
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## 매 한 줄
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> **"매 finite state + 매 transition (event-driven)"**. 매 oldest CS abstraction. 매 modern: 매 statechart (Harel), 매 XState, 매 behavior tree (game). 매 game AI, 매 UI workflow, 매 protocol, 매 LLM agent의 control 의 사용.
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## 매 핵심
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### 매 component
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- **State**: 매 finite set.
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- **Initial state**.
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- **Transition**: state × event → state.
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- **Guard**: 매 transition condition.
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- **Action**: 매 transition / entry / exit 의 effect.
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### 매 variant
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- **DFA**: 매 deterministic.
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- **NFA**: 매 non-deterministic.
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- **Statechart** (Harel): 매 nested + parallel.
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- **HSM** (hierarchical).
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- **Behavior tree**: 매 game alternative.
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### 매 응용
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1. **Game AI**: 매 enemy behavior.
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2. **UI**: 매 form workflow.
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3. **Protocol**: 매 TCP, BLE.
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4. **Compiler**: 매 lexer.
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5. **LLM agent**: 매 conversation flow.
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6. **Saga**: 매 distributed transaction.
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## 💻 패턴
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### Simple FSM (TypeScript)
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```typescript
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type State = 'idle' | 'fetching' | 'success' | 'error';
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type Event = { type: 'FETCH' } | { type: 'RESOLVE'; data: any } | { type: 'REJECT' } | { type: 'RESET' };
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function transition(state: State, event: Event): State {
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switch (state) {
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case 'idle':
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if (event.type === 'FETCH') return 'fetching';
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break;
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case 'fetching':
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if (event.type === 'RESOLVE') return 'success';
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if (event.type === 'REJECT') return 'error';
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break;
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case 'success':
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case 'error':
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if (event.type === 'RESET') return 'idle';
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break;
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}
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return state;
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}
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```
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### XState (declarative)
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```typescript
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import { createMachine, assign, interpret } from 'xstate';
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const fetchMachine = createMachine({
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id: 'fetch',
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initial: 'idle',
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context: { data: null, error: null },
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states: {
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idle: {
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on: { FETCH: 'loading' },
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},
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loading: {
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invoke: {
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src: 'fetchData',
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onDone: { target: 'success', actions: assign({ data: (_, e) => e.data }) },
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onError: { target: 'failure', actions: assign({ error: (_, e) => e.data }) },
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},
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},
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success: {
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on: { REFRESH: 'loading' },
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},
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failure: {
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on: { RETRY: 'loading' },
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},
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},
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});
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const service = interpret(fetchMachine).start();
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service.send({ type: 'FETCH' });
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```
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### Hierarchical (statechart)
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```typescript
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const trafficLight = createMachine({
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initial: 'red',
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states: {
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red: {
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after: { 5000: 'green' },
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},
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green: {
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after: { 5000: 'yellow' },
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},
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yellow: {
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initial: 'flashing',
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states: {
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flashing: {},
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steady: {},
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},
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after: { 2000: 'red' },
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},
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},
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});
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```
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### Game AI FSM (Python)
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```python
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class EnemyAI:
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def __init__(self):
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self.state = 'patrol'
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def update(self, world):
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if self.state == 'patrol':
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if world.player_visible(self): self.state = 'chase'
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else: self.patrol_move()
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elif self.state == 'chase':
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if world.in_attack_range(self): self.state = 'attack'
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elif not world.player_visible(self): self.state = 'search'
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else: self.chase_player()
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elif self.state == 'attack':
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if not world.in_attack_range(self): self.state = 'chase'
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else: self.attack()
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elif self.state == 'search':
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if world.search_timeout(): self.state = 'patrol'
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elif world.player_visible(self): self.state = 'chase'
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else: self.search_area()
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```
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### Behavior tree (alternative)
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```python
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class Node:
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def tick(self): raise NotImplementedError
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class Sequence(Node):
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def __init__(self, *children): self.children = children
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def tick(self):
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for c in self.children:
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if c.tick() != 'success': return 'fail'
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return 'success'
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class Selector(Node):
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def __init__(self, *children): self.children = children
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def tick(self):
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for c in self.children:
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if c.tick() == 'success': return 'success'
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return 'fail'
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ai = Selector(
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Sequence(IsPlayerVisible(), AttackPlayer()),
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PatrolArea(),
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)
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```
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### Lexer (FSM)
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```python
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def lex(input):
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state = 'start'
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tokens = []
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buf = ''
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for ch in input + ' ':
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if state == 'start':
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if ch.isalpha(): state, buf = 'ident', ch
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elif ch.isdigit(): state, buf = 'number', ch
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elif state == 'ident':
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if ch.isalnum(): buf += ch
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else: tokens.append(('IDENT', buf)); state, buf = 'start', ''
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elif state == 'number':
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if ch.isdigit(): buf += ch
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else: tokens.append(('NUMBER', int(buf))); state, buf = 'start', ''
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return tokens
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```
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### LLM agent state machine
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```typescript
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const agent = createMachine({
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initial: 'understanding',
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states: {
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understanding: {
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on: { CLEAR: 'planning', NEEDS_INFO: 'asking' },
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},
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asking: {
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on: { ANSWERED: 'understanding' },
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},
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planning: {
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on: { READY: 'executing' },
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},
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executing: {
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on: { COMPLETE: 'reflecting', NEED_REPLAN: 'planning', BLOCKED: 'asking' },
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},
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reflecting: {
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on: { OK: 'done', RETRY: 'planning' },
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},
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done: { type: 'final' },
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},
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});
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```
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### Saga (distributed transaction)
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```typescript
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const orderSaga = createMachine({
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initial: 'reserving',
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context: { compensations: [] },
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states: {
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reserving: {
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invoke: {
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src: 'reserveInventory',
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onDone: { target: 'charging', actions: 'pushCompensation' },
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onError: 'failed',
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},
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},
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charging: {
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invoke: {
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src: 'chargeCard',
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onDone: 'completed',
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onError: { target: 'compensating' },
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},
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},
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compensating: {
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invoke: {
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src: 'runCompensations',
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onDone: 'failed',
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},
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},
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completed: { type: 'final' },
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failed: { type: 'final' },
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},
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});
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```
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### State persistence
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```typescript
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const persistedState = JSON.stringify(service.getSnapshot());
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localStorage.setItem('appState', persistedState);
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// 매 restore
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const restored = JSON.parse(localStorage.getItem('appState')!);
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const restoredService = interpret(machine, { state: restored }).start();
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```
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### Visualizer (XState Inspector)
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```typescript
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import { inspect } from '@xstate/inspect';
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inspect({ url: 'https://stately.ai/viz?inspect', iframe: false });
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const service = interpret(machine, { devTools: true }).start();
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```
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### Test (path coverage)
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```typescript
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import { createModel } from '@xstate/test';
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const testModel = createModel(machine).withEvents({
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FETCH: { exec: async () => fireEvent.click(fetchBtn) },
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RESOLVE: { ... },
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});
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testModel.getSimplePathPlans().forEach(plan => {
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describe(plan.description, () => {
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plan.paths.forEach(path => {
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it(path.description, async () => {
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await path.test(/* page */);
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});
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});
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});
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});
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```
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## 매 결정 기준
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| 상황 | Approach |
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| UI workflow | XState |
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| Game AI | FSM or Behavior Tree |
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| Lexer / parser | DFA |
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| Distributed | Saga (XState compatible) |
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| LLM agent | Statechart |
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| Tiny linear | Plain switch |
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**기본값**: 매 XState (TS) + 매 statechart (hierarchical) + 매 visualizer + 매 model-based test.
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## 🔗 Graph
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- 변형: [[Statechart]] · [[Hierarchical-FSM]] · [[Behavior-Tree]]
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- Adjacent: [[Exhaustiveness-Checking]] · [[Encapsulation-of-Domain-Invariants]] · [[Event-Driven-Architecture]]
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## 🤖 LLM 활용
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**언제**: 매 multi-step workflow. 매 game AI. 매 protocol.
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**언제 X**: 매 1-2 state.
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## ❌ 안티패턴
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- **Boolean explosion**: 매 N flags 의 implicit FSM.
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- **Hidden FSM**: 매 spaghetti.
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- **No exhaustive transition**: 매 invalid state.
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- **State + global side effect**: 매 untestable.
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- **Reinvent XState**: 매 use library.
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## 🧪 검증 / 중복
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- Verified (Harel statecharts 1987, XState docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-04-26 | FSM auto |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — FSM + 매 XState / hierarchical / game / saga / LLM agent code |
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