2nd/10_Wiki/Topics/AI_and_ML/Goal-Oriented-Action-Planning.md

---
id: wiki-2026-0508-goal-oriented-action-planning
title: Goal-Oriented Action Planning (GOAP)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [GOAP, action planning, F.E.A.R AI, STRIPS, HTN, behavior tree alternative]
duplicate_of: none
source_trust_level: A
confidence_score: 0.92
verification_status: applied
tags: [game-ai, planning, goap, strips, htn, action-planning, npc]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: C# / C++ / Python
  applicable_to: [Game AI, Planning, NPC]
---

# Goal-Oriented Action Planning (GOAP)

## 매 한 줄
> **"매 NPC 의 goal 의 의 의 action sequence 의 plan"**. F.E.A.R. (Monolith 2005) 매 famous. 매 STRIPS-derived. 매 modern alternative: 매 behavior tree, HTN, utility AI. 매 LLM 의 action plan 의 also similar.

## 매 핵심

### 매 component
- **Goal**: 매 desired world state.
- **Action**: 매 (precondition, effect, cost).
- **Planner**: 매 A* search 의 의 의 actions.
- **WorldState**: 매 current facts.

### 매 vs alternatives
- **FSM**: 매 hardcoded transitions.
- **Behavior Tree**: 매 reactive, designer-friendly.
- **GOAP**: 매 emergent, dynamic plan.
- **HTN**: 매 hierarchical task decompose.
- **Utility AI**: 매 score actions.

### 매 응용
1. **Game AI** (FPS, RTS).
2. **Robotics planning**.
3. **NPC complex behavior**.
4. **Strategy game**.
5. **LLM agent (similar pattern)**.

## 💻 패턴

### Action definition
```python
@dataclass
class Action:
    name: str
    cost: float
    preconditions: dict  # 매 state requirements
    effects: dict  # 매 state changes
    
    def can_run(self, world_state):
        return all(world_state.get(k) == v for k, v in self.preconditions.items())
    
    def apply(self, world_state):
        new = world_state.copy()
        new.update(self.effects)
        return new
```

### Goal definition
```python
@dataclass
class Goal:
    name: str
    desired_state: dict
    priority: float
    
    def is_satisfied(self, world_state):
        return all(world_state.get(k) == v for k, v in self.desired_state.items())
```

### A* planner
```python
import heapq

def goap_plan(world_state, goal, actions):
    queue = [(0, world_state, [])]
    visited = set()
    
    while queue:
        cost, state, path = heapq.heappop(queue)
        state_key = frozenset(state.items())
        if state_key in visited: continue
        visited.add(state_key)
        
        if goal.is_satisfied(state): return path
        
        for action in actions:
            if action.can_run(state):
                new_state = action.apply(state)
                heuristic = compute_heuristic(new_state, goal)
                heapq.heappush(queue, (cost + action.cost + heuristic, new_state, path + [action]))
    
    return None  # 매 no plan
```

### Heuristic (number of unsatisfied goal facts)
```python
def compute_heuristic(state, goal):
    return sum(1 for k, v in goal.desired_state.items() if state.get(k) != v)
```

### Example: combat AI
```python
ACTIONS = [
    Action('reload', cost=1, preconditions={'has_weapon': True, 'has_ammo': True}, effects={'weapon_loaded': True}),
    Action('grab_ammo', cost=2, preconditions={'near_ammo': True}, effects={'has_ammo': True}),
    Action('fire', cost=1, preconditions={'weapon_loaded': True, 'enemy_visible': True}, effects={'enemy_dead': True, 'weapon_loaded': False}),
    Action('take_cover', cost=3, preconditions={'cover_available': True}, effects={'in_cover': True}),
]

GOAL_KILL = Goal('kill_enemy', {'enemy_dead': True}, priority=1.0)

world = {'has_weapon': True, 'has_ammo': False, 'near_ammo': True, 'enemy_visible': True, 'cover_available': True}
plan = goap_plan(world, GOAL_KILL, ACTIONS)
# 매 → [grab_ammo, reload, fire]
```

### Reactive replanning
```python
class GOAPAgent:
    def __init__(self, actions):
        self.actions = actions
        self.current_plan = []
        self.current_goal = None
    
    def update(self, world_state):
        # 매 select goal
        goals = self.evaluate_goals(world_state)
        new_goal = max(goals, key=lambda g: g.priority)
        
        # 매 replan if goal changed or plan invalid
        if new_goal != self.current_goal or not self.plan_valid(world_state):
            self.current_plan = goap_plan(world_state, new_goal, self.actions)
            self.current_goal = new_goal
        
        if self.current_plan:
            return self.current_plan.pop(0)
        return None
```

### Cost dynamic adjustment
```python
def dynamic_cost(action, world_state):
    base = action.cost
    if action.name == 'fire' and world_state['low_ammo']: return base * 3
    if action.name == 'take_cover' and world_state['health'] < 30: return base * 0.3
    return base
```

### HTN (Hierarchical Task Network)
```python
class Task:
    def __init__(self, name, methods):
        self.name = name; self.methods = methods  # 매 list of decomposition

class Method:
    def __init__(self, preconditions, subtasks):
        self.preconditions = preconditions; self.subtasks = subtasks

# 매 decompose top task → primitive actions
```

### Behavior Tree (alternative)
```python
class Selector:
    def tick(self):
        for child in self.children:
            if child.tick() == 'success': return 'success'
        return 'fail'

class Sequence:
    def tick(self):
        for child in self.children:
            if child.tick() != 'success': return 'fail'
        return 'success'

# 매 attack tree
attack = Sequence([HasAmmo(), HasWeapon(), Selector([Fire(), Reload()])])
```

### Utility AI (alternative)
```python
def utility_decide(actions, world_state):
    scores = []
    for action in actions:
        if not action.can_run(world_state): continue
        score = sum(consider.score(world_state) for consider in action.considerations)
        scores.append((action, score))
    return max(scores, key=lambda x: x[1])[0]
```

### LLM agent (similar pattern)
```python
def llm_plan(goal, world_state, available_tools, llm):
    prompt = f"""You are a planner. Goal: {goal}
Current state: {world_state}
Tools: {available_tools}

Output a plan (JSON list of tool invocations) to achieve the goal."""
    return json.loads(llm.generate(prompt))
```

### Plan visualization
```python
def visualize_plan(plan, world_state):
    state = world_state.copy()
    for i, action in enumerate(plan):
        print(f'{i+1}. {action.name} (cost {action.cost})')
        state = action.apply(state)
    print(f'Final state: {state}')
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Complex emergent NPC | GOAP |
| Designer-driven | Behavior Tree |
| Hierarchical task | HTN |
| Score-based | Utility AI |
| Modern flexible | LLM agent |
| Simple | FSM |

**기본값**: 매 GOAP for emergent + 매 BT for reactive + 매 HTN for hierarchical + 매 LLM for flexible/data-rich.

## 🔗 Graph
- 부모: [[Planning]]
- 변형: [[Behavior-Tree]] · [[HTN]]
- 응용: [[F.E.A.R-AI]]
- Adjacent: [[STRIPS]] · [[Drama Management Systems]] · [[Foundation-Models]]

## 🤖 LLM 활용
**언제**: 매 game NPC. 매 robot planning.
**언제 X**: 매 simple linear behavior.

## ❌ 안티패턴
- **Plan once + execute blind**: 매 dynamic world fail.
- **Action explosion**: 매 search slow.
- **No replanning**: 매 stale.
- **GOAP for trivial NPC**: 매 over-engineer.

## 🧪 검증 / 중복
- Verified (F.E.A.R AI postmortem, Orkin, GOAP literature).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-20 | Auto |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — GOAP + 매 A* / BT / HTN / utility / LLM agent code |