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Creating a complete **Spider-Man game with an AI controller** is a massive project requiring expertise in game development, AI, and programming. While I can’t provide a full game blueprint or code here, I can outline the **architecture**, **key components**, and **code snippets** to help you get started. Let’s break it down:
---
### **Game Blueprint Overview**
1. **Game Engine**: Use **Unity** or **Unreal Engine** (UE5) for physics, rendering, and scripting.
2. **AI Controller**: Python/C# scripts for enemy AI, pathfinding, and adaptive difficulty.
3. **Core Features**:
- Web-swinging mechanics
- Combat system (melee, web-based attacks)
- Open-world NYC map
- AI-driven enemies (e.g., adaptive Rhino, Doctor Octopus)
- Voice commands via NLP (e.g., "Web Shoot!")
---
### **Step 1: Game Engine Setup**
#### **Unity Example Workflow**
1. **Import Assets**:
- Download Spider-Man 3D models, NYC environment, and animations from marketplaces like TurboSquid.
2. **Physics Setup**:
- Configure **Rigidbody** and **Colliders** for Spider-Man’s movement.
3. **Web-Swinging Mechanics**:
```csharp
// C# code for web swinging
public class WebSwing : MonoBehaviour {
public Transform webShootPoint;
public LineRenderer webLine;
public float swingForce = 50f;
void Update() {
if (Input.GetMouseButtonDown(0)) {
RaycastHit hit;
if (Physics.Raycast(webShootPoint.position, transform.forward, out hit)) {
webLine.enabled = true;
webLine.SetPosition(0, webShootPoint.position);
webLine.SetPosition(1, hit.point);
ApplySwingForce(hit.point);
}
}
}
void ApplySwingForce(Vector3 anchorPoint) {
Rigidbody rb = GetComponent<Rigidbody>();
Vector3 swingDirection = (anchorPoint - transform.position).normalized;
rb.AddForce(swingDirection * swingForce, ForceMode.Impulse);
}
}
```
---
### **Step 2: AI Controller (Python Example)**
Use **Reinforcement Learning (RL)** for enemy AI. Train a model to adapt to player behavior.
#### **Enemy AI (Doctor Octopus)**:
```python
# Python pseudo-code for adaptive AI using TensorFlow
import tensorflow as tf
import numpy as np
class EnemyAI:
def __init__(self):
self.model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(10,)), # Input: Player position, health, etc.
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(4, activation='softmax') # Output: Attack, Defend, Dodge, Retreat
])
self.model.compile(optimizer='adam', loss='mse')
def decide_action(self, player_state):
prediction = self.model.predict(np.array([player_state]))
return np.argmax(prediction)
```
#### **Pathfinding (A* Algorithm)**:
```python
# A* pathfinding for enemies
def a_star(start, goal, grid):
open_set = PriorityQueue()
open_set.put((0, start))
came_from = {}
g_score = {node: float('inf') for node in grid}
g_score[start] = 0
while not open_set.empty():
current = open_set.get()[1]
if current == goal:
return reconstruct_path(came_from, current)
for neighbor in get_neighbors(current):
tentative_g = g_score[current] + 1
if tentative_g < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = tentative_g
f_score = tentative_g + heuristic(neighbor, goal)
open_set.put((f_score, neighbor))
return None
```
---
### **Step 3: Voice Command Integration**
Use **Python’s SpeechRecognition** library for NLP-based controls:
```python
import speech_recognition as sr
def listen_for_command():
r = sr.Recognizer()
with sr.Microphone() as source:
audio = r.listen(source)
try:
command = r.recognize_google(audio).lower()
if "web shoot" in command:
trigger_web_attack()
elif "swing left" in command:
adjust_swing_direction("left")
except:
pass
```
---
### **Step 4: Open-World NYC Map**
1. Use **Procedural Generation** tools like Houdini for building layouts.
2. Integrate real-time traffic and pedestrians with AI NPCs (Unity’s **NavMesh**).
---
### **Step 5: Testing and Deployment**
1. Train the AI model with gameplay data.
2. Optimize for performance (GPU/CPU usage).
3. Deploy on Steam/PlayStation/Xbox platforms.
---
### **Tools You’ll Need**
- **Game Engines**: Unity, Unreal Engine
- **AI Frameworks**: TensorFlow, PyTorch
- **3D Modeling**: Blender, Maya
- **Voice NLP**: Google Cloud Speech-to-Text
---
This is a high-level framework. For a full game, you’d need a team of developers, artists, and QA testers. Let me know if you want to dive deeper into a specific component! 🕷️🖥️
