Unity ile AI Destekli Oyun Geliştirme

# Unity ile AI Destekli Oyun Geliştirme

Unity'de AI entegrasyonu oyun deneyimini bambaşka bir seviyeye taşıyor. Bu yazıda, Unity'de AI destekli NPC'ler ve akıllı oyun mekaniği nasıl geliştirileceğini öğreneceksiniz.

## Unity AI Araçları

### 1. Unity ML-Agents
- Machine learning tabanlı AI
- Reinforcement learning desteği
- Python entegrasyonu

### 2. Unity NavMesh
- Pathfinding algoritmaları
- Dynamic obstacle avoidance
- Multi-agent navigation

### 3. Behavior Trees
- Visual scripting
- Complex AI behaviors
- State management

## AI Destekli NPC Geliştirme

### 1. Temel AI Controller

csharp
using UnityEngine;
using UnityEngine.AI;

public class AIController : MonoBehaviour
{
    private NavMeshAgent agent;
    private Transform player;
    private Animator animator;
    
    [Header("AI Settings")]
    public float detectionRange = 10f;
    public float attackRange = 2f;
    public float patrolSpeed = 2f;
    public float chaseSpeed = 5f;
    
    private AIState currentState;
    
    public enum AIState
    {
        Patrol,
        Chase,
        Attack,
        Idle
    }
    
    void Start()
    {
        agent = GetComponent();
        animator = GetComponent();
        player = GameObject.FindWithTag("Player").transform;
        
        currentState = AIState.Patrol;
    }
    
    void Update()
    {
        switch (currentState)
        {
            case AIState.Patrol:
                PatrolBehavior();
                break;
            case AIState.Chase:
                ChaseBehavior();
                break;
            case AIState.Attack:
                AttackBehavior();
                break;
        }
    }
    
    void PatrolBehavior()
    {
        // Patrol logic
        if (Vector3.Distance(transform.position, player.position) < detectionRange)
        {
            currentState = AIState.Chase;
        }
    }
    
    void ChaseBehavior()
    {
        agent.SetDestination(player.position);
        agent.speed = chaseSpeed;
        
        if (Vector3.Distance(transform.position, player.position) < attackRange)
        {
            currentState = AIState.Attack;
        }
    }
    
    void AttackBehavior()
    {
        // Attack logic
        if (Vector3.Distance(transform.position, player.position) > attackRange)
        {
            currentState = AIState.Chase;
        }
    }
}

### 2. Machine Learning Entegrasyonu

csharp
using Unity.MLAgents;
using Unity.MLAgents.Sensors;
using Unity.MLAgents.Actuators;

public class MLAgent : Agent
{
    [Header("ML Settings")]
    public float moveSpeed = 5f;
    public float rotationSpeed = 100f;
    
    private Rigidbody rb;
    private Transform target;
    
    public override void Initialize()
    {
        rb = GetComponent();
        target = GameObject.FindWithTag("Target").transform;
    }
    
    public override void OnEpisodeBegin()
    {
        // Reset agent position
        transform.localPosition = new Vector3(0, 0.5f, 0);
        rb.velocity = Vector3.zero;
    }
    
    public override void CollectObservations(VectorSensor sensor)
    {
        // Add observations
        sensor.AddObservation(transform.localPosition);
        sensor.AddObservation(target.localPosition);
        sensor.AddObservation(rb.velocity);
    }
    
    public override void OnActionReceived(ActionBuffers actions)
    {
        // Process actions
        float moveX = actions.ContinuousActions[0];
        float moveZ = actions.ContinuousActions[1];
        
        Vector3 movement = new Vector3(moveX, 0, moveZ) * moveSpeed;
        rb.AddForce(movement);
        
        // Reward system
        float distanceToTarget = Vector3.Distance(transform.localPosition, target.localPosition);
        if (distanceToTarget < 1.5f)
        {
            SetReward(1.0f);
            EndEpisode();
        }
        else
        {
            SetReward(-0.01f);
        }
    }
}

## Gelişmiş AI Teknikleri

### 1. Behavior Trees ile Kompleks AI
- Visual scripting
- State transitions
- Conditional behaviors

### 2. Neural Networks
- Custom AI models
- Training data collection
- Model optimization

### 3. Multi-Agent Systems
- Swarm intelligence
- Collaborative behaviors
- Emergent gameplay

## Performance Optimizasyonu

### 1. AI Culling
- Distance-based activation
- LOD systems
- Batch processing

### 2. Memory Management
- Object pooling
- Garbage collection optimization
- Efficient data structures

## Sonuç

Unity'de AI geliştirme, oyun deneyimini zenginleştiren güçlü bir araçtır. Bu teknikleri kullanarak akıllı ve etkileşimli NPC'ler oluşturabilirsiniz.