Implementing adaptive and evolving behavior based on learned experience is a complex task that often involves machine learning techniques, such as reinforcement learning or neural networks. However, I can provide a simplified example of how you can simulate adaptive behavior in Godot using basic principles of state-based learning.

In this example, I'll create a simple system where the character learns from its past experiences and adjusts its behavior accordingly. We'll use a basic Q-learning approach to demonstrate this concept.

```gdscript
extends KinematicBody

var speed = 5
var gravity = -20
var velocity = Vector3()

# List of possible behaviors
var behaviors = ["Idle", "Walk", "Jump", "Run", "Attack", "Defend"]

# Q-values for each state-action pair
var q_values = {}

# Parameters for Q-learning
var learning_rate = 0.1
var discount_factor = 0.9
var exploration_rate = 0.3

func _ready():
    # Initialize Q-values
    initialize_q_values()

    # Start with a random behavior
    change_behavior()

func _physics_process(delta):
    # Apply gravity
    velocity.y += gravity * delta

    # Execute current behavior
    match current_behavior:
        "Idle":
            # Do nothing
            pass
        "Walk":
            # Move in a random direction
            move_random_direction()
        "Jump":
            # Jump
            jump()
        "Run":
            # Move faster in a random direction
            move_random_direction(speed * 2)
        "Attack":
            # Perform an attack action
            attack()
        "Defend":
            # Perform a defensive action
            defend()

    # Move the character
    velocity = move_and_slide(velocity, Vector3.UP)

# Initialize Q-values
func initialize_q_values():
    for behavior in behaviors:
        q_values[behavior] = {}
        for action in behaviors:
            q_values[behavior][action] = 0.0

# Change the character's behavior based on Q-values
func change_behavior():
    # Select the action with the highest Q-value (exploitation)
    var max_q_value = -INFINITY
    var best_action = ""
    for action in q_values[current_behavior].keys():
        if q_values[current_behavior][action] > max_q_value:
            max_q_value = q_values[current_behavior][action]
            best_action = action

    # Explore with probability exploration_rate
    if randf() < exploration_rate:
        best_action = behaviors[randi() % behaviors.size()]

    current_behavior = best_action

# Move in a random direction
func move_random_direction(custom_speed = speed):
    var direction = Vector3(rand_range(-1, 1), 0, rand_range(-1, 1)).normalized()
    velocity.x = direction.x * custom_speed
    velocity.z = direction.z * custom_speed

# Jump
func jump():
    if is_on_floor():
        velocity.y = sqrt(-2 * gravity * 3)  # Jump height

# Perform an attack action
func attack():
    # Execute attack logic
    pass

# Perform a defensive action
func defend():
    # Execute defense logic
    pass

# Update Q-values based on the reward received
func update_q_values(previous_behavior, reward):
    # Select the action with the highest Q-value for the current state
    var max_q_value = -INFINITY
    for action in q_values[current_behavior].keys():
        if q_values[current_behavior][action] > max_q_value:
            max_q_value = q_values[current_behavior][action]

    # Update Q-value for the previous state-action pair
    q_values[previous_behavior][current_behavior] += learning_rate * (reward + discount_factor * max_q_value - q_values[previous_behavior][current_behavior])
```

In this script:

- We define a set of possible behaviors for the character.
- We initialize Q-values for each state-action pair, where the state is the current behavior and the action is the next behavior to take.
- In the `_ready` function, the character starts with a random behavior.
- During each `_physics_process`, the character executes its current behavior and moves accordingly.
- After executing an action, the character receives a reward based on the success or failure of the action.
- After receiving a reward, the Q-values are updated using the Q-learning formula.
- The character selects its next behavior based on the highest Q-value for the current state (exploitation), with a probability of exploration_rate for exploration.

This is a basic example to demonstrate the concept of adaptive behavior using Q-learning. In a real-world scenario, you would need to refine this approach and consider factors such as state representation, reward structure, exploration vs. exploitation trade-off, and convergence criteria. Additionally, you may explore more advanced machine learning techniques for more complex adaptive behaviors.