limboai/doc/getting-started.md

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Getting Started

🛈 See also: How to get LimboAI.

TL;DR

  • To create your own actions, extend the BTAction class.
  • To create your own conditions, extend the BTCondition class.
  • In-editor class documentation is available. Try searching BTTask and BehaviorTree.
  • Add a script template with "Misc → Create script template".

Introduction to Behavior Trees

Behavior Trees (BT) are hierarchical structures used to model and control the behavior of agents in a game (e.g., characters, enemies, entities). They are designed to make it easier to create complex and highly modular behaviors for your games.

Behavior Trees are composed of tasks that represent specific actions or decision-making rules. Tasks can be broadly categorized into two main types: control tasks and leaf tasks. Control tasks determine the execution flow within the tree. They include BTSequence, BTSelector, and BTInvert. Leaf tasks represent specific actions to perform, like moving or attacking, or conditions that need to be checked. The BTTask class provides the foundation for various building blocks of the Behavior Trees. BT tasks can share data with the help of Blackboard.

🛈 Note: To create your own actions, extend the BTAction class.

The BehaviorTree is executed from the root task and follows the rules specified by the control tasks, all the way down to the leaf tasks, which represent the actual actions that the agent should perform or conditions that should be checked. Each task returns a status when it is executed. It can be SUCCESS, RUNNING, or FAILURE. These statuses determine how the tree progresses. They are defined in BT.Status.

Behavior Trees handle conditional logic using condition tasks. These tasks check for specific conditions and return either SUCCESS or FAILURE based on the state of the agent or its environment (e.g., "IsLowOnHealth", "IsTargetInSight"). Conditions can be used together with BTSequence and BTSelector to craft your decision-making logic.

🛈 Note: To create your own conditions, extend the BTCondition class.

Check out the BTTask class documentation in the editor, which provides the foundation for various building blocks of Behavior Trees.

Creating custom tasks in GDScript

🛈 Note: You can add a script template to your project with "Misc → Create script template" menu option.

Task anatomy

@tool
extends BTAction

# Task parameters.
@export var parameter1: float
@export var parameter2: Vector2

## Note: Each method declaration is optional.
## At minimum, you only need to define the "_tick" method.

# Called to generate a display name for the task (requires @tool).
func _generate_name() -> String:
    return "MyTask"

# Called to initialize the task.
func _setup() -> void:
    pass

# Called when task is entered.
func _enter() -> void:
    pass

# Called when task is exited.
func _exit() -> void:
    pass

# Called each time this task is ticked (aka executed).
func _tick(delta: float) -> Status:
    return SUCCESS

Custom task example

@tool
extends BTCondition

## InRange condition checks if the agent is within a range of target, defined by
## distance_min and distance_max.
## Returns SUCCESS if agent is within the defined range;
## otherwise, returns FAILURE.

@export var distance_min: float
@export var distance_max: float
@export var target_var := "target"

var _min_distance_squared: float
var _max_distance_squared: float


# Called to generate a display name for the task.
func _generate_name() -> String:
	return "InRange (%d, %d) of %s" % [distance_min, distance_max,
			LimboUtility.decorate_var(target_var)]


# Called to initialize the task.
func _setup() -> void:
	_min_distance_squared = distance_min * distance_min
	_max_distance_squared = distance_max * distance_max


# Called when task is executed.
func _tick(_delta: float) -> int:
	var target: Node2D = blackboard.get_var(target_var, null)
	if not is_instance_valid(target):
		return FAILURE

	var dist_sq: float = agent.global_position.distance_squared_to(target.global_position)
	if dist_sq >= _min_distance_squared and dist_sq <= _max_distance_squared:
		return SUCCESS
	else:
		return FAILURE