Behaviours

Behaviours determine how enemies behave and react in the game world via a modular behaviour tree.

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Overview

Enemy behaviours are defined through a Behaviour Tree that controls how the AI transitions between different 'states' such as Idle, Alerted and Target Interaction. The behaviour tree acts as the decision-making backbone, combining modular tasks into fluid and reactive enemy behaviour.

Each behaviour is built as a reusable subtree that can be plugged into the main tree, making it easy to extend or customise for new enemy types. By combining these trees, developers can create enemies with varied personalities and strategies without rewriting core logic.

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Behaviour Tree Assets

Behaviour trees form the core of all AI decision-making processes within the Enemy AI - Toolkit. At the highest level is the BT_Enemy behaviour tree graph. This contains different subgraphs, each representing a different 'state': Target Interaction, Alerted and Idle. Each of these states is represented by a separate behaviour tree graph. While the main BT_Enemy graph decides which state to execute based on the current world state, as represented by the blackboard, the nested behaviours handle the active decision-making. The following table explains all behaviour tree graphs:

Name	Type	Description
BT Enemy	Main	Handles the highest level of decision-making. Decides which general state to execute.
BT Target	Nested	Handles concrete target interaction, such as movement towards the target and interaction with the combat system.
BT Alerted	Nested	Handles investigation logic. It is triggered when the AI loses a target. It lets the AI investigate the last known target area.
BT Idle	Nested	It handles the idle decision-making process. It is executed when there is no target and nothing to investigate. It handles waiting, roaming or patrolling based on the selected enemy's idle mode.

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Blackboard - World State

All enemy behaviour is based on decision-making through behaviour tree graphs. However, behaviour trees base their decision-making on the current world state, which is represented by the Blackboard. A blackboard essentially acts as a data container. Generally, there is one for each instance of your enemy agent. The AI controller writes data into the blackboard while the behaviour trees read data from it in order to make decisions. The following table provides a brief overview of all the data listed in the blackboard:

General

Contains general data that is used troughout the entire behaviour tree collection. Used to manage high level info or often used data.

Name	Description
Self Actor	References the actor of the agent. Default in any blackboard.
Origin Location	Holds the origin location. Selected by the AI controller, when the enemy is started. Takes agents postion at moment of execution.
Target	Defines the actor currently selected as the enmies target.
Idle Mode	Defines what mode the enemy should try to execute while within idle state.
Alerted	Defines if the enemy is currently alerted. Example: No target but still needs to investigate environment.

Roaming

Contains data used to fine-tune the enemies roaming behaviour on a per instance level. Holds lots of values set in the config, written into the blackboard by the AI Controller.

Name	Description
Roaming Location	Holds the location where the agent should move while roaming. Set by environment queries in each roaming iteration.
Roam Wait Time	Defines how long the agent should wait between each roaming iteration.
Min. Roaming Radius	Describes the minimum distance (from agent to new location). Used by EQS to gather next roaming location.
Max. Roaming Radius	Describes the maximum distance (from agent to new location). Used by EQS to gather next roaming location.

Patrolling

Contains data used to fine-tune the enemies patrolling behaviour on a per instance level. Holds lots of values set in the config, written into the blackboard by the AI Controller.

Name	Description
Route	The patrol route that the enemy should patrol along. Used to gather patrol points & locations.
Point Index	The current index representing the point along the patrol route.
Point Location	The location of the patrol point in world space. Used as the agents movement location.
Skip Point	If the next point should be skipped. Can happen randomly by a chance set in the enemy config.
Patrol Waiting Time	The waiting time at each (not skipped) patrol point in seconds.

Investigation

Contains data used to support the investigation of areas while the AI is within alterted state. Holds lots of values set in the config, written into the blackboard by the AI Controller.

Name	Description
Investigation Center	Defines the center of the area to investigate. For example the last known position of a lost target.
Investigation Iterations	Defines how many iterations of random investigation locations should be generated.
Investigation Min. Radius	Describes the minimum distance (from agent to new location). Used by EQS to gather next roaming location.
Investigation Max. Radius	Describes the maximal distance (from agent to new location). Used by EQS to gather next roaming location.
Investigation Waiting Time	Defines how long the agent should wait between each investigation iteration.
Investigation Iteration Location	Holds the new investigation location for the agent to move to.

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Nodes

The Enemy AI - Toolkit comes with several custom behaviour graph nodes. These nodes are used to deploy custom behaviour tailored to this system's exact use cases directly into the behaviour tree graph. The table below provides a quick overview of all the newly added nodes:

Name	Type	Description
Get Patrol Point	Task	This allows you to reach the next patrol point along the selected patrol route.
Perform Attack	Task	Performs a weighted attack, based from all attacks set in the enemy config.
Set Bool	Service	Sets a bool within the world state. Can be attached to other nodes in the behaviour tree graph.
Mode Comparer	Decorator	Compares the currently active mode with a custom value.