What is a behavior tree?

Understanding Behavior Trees in AI

A behavior tree is a hierarchical model for structuring decision-making logic in an agent. Each node in the tree represents an action or a condition, and conditions determine whether an action executes. This lets a designer build layered, reactive behavior, such as a video game guard character who checks a series of conditions, like line of sight or alert status, before deciding whether to engage.

A Sequence node runs its children in order and succeeds only if every child succeeds; a Selector tries children until one succeeds; and a Decorator modifies how a child executes or reports its result. For example, a guard Selector can first try a Sequence containing “player visible” and “attack,” then fall back to a “patrol” action.

Behavior trees are modular: new behaviors can be added or rearranged as branches without rewriting existing logic, which is one reason they are often used in place of finite state machines as a design grows. Their tree structure also makes decision flows easier to follow visually, and traversal typically only evaluates the nodes relevant to the current path rather than checking every possible state.

Behavior trees also have tradeoffs. Large trees can become complex to design and debug, and evaluating deep or wide trees adds computational overhead. Building an effective one requires a clear understanding of the agent's goals, the environment it operates in, and testing to confirm it behaves as intended.

Behavior trees are common in video games for non-player character (NPC) behavior and also appear in robotics and automated planning. Structurally, they are a network of action and condition nodes connected by edges, with the root node representing the agent's overall goal. An agent traverses the tree from root to leaf, executing an action only if every condition along that path is satisfied.