Differences between BT and if-then systems and their applications

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Differences Between BT (Behavior Tree) and If-Then Systems

A Behavior Tree (BT) is a method for determining “how an agent should act now” using a tree-like structure. Simply put, it organizes “If condition A, do action B; if that fails, do action C…” in a clean and structured tree format.

However, this explanation alone makes it hard to distinguish BT from a simple if-then (rule-based) system.

An if-then system lists simple, flat rules of the form “if X, then do Y,” whereas BT uses a tree structure to combine conditions and actions hierarchically. This approach allows for intuitive understanding of overall logic and priority flow, making complex control structures much easier to manage.

For example, an if-then structure might look like this:

If an unknown area is detected:
    If other agents are nearby:
        Divide roles and explore collaboratively
    Else:
        Explore alone
Else:
    If model confidence is low:
        Recheck previous data points
    Else:
        Support other agents

At first glance, this seems organized like a tree through nested if-then statements. However, applying simple if-then structures like this creates several practical problems:

Specific Problems with if-then Systems

  • Deep Nesting Reduces Readability:
    When multiple conditions combine, the nesting gets deeper, making the structure harder to read. In the above example, checks for “unknown area,” “agent location,” and “model confidence” create more than four levels of nesting. This makes understanding the code’s intent and flow difficult and prone to bugs.
  • Explosion of Condition Combinations:
    If-then structures require manually writing branches for every combination of conditions. For example, adding conditions like “unknown area status,” “agent location,” “model confidence,” or “resource availability” rapidly increases combinations, leading to a code explosion—commonly known as combinatorial explosion.
  • Difficult to Swap Action Logic:
    Changing specific actions in deeply nested if-then structures is cumbersome. For instance, replacing “risk-reduction exploration” requires carefully finding and modifying code buried several layers deep, increasing the chance of mistakes.
  • Hard to Adapt to Evolving Learning Models:
    In systems with machine learning, model updates or output format changes happen frequently. For example, introducing a “medium confidence range” or more complex outputs requires a large-scale rewrite of the existing if-then structure. This makes adapting to improvements risky and hinders flexible development.

How BT Solves These Problems

The same control logic expressed using a Behavior Tree would look like:

Root
├─ Sequence (Unknown Area → Action Selection)
│   ├─ Selector
│   │   ├─ Sequence (Teammate Nearby → High Confidence → Collaborative High-Precision Exploration)
│   │   ├─ Sequence (Teammate Nearby → Low Confidence → Collaborative Risk-Reduction Exploration)
│   │   ├─ Sequence (Resources Available → Solo Detailed Exploration)
│   │   └─ Solo Basic Exploration
├─ Selector
│   ├─ Sequence (Low Confidence → Recheck Data)
│   └─ Support Other Agents

Advantages of BT Structure

  • Clear Hierarchical Organization of Conditions and Actions:
    Nodes for “unknown area detection,” “agent location check,” and “confidence check” are separated hierarchically, making the overall structure visually intuitive.
  • Easy to Swap Subtrees:
    Actions like “risk-reduction exploration” or “collaborative data collection” can be replaced independently without affecting the overall logic, enabling easy modification or expansion of behavior patterns.
  • Natural Structuring of Condition Combinations:
    The fixed tree depth expresses combinations naturally within the structure, keeping code readable even as the number of conditions grows.
  • Flexible Adaptation to Learning Model Evolution:
    If model confidence logic or output format changes, only the relevant Condition node needs to be modified, minimizing the impact on the overall structure.
  • Easy Switching Between Collaborative and Solo Actions:
    BT separates “collaborative” and “solo” actions clearly in the tree, allowing easy differentiation and role changes per agent.
  • Visual Behavior Flow Organization:
    BT structures can be directly visualized as flowcharts or DSLs, making them easy to understand for both developers and designers.

Conclusion: Why BT Is Superior for Complex Systems

In systems requiring complex decision-making, coordinated actions, and integration of learning-based knowledge, BT enables:

  • Clear organization and reusability of actions
  • Prevention of combinatorial explosion
  • Flexible integration of learning results and knowledge reasoning
  • Visual, intuitive understanding of the overall design

This greatly enhances the flexibility, maintainability, and scalability of the system.

Python Implementation Comparison: BT vs. If-Then System

Below is a comparison between a Behavior Tree (BT) system and a simple if-then system using Python.

If-Then System Example

class Agent:
    def __init__(self, model_confidence, teammate_nearby, unknown_area_detected):
        self.model_confidence = model_confidence
        self.teammate_nearby = teammate_nearby
        self.unknown_area_detected = unknown_area_detected

    def act(self):
        if self.unknown_area_detected:
            if self.teammate_nearby:
                if self.model_confidence > 0.7:
                    print("Collaborative detailed exploration")
                else:
                    print("Collaborative risk-reduction exploration")
            else:
                print("Solo exploration")
        else:
            print("Patrolling")

# Example execution
agent = Agent(model_confidence=0.8, teammate_nearby=True, unknown_area_detected=True)
agent.act()

Explanation

In this code, the agent’s behavior logic is expressed simply.

  • The Agent class holds three internal states:
    • model_confidence: Confidence level of the AI model
    • teammate_nearby: Whether other agents are nearby
    • unknown_area_detected: Whether an unknown area has been detected
  • The act() method determines the agent’s behavior based on these states:
    • If an unknown area is detected:
      • If a teammate is nearby:
        • If model confidence is high → “Collaborative detailed exploration”
        • Else → “Collaborative risk-reduction exploration”
      • Else → “Solo exploration”
    • If no unknown area is detected → “Patrolling”

BT System Example

# Basic Node classes
class Node:
    def run(self):
        raise NotImplementedError

class Sequence(Node):
    def __init__(self, children):
        self.children = children

    def run(self):
        for child in self.children:
            if not child.run():
                return False
        return True

class Selector(Node):
    def __init__(self, children):
        self.children = children

    def run(self):
        for child in self.children:
            if child.run():
                return True
        return False

class Condition(Node):
    def __init__(self, func):
        self.func = func

    def run(self):
        return self.func()

class Action(Node):
    def __init__(self, func):
        self.func = func

    def run(self):
        self.func()
        return True

# Agent with BT structure
class Agent:
    def __init__(self, model_confidence, teammate_nearby, unknown_area_detected):
        self.model_confidence = model_confidence
        self.teammate_nearby = teammate_nearby
        self.unknown_area_detected = unknown_area_detected
        self.build_bt()

    def build_bt(self):
        self.bt = Selector([
            Sequence([
                Condition(lambda: self.unknown_area_detected),
                Selector([
                    Sequence([
                        Condition(lambda: self.teammate_nearby),
                        Condition(lambda: self.model_confidence > 0.7),
                        Action(lambda: print("Collaborative detailed exploration"))
                    ]),
                    Sequence([
                        Condition(lambda: self.teammate_nearby),
                        Action(lambda: print("Collaborative risk-reduction exploration"))
                    ]),
                    Action(lambda: print("Solo exploration"))
                ])
            ]),
            Action(lambda: print("Patrolling"))
        ])

    def act(self):
        self.bt.run()

# Example execution
agent = Agent(model_confidence=0.8, teammate_nearby=True, unknown_area_detected=True)
agent.act()

Explanation

In this implementation:

  • Node class is the common parent of all nodes, providing the structure, with run() implemented in child classes.
  • Sequence node:
    • Runs child nodes in order, returning False if any fail, True if all succeed.
  • Selector node:
    • Runs child nodes in order, returning True if any succeed, False if all fail.
  • Condition node:
    • Executes a condition function, returning True or False.
  • Action node:
    • Executes an action function, always returning True.
  • The Agent class:
    • Holds agent state (confidence, teammate proximity, unknown area detection).
    • Builds a Behavior Tree (BT) structure in build_bt():
      • If an unknown area is detected:
        • If a teammate is nearby and confidence is high → “Collaborative detailed exploration”
        • If a teammate is nearby → “Collaborative risk-reduction exploration”
        • Else → “Solo exploration”
      • If no unknown area is detected → “Patrolling”

Summary of the BT Approach

At first glance, the BT implementation appears more complex due to additional classes and structure. However, by encapsulating all components as independent nodes:

  • Conditions and actions are modular and separated.
  • Behavior is organized hierarchically for clear structure.
  • Nodes can be easily swapped or extended for flexibility.
  • Conditions and actions can be reused across different scenarios.

Compared to the if-then system, where increasing conditions deepens nesting and causes confusion, BT provides a scalable, structured approach that keeps logic clear, even as complexity grows.

BT offers a solution to the typical problems of if-then systems by providing:

  • Organized, reusable behavior logic
  • Prevention of deep nesting and combinatorial explosion
  • Easy integration of learning models and adaptive logic
  • Visual, maintainable behavior flows

This makes BT especially suitable for complex systems where flexible, scalable decision-making is essential.

Practical Application Examples

Behavior Trees (BT) are widely used for complex system control due to their ability to flexibly switch actions based on environmental conditions and internal states. Below are specific examples of where BT is effectively applied:

① Game AI

  • Example: Enemy character behavior control, cooperative actions of NPC allies
  • Description:
    Attack when the player approaches, retreat when health is low, patrol when no enemies are present
  • Key Point:
    Flexibly switches between actions like “attack,” “retreat,” and “patrol” depending on the situation

② Robot Control and Autonomous Navigation

  • Example: Cleaning robots, autonomous delivery robots
  • Description:
    Vacuum when dirt is detected, return to the charging station when battery is low, explore if the room mapping is incomplete
  • Key Point:
    Structures and switches actions optimally based on environmental conditions

③ Drone and Exploration Systems

  • Example: Disaster rescue drones, multi-drone cooperative exploration
  • Description:
    Conduct detailed investigation when an unknown area is detected, cooperate with nearby allies, retreat to a safe zone if danger is detected
  • Key Point:
    Organizes situations and collaboration elements to enable efficient exploration

④ Multi-Agent Cooperative Systems

  • Example: Disaster response or security robot teams, multi-agent systems in virtual environments
  • Description:
    Based on shared data, agents choose between “individual exploration” or “cooperative action,” with all agents responding collectively in emergencies
  • Key Point:
    Assigns a BT to each agent while enabling coordinated group control

Wider Adoption of BT

BT has been broadly adopted across game development, robotics, and military or defense fields. For example:

  • In Unity and Unreal Engine, many game AI systems are structured with BT, enabling complex, adaptable character behaviors.
  • In ROS (Robot Operating System), BT plugins allow flexible implementation of functions like exploration and obstacle avoidance.
  • In military and defense systems, BT is utilized to enable multiple drones or robots to cooperatively execute complex missions with flexible action control.

BT’s hierarchical structure and modularity provide a powerful framework for scalable, maintainable, and adaptable system behavior across these diverse domains.

Reference Materials

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