The Neuron

At the heart of the KLEP system is the KLEPNeuron, a lightweight yet powerful component that automates the process of behavior discovery, synchronization, and execution. The neuron is designed to be both the entry point for enabling KLEP on an object and the linchpin for handling dynamic behavior updates in real-time.

How KLEPNeuron Works

The KLEPNeuron embodies a key principle: self-discovery and automation. Once added to an object, the neuron immediately begins identifying and managing all the KLEPExecutables (behaviors) associated with that object and its hierarchy. This allows the neuron to:

Dynamically synchronize its behaviors every frame.
Maintain a constantly up-to-date list of executable actions based on the object’s current state.
Simplify implementation by eliminating the need for manual setup or rigid definitions.

Behavior Discovery: A Recursive Approach

The KLEPNeuron uses a recursive discovery mechanism to identify all relevant behaviors within an object’s hierarchy. This is done through the GatherExecutables function, which:

Traverses the object’s transform hierarchy.
Collects all components of type KLEPExecutableBase.
Ensures that only enabled and valid behaviors are considered.

This mechanism ensures that the neuron can adapt in real-time to:

Dynamic object hierarchies: Behaviors can be added or removed during runtime, and the neuron will automatically pick up the changes.
Selective behavior discovery: The scope of discovery can be extended or constrained to specific sub-trees of the hierarchy by modifying the parent transform passed to GatherExecutables.

Synchronizing Executables

Once behaviors are discovered, the neuron synchronizes them with its internal list. This involves:

Identifying new behaviors and registering them.
Detecting removed or invalid behaviors and cleaning them up.
Ensuring that all behaviors are ready and initialized before decision-making begins.

This synchronization process runs every frame, meaning the neuron always has an accurate, real-time understanding of the behaviors available to it. The result is frame-perfect updates to the agent’s decision-making logic, enabling:

Instant adaptation to new gameplay elements.
Dynamic updates to available actions, even in highly fluid scenarios.

Simplifying KLEP Implementation

The self-discovering nature of the KLEPNeuron makes implementing KLEP in a game environment remarkably simple. Developers only need to:

Add the KLEPNeuron component to an object.
Attach behaviors (KLEPExecutables) as needed, either at design time or dynamically at runtime.

Everything else—behavior registration, synchronization, and key management—is handled automatically. This approach eliminates the manual overhead of defining complex behavior trees or maintaining rigid dependencies.

Extensibility Through Automation

The neuron’s recursive discovery mechanism isn’t limited to its own hierarchy. Developers can extend it to:

Focus on specific objects or sub-trees in the hierarchy, using custom logic to filter behaviors.
Discover behaviors in entirely separate objects or across scenes, enabling advanced coordination between different KLEP agents.

This extensibility paves the way for more sophisticated implementations, such as:

Context-sensitive behaviors: Neurons that dynamically adapt their behavior sets based on the objects or environments they interact with.
Multi-agent systems: Neurons that collaborate by referencing each other’s behavior trees.

Real-Time Updates for In-Frame Decisions

One of the neuron’s defining features is its ability to update available behaviors before the decision-making process. By ensuring that the list of executables is synchronized at the start of every frame, the neuron allows the agent to:

Always select actions based on the most accurate and current state of the environment.
Avoid outdated or invalid behaviors that might otherwise lead to poor decisions.

This in-frame update cycle is a game-changer for dynamic gameplay scenarios, ensuring that KLEP agents remain responsive and adaptable.

A Foundation for Agent Automation

The principles behind the neuron extend naturally to higher-order behaviors, enabling:

Self-configuring agents: Neurons that spawn and configure other neurons to handle sub-tasks, creating a cascading hierarchy of autonomous agents.
Emergent systems: Complex interactions between multiple neurons, where each one dynamically discovers and reacts to its own local environment while contributing to a global system.

For example, imagine an AI agent that spawns minions, each with its own neuron. The master neuron could:

Automatically assign keys and behaviors to the minions it spawns.
Dynamically adapt its own behavior based on the state of its minions.

This kind of automation creates emergent complexity while remaining developer-friendly.

Why the KLEPNeuron is the Key

The KLEPNeuron is more than just a framework component—it’s a philosophy in action. By automating discovery, synchronization, and decision-making, the neuron removes the barriers to implementing dynamic AI. Its recursive design ensures adaptability, while its extensibility invites creative problem-solving.

With the KLEPNeuron, complex behaviors become manageable, and scalable systems become intuitive. It’s not just about enabling KLEP—it’s about rethinking how behaviors are managed in games.