KLEP AI Framework: Symbolic Decision-Making for Game Development and Adaptive Systems

Dec 2, 2025

Introduction

KLEP (Key-Lock-Executable-Process) is a cutting-edge symbolic AI framework designed for developers and researchers aiming to build dynamic, adaptive AI systems. It models decision-making processes via symbolic keys, locks, and executables to foster transparency, flexibility, and ethical behavior. Ideal for experimental AI research, game AI, and interactive simulations, KLEP offers a lightweight alternative to heavy data-driven AI models.

Designed with modularity and runtime modification, KLEP promotes rapid prototyping and real-time adaptation of intelligent behaviors.

Key Features

  • Symbolic AI model: Uses keys (inputs/concepts), locks (conditions), and executables (actions) for transparent decision-making.
  • Modularity: Easily create custom keys, locks, and executables tailored to your AI logic needs.
  • Runtime modification: Change AI behavior dynamically during execution for experimentation and adaptation.
  • Lightweight and efficient: Does not require large training datasets or heavy computation.
  • Flexible integration: Built in C# for Unity but conceptually portable across platforms.
  • Behavior lifecycle management: Supports clean and dirty release of keys for predictable state changes.
  • Supports advanced AI research: Can integrate symbolic reasoning with neuroevolution, genetic algorithms, or other learning mechanisms.

Installation Guide

Installing KLEP is straightforward. Simply download the KLEP folder from the GitHub repository and move it into your Unity project directory. Since KLEP uses symbolic logic rather than large datasets, installation is lightweight and quick.

Steps to begin:

  • Drag the KLEPNeuron script onto an empty GameObject in your scene.
  • Play the scene to auto-initialize the KLEP system.
  • Attach sensors like the KeyboardSensor to entities to inject inputs.

How to Use

KLEP revolves around a simple interplay of keys, locks, and executables:

  • Keys: Represent concepts or inputs which flow through the system.
  • Locks: Conditions that must be satisfied for behaviors (executables) to run.
  • Executables: Encapsulate actions or processes triggered when locks are validated.

Example workflow:

  • Create keys such as “JumpKey” in sensors or code.
  • Define locks that look for specific keys.
  • Create executables that perform actions like jumping when locks are satisfied.

KLEP supports both clean release of keys (behavior completion) and dirty release (ad hoc key pushing during execution).

Code Examples

public class JumpAction : KLEPExecutableBase {n public override void Execute() {n if (CanValidate(parentNeuron.heldKeys) && CanExecute(parentNeuron.heldKeys)) {n Debug.Log("Jumping!");n // Clean key release on completionn PushKey(MakeKey("LandedKey", 1.0f));n }n }n public override bool IsComplete() {n return true; // Action finishedn }n public override void Cleanup() {n Debug.Log("Cleaning up JumpAction");n }n}

This example defines a custom executable action for jumping, including behavior validation, execution, key release, and cleanup.

Contribution Guide

KLEP welcomes contributions for experimentation, feature enhancements, and integration with advanced AI methods.

  • Fork the repository on GitHub.
  • Create feature branches for your changes.
  • Follow coding standards and document your code clearly.
  • Submit pull requests with a detailed description of changes.
  • Engage with the community to discuss ideas and improvements.

Refer to the CONTRIBUTING.md file in the repository for detailed guidelines.

Community & Support

Join the growing community of AI developers and researchers working with KLEP by visiting the GitHub repository discussions, issues, and project wiki. Connect through GitHub to report issues, ask questions, and share your experiments.

Official philosophy and practical use examples can be explored on the project website linked in the repository.

Conclusion

KLEP stands out as a transparent, ethical, and modular symbolic AI framework emphasizing flexibility and runtime experimentation. Its unique approach to AI decision-making opens possibilities for developers interested in advanced AI research and practical implementations. Whether for game AI, simulations, or learning systems integration, KLEP provides a robust foundation.

Resources

What is KLEP and what problems does it solve?

KLEP is a symbolic AI framework that models decision-making using keys, locks, and executables. It enables developers to build adaptive, interpretable AI systems without heavy data requirements.

How do I get started with KLEP in Unity?

Start by downloading the KLEP folder, add the KLEPNeuron script to an empty GameObject, and attach sensors like KeyboardSensor to input data. Play your scene to initialize the system.

Can I customize AI behaviors in KLEP?

Yes, KLEP allows custom creation of keys, locks, and executables, letting you tailor AI behavior to your project’s requirements.

Does KLEP support runtime behavior changes?

Absolutely. KLEP supports modification of AI logic at runtime, which is useful for experimentation and adapting behavior dynamically.

Where can I find community support for KLEP?

Community discussions, issue tracking, and collaborative development occur on the GitHub repository, accessible through the project link.