Sep 1

The Symbolic Simulation Engine is a recursive framework that represents characters, worlds, and stories as dynamic, evolving systems. It combines philosophy, symbolic structures, and computational design to simulate growth, transformation, and interaction in a way that’s both interpretable and generative.

Rather than being a fixed narrative or game engine, it’s a toolkit for building living systems:

Characters have traits, beliefs, and histories that evolve with every interaction.

Worlds change over time, responding to player or author decisions.

Items, symbols, and events carry meaning that feeds back into the system, creating emergent storylines.

Presently my aim is to provide creators, developers, and researchers with a platform that bridges mythic storytelling, AI simulation, and interactive design. Whether used for games, films, VR/AR, or AI research, the system is meant to grow, adapt, and generate rich worlds of meaning, because that is the direction I designed the system for. But it could be branched into different purposes, and in the future I will extend it toward AGI, general AI, and robotics by integrating perception adapters, planner/controller bridges, persistent grounding, safety constraints, and benchmarking. In those directions, the URGB serves as a symbolic cognition layer that grounds raw data in meaning, guides planners with narrative and goal structures, and makes decisions explainable in human-legible terms—whether in games, films, VR/AR, AI research, or embodied robotic platforms.

The system could potentially extend into robotics as a symbolic cognition layer, translating sensor data into meaningful symbols, structuring goals in narrative form, and guiding task planners with human-legible reasoning. It would sit above low-level control, adding grounding, adaptability, and explainability.

The system could potentially enhance large language models by providing a symbolic scaffold for continuity, identity, and meaning. Instead of producing only surface text, LLMs integrated with the system could generate responses anchored in traits, goals, and world states—maintaining coherence, memory, and narrative consistency over time.

With perception and control bridges, the system can sit above low-level controllers as a symbolic cognition layer: translate sensor data into meaningful symbols, frame goals in narrative form, guide task planners, and generate human-legible explanations of actions.

The system could potentially contribute to AGI by serving as a symbolic cognition layer. It organizes perception, meaning, planning, and reflection in recursive structures, allowing agents to ground raw data, set narrative-like goals, and explain their reasoning in human terms. While not sufficient for AGI by itself, it can complement learning systems and planners by adding continuity, meaning, and explainability—key gaps in today’s architectures.

These are potential directions, dependent on adding perception/control bridges, grounding, safety constraints, and benchmarking.

This post showcases the evolution of my Symbolic Simulation Engine, a system designed to model and explore symbolic meaning, character arcs, and world dynamics through code. Below are eleven demos—seven with full PDF breakdowns—capturing major milestones in the project’s development. Each demo highlights a unique aspect of the engine, from recursive world generation to character interactions, and serves as both a technical proof of concept and a creative artifact.

At the moment I only have hugging face demos, the public facing hugging face is not ready, nor is the public facing github, I’m working on unreal demos, but I am just learning how to do that.