Project 1.2: STC Knowledge Graph - (Concept Only)

Autonomous design systems typically lack Contextual Continuity. When a system solves a problem in one environment, the underlying logic is often lost when transitioning to a different set of physical constraints.

We are investigating whether it is possible to create a "Unified Experience Schema" that allows an AI to understand that two seemingly different problems—such as heat management on a vacuum-based station and dust-fouling on a planetary surface—might share common structural risks.

The proposed solution is a Non-Linear Memory Substrate. Instead of a standard database, this would be a web of interconnected concepts. We are currently theorizing how this would interact with the broader ecosystem:

• Relationship Mapping: How do we define the "meaning" of an engineering component in relation to its environment?
• Inference Capability: Could a system suggest a solution for a planet it has never seen based on similarities found in existing data?
• Information Decay: How does the system prioritize relevant historical data over obsolete mission logs?

The methodology at this stage is purely Conceptual Modeling:

• Taxonomy Research: Investigating how to categorize autonomous actions into "Subjects" and "Objects" that a machine can reason through.
• Dimensional Analysis: Exploring how to represent complex environmental variables (gravity, atmospheric density, radiation) as mathematical vectors.
• Topological Inquiry: Looking at how "closeness" between two different engineering designs can be measured in an abstract space.

This project is currently in the planning stage. No active codebase exists.

Current Objective: NA

• To be confirmed.