This project is to test the premise of programming in higher dimensions using dimensionally reduced transmission techniques to indicate program state, instructions, instruction effects, object mutation and lifecycle.
For a given program, we typically have a language, parameters, instructions, datatypes, and well defined finite execution properties forming the basis for a thing humans run on computers to perform work.
it is my hope to express these factors in terms of pushing a hypothetical marble through space and declaring a program's state as a collection of trajectories signaled by positive energy changes to a signal, and describe instructions and instruction effects signalled by negative energy along the same dimension-- such as a plane or angular surface(s) that change the properties and manifestation of state along consistent proportional vectors to original imperative code.
A version of opennars software has been used as a research vehicle to measure the fit of a relatively succinct codebase capable of demonstrating learning along limitted dimensional frame of reference. My thesis involves proving learning equivalence in higher dimensional representations and interactions.