Week 07 [Markov Chain]

For this week's assignment, I wanted to explore how a Markov chain algorithm could interact with the output of an L-system.

During the semester, I've spent some time experimenting with L-systems, and I had the idea to fine-tune a model that could generate rule sets for me. I thought it might be interesting to apply a Markov chain only to the rules I'm currently working with to see what patterns or structures might emerge.

Initially, the results were chaotic. I hadn’t yet optimized the data I was providing to the model to align with the structure I created. For example, the number "4" should appear at the end of each sentence to close the shape, and the letter "s" has a required frequency in each rule.

I also tried structuring the data so that rotations and line orientations would appear at specific points in each rule output, aiming for a consistent sequence structure.

‍

However, I realized that if I want to keep working with this approach, I’ll need to assign unique symbols to each rule. This is because the algorithm learns which symbols should follow others, so reusing the same ones limits the flexibility needed to form distinct rules. I still find this idea interesting and may try to develop the right approach around it next semester.

‍