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Final Step(FOR REAL), the FINAL POLISH So it turns out, my method of attributing each element in my “maze” list a second value in order to check how many neighbors of empty space that they have is actually quite impractical and flawed. When you see a maze, you often see a bunch of OVERLAPPING paths in the maze, as they intersect each other. That is what makes a maze truly a maze, as there are countless options on where to go. However, if you look at my results above, you can

Step 3, polishing the main path Now that I have the basic generation down, I need to make it more like a maze. So, for this, I’ll construct a special mechanism during the generation of the path. Basically, what I’m going to do is make my maze, which is a list, into a list of lists. When I first made this generation, my list which generates the maze was just a bunch of 1’s and 0’s, 1’s representing a wall and 0’s representing empty space. Instead, I’ll make each element in my

So far, my project has been going pretty well, with most of the necessary mechanics to simulate a functioning game already in place. However, one major part of my project that is still in development is the process of developing a maze. I can't just manually make a maze every time; that defeats the purpose of letting AI control the game! Therefore, I’ve decided to stick with the process of automatically generating a malleable maze through an algorithm that the AI can control

In a neural network , not only does it contain a bias and a value , but is also has another numerical number of significance: the multiplier(which is also known as weight) . The arrows that connect every neuron to every other neuron in the layer in front of it isn't only their for show. These arrows all hold a multiplier, which multiplies the value of the neuron(including the bias of the neuron). These multipliers are there in order to weigh the significance of certain neur

AI is a new hot topic in the modern world, with dozens of different AI's pooping out left and right, such as Chat GPT, Grammarly , Google Gemini , and so on. The AI's I've just listed are all examples of a specific type of AI called " LLM " AIs. How these Large Language Model AIs usually work is when the AI is fed a ton of information, such as the entire English dictionary, or what is trending on TikTok currently. Despite only receiving digital information, these AIs are st