![]() ![]() In the context of Security Education, Training, and Awareness (SETA) programs, gamification is stated to be a major success factor. It includes all of the implementations discussed in this blog.Gamified learning management systems (LMS) can be effective in case game-design elements (GDE) address users' motivation to engage with the topic and lower barriers to learning. This was also the first time using babylon.js, and it was pretty easy to use. The only change I did was change the rules to better fit the 3D space. Implementing the game of life in 3D was pretty straight forward, and it helped a lot to first test it in 2D, as the algorithm works exactly the same in 3D space as 2D space. This reduces the number of batches to the GPU from 6,859 to less than 100! As a result, the game now runs at a premium 60 fps! Conclusion The greatest performance benefit came from using only one material for all the meshes and making the meshes copies of a root mesh. I also froze the positions of all the cells, as the cells don’t move after being created and so babylon.js doesn’t need to check for changes. To improve performance, if a cell was dead I would set the cube as not visible after it’s animation was finished. This of course led to pretty poor performance (15 fps). This means that I have 6,859 cubes being rendered to the screen… that’s a lot! The cube mesh only becomes visible when the cell becomes alive. In the above examples, for every possible location of a cell, there is a cube mesh. Let me know if you find any cool configurations! Optimizing ![]() See if you can find the configuration by trying it out yourself. I am still in the process of discovering it. I have tried really hard to find a configuration that produces the glider behavior found in the 2D version. This is a cube that repeats every 3 generations. I have taken the liberty to name them as well :) The Stairs I created the ability to randomize the starting population and found some really neat cell behaviors. Any dead cell with 4 live neighbors becomes a live cell.Īfter making the change to the algorithm, this was the result.Any live cell with 5-6 live neighbors survives.After tinkering with the numbers, I came up with the following rules: In 2D space, a cell can have 8 neighbors, but in 3D it becomes 26 neighbors. I also had to change the rules for the algorithm. This can be done effectively in typescript using a 2-dimensional array to represent the grid: Similarly, all other dead cells stay dead.Īs seen above, this can be implemented easily using a HTML canvas. All other live cells die in the next generation.Any dead cell with 3 live neighbors becomes a live cell.Any live cell with 2-3 live neighbors survives.The game of life has 3 rules ( via wikipedia): This means that the game of life can be used to represent any calculation that a computer can do! Secondly, the game of life can simulate a Turing machine. Firstly, cells in the game of life can reproduce themselves. The population size must be just right in order to grow and sustain life.Ĭonway’s game of life has two important properties. If the population is too large, then the population will shrink due to overpopulation. For example, if there are too few rabbits in an area, the rabbit population will shrink in that area. ![]() What is Conway’s game of life exactly?Ĭonway’s game of life is an algorithm that simulates changes in populations. After looking around the internet, I found only a few examples of 3D versions of the algorithm, so I’d thought I would give it a go. I’ve been really fascinated with Conway’s game of life lately and I wanted to make my own implementation of his famous algorithm in 3D space. ![]()
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