12/15/2023 0 Comments Turn based conway game of life![]() With this setup, we can now start playing the game for real. This then makes the field look very different: Then we create the R-Pentomino using this query: UNWIND, ,, , ] as pento MATCH ( c :Cell ) Michael suggested that we would use an ( ), the smallest 5 element starting point.įirst we need to reset the field to all dead - all Cells need to have the Dead Label. That's why my friend handed me the suggestion of starting out with a different type of seeding for the field. Which in an of itself is interesting, but leads to very unpredictable behaviour in the rounds of the game. This is actually a bit of an issue for the game - as it will immediately eliminate a huge part of the population upon the first iteration of executing the rules. You can now see the difference before and after by running a simple query: match p = ()->() return p Īs you can see from the screenshot, the " Live" nodes are a bit spread out in the field right now. Here, I will start by setting approximately 20% of the cells/nodes to Live, randomly throughout the field. We call this the seeding of the game, and there are obviously lots of ways that we could do that. In the Game of Life, there's always a need to introduce the starting state of the field. Now we are ready to start playing the game. we connect the cells using the NEIGHBOUR_OF relationship.We can do this in one query, which included two steps: Create the 25x25 matrix and connect the cells Then we can create the "field" that we will be playing the game in. Setup the database and the indexes : use system I figured how hard could it be to emulate this in a graph, as clearly the connectivity between cells and their neighbours would lend itself to some serious graphiness.įirst we start by setting up that database. So then you can actually see what would happen to a population and simulate it's evolution. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.Any live cell with more than three live neighbours dies, as if by overpopulation.Any live cell with two or three live neighbours lives on to the next generation.Any live cell with fewer than two live neighbours dies, as if by underpopulation. ![]() So at every "turn", the game will evaluate what will happen to every cell based on a very simple set of rules: Every cell will have a maximum of 8 neighbours, and will be evolving it's state (either Dead or Live) with every iteration. The whole idea of the Game is that you will create some kind of a " population" of cells in a matrix of cells. Things will evolve - or not - based on their connectivity. Why? Because the rules are all about connections between members of a population. So when I heard about it, I immediately thought that it would be a ton of fun to run this experiment in Neo4j. It is Turing complete and can simulate a universal constructor or any other Turing machine. One interacts with the Game of Life by creating an initial configuration and observing how it evolves. It is a zero-player game, meaning that its evolution is determined by its initial state, requiring no further input. Just take a look at this example and you will see the idea in action: The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970. There's an entire Wiki dedicated just to this "game" - it's one of the most wonderful rabbitholes on the web that I have ever seen. It basically allows you to simulate evolution in a rudimentary and simplified kind of way, but it's really fascinating how it works based on a very simple set of rules (see below). I had never heard of this thing, but was immediately fascinated. A couple of weeks ago, me and my Neo4j Breakfast Club friends were just freewheeling our way into the day, and one of my colleagues started talking to me about Conway's Game of Life.
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