In this lab you will revisit the implementation of Conways Game of Life seen in an earlier lab. The purpose of this lab is consider the implementation of that program using Java 8 lambda expressions, rather than using inheritance. You will find that the implementation using lambdas is more concise and simpler to write.

Conways Game of Life

Recall that Conways Game of Life is a simple cellular simulation devised by John Horton Conway (a British mathematician) in 1970. The Game of Life has been studied extensively since then for both scientific interest, and also just for fun. For example, it has been shown that the problem of deciding whether a given state of the game will ever reach the empty board is undecidable. A simple example is:

Here, cells marked in black are alive whilst those in white are dead. Conway identified four rules for governing the transition between life and death in the Game of Life:

1. Any live cell with fewer than two live neighbours dies, as if caused by under-population.
2. Any live cell with exactly two live neighbours continues on to the next generation.
3. Any live cell with more than three live neighbours dies, as if by over-population.
4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.

Here, the neighbours of a cell are the eight surrounding cells (i.e. the four cells at North, South, East, and West and the four diagonals at NorthWest, NorthEast, etc). See en.wikipedia.org/wiki/Conways_ Game_of_Life for more.

In this lab, we are going to play with an extensible implementation of the Game of Life. The implementation is extensible because new rules can easily be written to control the game in different ways. The implementation also supports more complex forms of the Game, as it allows for more than just cells which are alive or dead. Instead, each cell is in a state between 0 and 9, where 9 corresponds roughly to dead and the other states to varying forms of alive. The meaning of the intermediate states is left up to the rule implementor, but can be used to simulate age, sickness, happiness, etc.

Getting Started

To get started, download the lambda-conway.jar file from the course website and import this into an Eclipse project. The Game of Life is provided with a Graphical User Interface, which you can run by rightclicking on GameOfLife and selecting Run AsJava Application (ignore CellDecayGameOfLife for now). You should see a window pop up like this:

You can play the game by clicking on the window to place cells and the starting the simulation by pressing run. Take a few minutes to play around with the game. You should notice that not all rules are implemented and we will begin by fixing them in Activity 1.

NOTE: it is useful to take the time and revisit the implementation of Conway using inheritance from an earlier lab. You will notice that the rules package is no longer required in the implementation for this lab. In fact, the number of classes required has reduced by four in total.

Activity 1: Conways Missing Rules

The implementation of the Game of Life provided does not properly implement rules (3) and (4) from above. The goal of this activity is to complete these rules. The rules themselves are implemented using lambda expressions. For example, Conways underpopulation rule is implemented as follows:

1

2

3

4

5

This implementation of the rule is very easy to compare with the original English description. You will find this rule in the class GameOfLife and it should provide a useful guide which you can follow. Having completed rules (3) and (4), you should find that all tests in GameOfLifeTests now pass.

Activity 2: Cell Decay

The goal now is to implement a variation on the basic rules for the Game Of Life which models the age of a cell. Our updated rules for the game are:

1. Any live cell with fewer than two live neighbours gets older, as if caused by under-population.
2. Any live cell with two live neighbours continues in stasis to the next generation.
3. Any live cell with more than three live neighbours gets older, as if by over-population.
4. Any cell with exactly three live neighbours gets younger, as if by happiness.

The age of a cell is determined by its current state which, if you recall, is a value between 09. Here, 0 is youngest whilst 9 is oldest. We consider that cells of age 9 are dead, whilst all others are varying forms of alive. At this point, we will now concentrate on using the variation CellDecayGameOfLife and the accompanying test cases, CellDecayTests. You can run the simulation as a Java Application with CellDecayGameOfLife, though at this stage you should find that it does nothing.

What to do. Your aim is to implement the above rules for cell decay. You should add any rules you create to the CellDecayRules array in CellDecayGameOfLife, as was done for the original GameOfLife. Having done this, all tests in CellDecayTests should now pass.

Having completed this part, you should find that the balance has tipped and cell growth expands quickly. This is because cells stay alive for much longer than before. For example, the system will often produce something like this: