# Conway's Game of Life

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Conway's Game of Life
Rulestring 23/3
B3/S23
Character Chaotic

Conway's Game of Life, also known as the Game of Life or simply Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular automaton.

The "game" is actually a zero-player game, meaning that its evolution is determined by its initial state, needing no input from human players. One interacts with the Game of Life by creating an initial configuration and observing how it evolves.

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## Patterns

Main article: Patterns

Many different types of patterns occur in the Game of Life, including static patterns ("still lives"), repeating patterns ("oscillators" – a superset of still lives), and patterns that translate themselves across the board ("spaceships"). Common examples of these three classes are shown below, with live cells shown in black, and dead cells shown in white.

Gosper glider gun

Conway originally conjectured that no pattern can grow indefinitely – i.e., that for any initial configuration with a finite number of living cells, the population cannot grow beyond some finite upper limit. In the game's original appearance in "Mathematical Games", Conway offered a \$50 prize to the first person who could prove or disprove the conjecture before the end of 1970. One way to disprove it would be to discover patterns that keep adding counters to the field: a "gun", which would be a configuration that repeatedly shoots out moving objects such as the "glider", or a "puffer train", which would be a configuration that moves but leaves behind a trail of persistent "smoke".

The prize was won in November of the same year by a team from the Massachusetts Institute of Technology, led by Bill Gosper; the "Gosper glider gun" shown to the right produces its first glider on the 15th generation, and another glider every 30th generation from then on. This first glider gun is still the smallest one known.

Later discoveries included other guns, puffers, and "rakes", which move and emit spaceships. Gosper also constructed the first pattern with an asymptotically optimal quadratic growth rate, called a "breeder", or "lobster", which worked by leaving behind a trail of guns.

## Iteration

From a random initial pattern of living cells on the grid, observers will find the population constantly changing as the generations tick by. The patterns that emerge from the simple rules may be considered a form of beauty. Small isolated subpatterns with no initial symmetry tend to become symmetrical. Once this happens the symmetry may increase in richness, but it cannot be lost unless a nearby subpattern comes close enough to disturb it. In a very few cases the society eventually dies out, with all living cells vanishing, though this may not happen for a great many generations. Most initial patterns eventually "burn out", producing either stable figures or patterns that oscillate forever between two or more states (known as ash); many also produce one or more gliders or spaceships that travel indefinitely away from the initial location.

## Algorithms

R-pentomino

The earliest results in the Game of Life were obtained without the use of computers. The simplest still-lives and oscillators were discovered while tracking the fates of various small starting configurations using graph paper, blackboards, physical game boards (such as Go) and the like. During this early research, Conway discovered that the R-pentomino failed to stabilize in a small number of generations.

These discoveries inspired computer programmers over the world to write programs to track the evolution of Life patterns. Most of the early algorithms were similar. They represented Life patterns as two-dimensional arrays in computer memory. Typically two arrays are used, one to hold the current generation and one in which to calculate its successor. Often 0 and 1 represent dead and live cells, respectively. A double loop considers each element of the current array in turn, counting the live neighbours of each cell to decide whether the corresponding element of the successor array should be 0 or 1. The successor array is displayed. For the next iteration the arrays swap roles so that the successor array in the last iteration becomes the current array in the next iteration.

A variety of minor enhancements to this basic scheme are possible, and there are many ways to save unnecessary computation. A cell that did not change at the last time step, and none of whose neighbours changed, is guaranteed not to change at the current time step as well, so a program that keeps track of which areas are active can save time by not updating the inactive zones.

In principle, the Life field is infinite, but computers have finite memory, and usually array sizes must be declared in advance. This leads to problems when the active area encroaches on the border of the array. Programmers have used several strategies to address these problems. The simplest strategy is simply to assume that every cell outside the array is dead. This is easy to program, but leads to inaccurate results when the active area crosses the boundary. A more sophisticated trick is to consider the left and right edges of the field to be stitched together, and the top and bottom edges also, yielding a toroidal array. The result is that active areas that move across a field edge reappear at the opposite edge. Inaccuracy can still result if the pattern grows too large, but at least there are no pathological edge effects. Techniques of dynamic storage allocation may also be used, creating ever-larger arrays to hold growing patterns.

Alternatively, the programmer may abandon the notion of representing the Life field with a 2-dimensional array, and use a different data structure, like a vector of coordinate pairs representing live cells. This approach allows the pattern to move about the field unhindered, as long as the population does not exceed the size of the live-coordinate array. The drawback is that counting live neighbours becomes a search operation, slowing down simulation speed. With more sophisticated data structures this problem can also be largely solved.

For exploring large patterns at great time depths, sophisticated algorithms like Hashlife may be useful.

There is also a method for implementation of the Game of Life using arbitrary asynchronous updates but still exacting emulating the behaviour of the synchronous game, also applicable to other cellular automata.[1]

## Variations on Life

Since Life's original inception, new rules have been developed. The standard Game of Life, in which a cell is "born" if it has exactly 3 neighbours, stays alive if it has 2 or 3 living neighbours, and dies otherwise, is symbolised as "23/3". The first number, or list of numbers, is what is required for a cell to continue. The second set is the requirement for birth. Hence "16/6" means "a cell is born if there are 6 neighbours, and lives on if there are either 1 or 6 neighbours". HighLife is 23/36, because having 6 neighbours, in addition to the original game's 23/3 rule, causes a birth. HighLife is best known for its replicators. Additional variations on Life exist, although the vast majority of these universes are either too chaotic or desolate.

Some variations modify the geometry of the universe as well as the rule. The above variations can be thought of as 2D Square, because the world is two-dimensional and laid out in a square grid. 3D Square and 1D Square variations have been developed, as have 2D Hexagonal variations where the grid is hexagonal or triangular instead of square.

Conway's rules may also be generalized so that instead of two states (live and dead) there are three or more. State transitions are then determined either by a weighting system or by a table specifying separate transition rules for each state; for example, Mirek's Cellebration's multi-coloured "Rules Table" and "Weighted Life" rule families each include sample rules equivalent to Conway's Life.

Patterns relating to fractals and fractal systems may also be observed in certain Life-like variations. For example, the automaton 12/1 generates four very close approximations to the Sierpiński triangle when applied to a single live cell.

Immigration is a variation that is the same as the Game of Life, except that there are two ON states (often expressed as two different colours). Whenever a new cell is born, it takes on the ON state that is the majority in the three cells that gave it birth. This feature can be used to examine interactions between spaceships and other objects within the game. Another similar variation, called QuadLife, involves four different ON states. When a new cell is born from three different ON neighbours, it takes on the fourth value, and otherwise like Immigration it takes the majority value. Except for the variation among ON cells, both of these variations act identically to Life.

## References

1. Nehaniv, Chrystopher L. (2002), "Self-Reproduction in Asynchronous Cellular Automata", 2002 NASA/DoD Conference on Evolvable Hardware (July 15-18, 2002, Alexandria, Virginia, USA), IEEE Computer Society Press, pp. 201-209