# Conway's game of life

It is also available from the `Models`

module as `Models.game_of_life`

.

`using Agents, AgentsPlots`

## 1. Define the rules

Rules of Conway's game of life: DSRO (Death, Survival, Reproduction, Overpopulation). Cells die if the number of their living neighbors is <D or >O, survive if the number of their living neighbors is ≤S, come to life if their living neighbors are ≥R and ≤O.

`rules = (2, 3, 3, 3)`

## 2. Build the model

First, define an agent type. It needs to have the compulsary `id`

and `pos`

fields, as well as an `status`

field that is `true`

for cells that are alive and `false`

otherwise.

```
mutable struct Cell <: AbstractAgent
id::Int
pos::Tuple{Int,Int}
status::Bool
end
```

The following function builds a 2D cellular automaton. `rules`

is of type `Tuple{Int,Int,Int, Int}`

representing DSRO.

`dims`

is a tuple of integers determining the width and height of the grid environment. `Moore`

specifies whether cells connect to their diagonal neighbors.

This function creates a model where all cells are "off".

```
function build_model(; rules::Tuple, dims = (100, 100), Moore = true)
space = GridSpace(dims, moore = Moore)
properties = Dict(:rules => rules)
model = ABM(Cell, space; properties = properties)
node_idx = 1
for x in 1:dims[1]
for y in 1:dims[2]
add_agent_pos!(Cell(node_idx, (x, y), false), model)
node_idx += 1
end
end
return model
end
```

Now we define a stepping function for the model to apply the rules to agents.

```
function ca_step!(model)
new_status = fill(false, nagents(model))
for (agid, ag) in model.agents
nlive = nlive_neighbors(ag, model)
if ag.status == true && (nlive ≤ model.rules[4] && nlive ≥ model.rules[1])
new_status[agid] = true
elseif ag.status == false && (nlive ≥ model.rules[3] && nlive ≤ model.rules[4])
new_status[agid] = true
end
end
for k in keys(model.agents)
model.agents[k].status = new_status[k]
end
end
function nlive_neighbors(ag, model)
neighbors_coords = node_neighbors(ag, model)
nlive = 0
for nc in neighbors_coords
nag = model.agents[Agents.coord2vertex((nc[2], nc[1]), model)]
if nag.status == true
nlive += 1
end
end
return nlive
end
```

now we can instantiate the model:

`model = build_model(rules = rules, dims = (50, 50), Moore = true)`

AgentBasedModel with 2500 agents of type Cell space: GridSpace with 2500 nodes and 9702 edges scheduler: fastest properties: Dict(:rules => (2, 3, 3, 3))

Let's make some random cells on

```
for i in 1:nv(model)
if rand() < 0.2
model.agents[i].status = true
end
end
```

## 3. Animate the model

We use the `plotabm`

function from `AgentsPlots.jl`

package for creating an animation.

```
ac(x) = x.status == true ? :black : :white
anim = @animate for i in 0:100
i > 0 && step!(model, dummystep, ca_step!, 1)
p1 = plotabm(model; ac = ac, as = 3, am = :square, showaxis = false)
end
```

We can now save the animation to a gif.

`gif(anim, "game_of_life.gif", fps = 5)`