Lotka Volterra

This is a demonstration of a predator-prey agent-based model.

We start with importing some libraries.

using Catlab, DataMigrations, AlgebraicRewriting
using Random, Test
using Luxor # optional: makes the sequence of images via `view_traj` at the end

using Catlab.Graphics.Graphviz: Attributes, Statement, Node
using Catlab.Graphics.Graphviz

const hom = homomorphism



Defining an ontology is stating what data is required to specify a state of the simulation at some point in time. In AlgebraicJulia, this is done via declaring a Presentation, i.e. a database schema. Objects (Ob, or tables) are types of entities. Homs (Hom, or foreign keys) are functional relationships between the aforementioned entities. AttrTypes are placeholders for Julia types, which are assigned to Ob via attributes (Attr).

The schema below extends the schema for directed graphs, which consists in two tables (E and V, for edges and vertices) and two homs (src and tgt, E→V). It says there are two more types of entities, Sheep and Wolf, and they can be thought of as living on the graph due to homs sheep_loc and wolf_loc which assign each of them a vertex.

Furthermore, we want to give these entities some attributes. In this model, wolves and sheep both have "energy", given by Eng (a type variable, which we'll later instantiate with Int). Also, grass lives on vertices, and it's represented by an integer. countdown being zero means the grass is ready to eat, whereas a value above zero represents a counter of time the grass needs until it grows back.

There is also a direction attribute type, and the edges (as well as animals) will be oriented in a particular direction at any point in time.

@present SchLV <: SchGraph begin
  (Sheep, Wolf)::Ob
  sheep_loc::Hom(Sheep, V); wolf_loc::Hom(Wolf, V)

  (Time, Eng)::AttrType
  countdown::Attr(V, Time);
  sheep_eng::Attr(Sheep, Eng); wolf_eng::Attr(Wolf, Eng)

  dir::Attr(E, Dir); sheep_dir::Attr(Sheep, Dir); wolf_dir::Attr(Wolf, Dir)

# efficient ABM rewriting uses BitSetParts rather than DenseParts to allow
# in-place pushout rewriting, rather than pure/non-mutating pushouts.)
@acset_type LV_Generic(SchLV, part_type=BitSetParts) <: HasGraph
const LV = LV_Generic{Int, Int, Symbol}

to_graphviz(SchLV; prog="dot")

We can further extend this schema with an additional attribute of (x,y) coordinates for every vertex. This is nice for visualization but is otherwise unnecessary when doing the actual agent-based modeling. So what we will do is build our model with the LV schema and then run our model with the LV′ schema.

@present SchLV′ <: SchLV begin
  coord::Attr(V, Coord)

@acset_type LV′_Generic(SchLV′, part_type=BitSetParts) <: HasGraph
const LV′ = LV′_Generic{Int, Int, Symbol, Tuple{Int,Int}};

We will be representing directions as Symbols and encode the geometry via left and right functions. The attribute will only take values :N, :E, :W, or :S.

import Catlab.CategoricalAlgebra: left, right

function right(s::Symbol)
  if s == :N
    return :E
  elseif s == :S
    return :W
  elseif s == :E
    return :S
  elseif s == :W
    return :N

function left(s::Symbol)
  if s == :N
    return :W
  elseif s == :S
    return :E
  elseif s == :E
    return :N
  elseif s == :W
    return :S

Data migration functors

The schema LV has a certain symmetry between wolves and sheep, and this symmetry can be used to take instances of the schema (i.e. world states) and swap the wolves and the sheep. This is helpful for avoiding repeating work: there are certain actions that wolves and sheep share, so, by using this data migration, we can define them in terms of sheep and then migrate along F to obtain the analogous actions for wolves.

F = Migrate(
  Dict(:Sheep => :Wolf, :Wolf => :Sheep),
  Dict([:sheep_loc => :wolf_loc, :wolf_loc => :sheep_loc,
    :sheep_eng => :wolf_eng, :wolf_eng => :sheep_eng, :countdown => :countdown,
    :sheep_dir => :wolf_dir, :wolf_dir => :sheep_dir,]), SchLV, LV);

We ought to be able to take a state of the world (with no coordinate information) and obtain a state of the world with coordinates (the canonical way to do this is to assign "variables" for the values of the coordinates).

F2 = Migrate(SchLV, LV, SchLV′, LV′; delta=false);

Initializing and visualizing world states

To help us create initial states for simulations, here is a helper function that makes an n × n grid with periodic boundary conditions. Edges in each cardinal direction originate at every point.

function create_grid(n::Int)
  lv = LV′()
  coords = Dict()
  for i in 0:n-1  # Initialize grass 50% green, 50% uniformly between 0-30
    for j in 0:n-1
      coords[i=>j] = add_part!(lv, :V; countdown=max(0, rand(-30:30)), coord=(i, j))
  for i in 0:n-1
    for j in 0:n-1
      add_part!(lv, :E; src=coords[i=>j], tgt=coords[mod(i + 1, n)=>j], dir=:E)
      add_part!(lv, :E; src=coords[i=>j], tgt=coords[mod(i - 1, n)=>j], dir=:W)
      add_part!(lv, :E; src=coords[i=>j], tgt=coords[i=>mod(j + 1, n)], dir=:N)
      add_part!(lv, :E; src=coords[i=>j], tgt=coords[i=>mod(j - 1, n)], dir=:S)

To initialize a state of the world with sheep and wolves, we also accept parameters which indicate the fraction of spaces that are populated with that animal.

function initialize(n::Int, sheep::Float64, wolves::Float64)::LV′
  grid = create_grid(n)
  args = [(sheep, :Sheep, :sheep_loc, :sheep_eng, :sheep_dir),
    (wolves, :Wolf, :wolf_loc, :wolf_eng, :wolf_dir)]
  for (n_, name, loc, eng, d) in args
    for _ in 1:round(Int, n_ * n^2)
      dic = Dict([eng => 5, loc => rand(vertices(grid)),
        d => rand([:N, :E, :S, :W])])
      add_part!(grid, name; dic...)

Some visualization code below will allow us to see states of the world. Edges are left implicit (we know from how the graphs were constructed that there are edges between every pair of adjacent vertices).

supscript_d = Dict(['1' => '¹', '2' => '²', '3' => '³', '4' => '⁴', '5' => '⁵', '6' => '⁶', '7' => '⁷', '8' => '⁸', '9' => '⁹', '0' => '⁰', 'x' => 'ˣ', 'y' => 'ʸ', 'z' => 'ᶻ', 'a' => 'ᵃ', 'b' => 'ᵇ', 'c' => 'ᶜ', 'd' => 'ᵈ'])
supscript(x::String) = join([get(supscript_d, c, c) for c in x]); # energy shown in superscript

function view_LV(p::ACSetTransformation, pth=tempname(); name="G", title="")
  if nparts(dom(p), :Wolf) == 1
    star = :Wolf => p[:Wolf](1)
  elseif nparts(dom(p), :Sheep) == 1
    star = :Sheep => p[:Sheep](1)
  elseif nparts(dom(p), :V) == 1
    star = :V => p[:V](1)
    star = nothing
  view_LV(codom(p), pth; name=name, title=title, star=star)

function view_LV(p::LV′, pth=tempname(); name="G", title="", star=nothing)
  pstr = ["$(i),$(j)!" for (i, j) in p[:coord]]
  stmts = Statement[]
  for s in 1:nv(p)
    st = (star == (:V => s)) ? "*" : ""
    gv = p[s, :countdown]
    col = gv == 0 ? "lightgreen" : "tan"
    push!(stmts, Node("v$s", Attributes(
      :label => gv == 0 ? "" : string(gv) * st,
      :shape => "circle",
      :color => col, :pos => pstr[s])))
  d = Dict([:E => (1, 0), :N => (0, 1), :S => (0, -1), :W => (-1, 0),])

  args = [(:true, :Wolf, :wolf_loc, :wolf_eng, :wolf_dir),
    (false, :Sheep, :sheep_loc, :sheep_eng, :sheep_dir)]

  for (is_wolf, prt, loc, eng, dr) in args
    for w in parts(p, prt)
      st = (star == ((is_wolf ? :Wolf : :Sheep) => w)) ? "*" : ""
      e = only(incident(p, p[w, loc], :src) ∩ incident(p, p[w, dr], :dir))
      s = src(p, e)
      dx, dy = d[p[e, :dir]]
      (sx, sy) = p[s, :coord]

      L, R = 0.25, 0.1
      wx = sx + L * dx + R * rand()
      wy = sy + L * dy + R * rand()
      ID = "$(is_wolf ? :w : :s)$w"
      append!(stmts, [Node(ID, Attributes(
        :label => "$w" * supscript("$(p[w,eng])") * st,
        :shape => "square", :width => "0.3px", :height => "0.3px", :fixedsize => "true",
        :pos => "$(wx),$(wy)!", :color => is_wolf ? "red" : "lightblue"))])

  g = Graphviz.Digraph(name, Statement[stmts...]; prog="neato",
    graph_attrs=Attributes(:label => title, :labelloc => "t"),
    node_attrs=Attributes(:shape => "plain", :style => "filled"))
  open(pth, "w") do io
    show(io, "image/svg+xml", g)

init = initialize(2, 0.5, 0.5)

Not only can we visualize states of the world, but we can visualize certain states of the world with certain distinguished agents, such as a sheep, wolf, or patch of grass. The way we specify a state of the world (X) with a distinguished sheep (for example) is a morphism S → X, where S is an ACSet with a single sheep in it.

Below we manually construct a generic sheep (in LV, which doesn't have coordinates). We then use the data migration to give it generic coordinates to obtain a generic LV′ sheep. We use this as the domain of a hom that assigns the sheep to Sheep #2 of the world state init from above.

S = @acset LV begin V=1; Sheep=1; Dir=1; Eng=1; Time=1;
  sheep_loc=1; sheep_dir=[AttrVar(1)]; sheep_eng=[AttrVar(1)]; countdown=[AttrVar(1)]

view_LV(hom(F2(S), init; initial=(Sheep=[2],)))

It will be helpful to not have to manually construct "generic" world states like above because it's tedious. We want to say "give me a sheep" or "give me a sheep and a wolf that are on the same vertex" and have it automatically specify the remaining information in the most generic way possible. The @acset_colim macro is perfect for exactly this. In order to use that macro, we need to compute something first with the yoneda_cache function.

yLV = yoneda_cache(LV; clear=false); # cache=false means reuse cached results
I = LV() # Empty agent type
S = @acset_colim yLV begin s::Sheep end # Generic sheep agent
W = F(S) # Generic wolf agent, obtained via the swapping `F` data migration
G = @acset_colim yLV begin v::V end # Generic grass agent
N = Names(Dict("W" => W, "S" => S, "G" => G, "" => I)); # give these ACSets names


We have finished specifying what makes up a simulation state, and next is to define what sorts of transitions are possible. This is done by declaring rewrite rules. We also will put these rules into little boxes with an incoming wire and two outgoing wires (called a RuleApp), where wires correspond to the successful (resp. unsuccessful) application of the rewrite rule. In the next section we will focus on assembling these miniature wiring diagrams into an overall simulation.

Here we just note that the wires of the simulation must be labeled with an agent. This is because, at all points in time, there is a distinguished agent (i.e. a morphism A → X, where A is an ACSet with a generic something in it, e.g. a generic sheep like above). So when we wrap our rules into the RuleApp boxes, we need to also specify what those distinguished agents are and how they relate to the pattern + replacement of the rewrite rule within the box.


Our first action that is possible for sheep (and wolves) is rotation. Animals will, with some probability, change their orientation. This is a rewrite rule which only modifies an attribute rather than changing any combinatorial data, so rather than the usual span L ← I → R data required we simply put in a single ACSet along with an expr dictionary which states how attributes change.

rl = Rule(S; expr=(Dir=[xs -> left(only(xs))],));
rr = Rule(S; expr=(Dir=[xs -> right(only(xs))],));

sheep_rotate_l = tryrule(RuleApp(:turn_left, rl, S));
sheep_rotate_r = tryrule(RuleApp(:turn_right, rr, S));

We can imagine executing these rules in sequence

seq_sched = (sheep_rotate_l ⋅ sheep_rotate_r);
view_sched(seq_sched; names=N)

... or in parallel.

par_sched = (sheep_rotate_l ⊗ sheep_rotate_r);
view_sched(par_sched; names=N)

Test rotation

  ex = @acset LV begin
    E=1; Sheep=1; V=2
    src=1; tgt=2; dir=:W; countdown = [0, 0]
    sheep_loc=1; sheep_eng=100; sheep_dir=:N

  expected = copy(ex);
  expected[:sheep_dir] = :W
  @test is_isomorphic(rewrite(rl, ex), expected)
  rewrite!(rl, ex)
  @test is_isomorphic(ex, expected)

Moving forward

s_fwd_l = @acset_colim yLV begin
  e::E; s::Sheep;
  sheep_loc(s) == src(e);
  dir(e) == sheep_dir(s)

s_fwd_i = @acset_colim yLV begin e::E end

s_fwd_r = @acset_colim yLV begin
  e::E; s::Sheep; sheep_loc(s) == tgt(e); dir(e) == sheep_dir(s)

s_n = @acset_colim yLV begin
  e::E; s::Sheep;
  sheep_loc(s) == src(e); dir(e) == sheep_dir(s)
  sheep_eng(s) == 0

sheep_fwd_rule = Rule(
  hom(s_fwd_i, s_fwd_l; monic=true),
  hom(s_fwd_i, s_fwd_r; monic=true),
  ac=[AppCond(hom(s_fwd_l, s_n), false)],
  expr=(Eng=[vs -> only(vs) - 1],))

sheep_fwd = tryrule(RuleApp(:move_fwd, sheep_fwd_rule,
  hom(S, s_fwd_l), hom(S, s_fwd_r)));

Moving forward test

  ex = @acset LV begin
    V=3; E=2; Sheep=1;
    src=[1,2]; tgt=[2,3]; dir=[:N,:W]
    sheep_loc=1; sheep_dir=:N; sheep_eng = 10
  expected = copy(ex);
  expected[:sheep_loc] = 2
  expected[:sheep_eng] = 9
  @test is_isomorphic(expected, rewrite(sheep_fwd_rule, ex))
  rewrite!(sheep_fwd_rule, ex)
  @test is_isomorphic(ex, expected)

Sheep eat grass

s_eat_pac = @acset_colim yLV begin s::Sheep; countdown(sheep_loc(s)) == 0 end;

se_rule = Rule(S; expr=(Eng=[vs -> only(vs) + 4], Time=[vs -> 30],),
  ac=[AppCond(hom(S, s_eat_pac))]);

sheep_eat = tryrule(RuleApp(:Sheep_eat, se_rule, S));

Sheep eating test

  ex = @acset LV begin
    E=1; V=2; Sheep=1;
    src=1; tgt=2; dir=:S; countdown=[10, 0]
    sheep_loc = 2; sheep_eng = 3; sheep_dir=:W

  expected = copy(ex)
  expected[2,:countdown] = 30
  expected[1,:sheep_eng] = 7

  @test is_isomorphic(expected, rewrite(se_rule, ex))
  rewrite!(se_rule, ex)
  @test is_isomorphic(ex, expected)

Wolves eat sheep

w_eat_l = @acset_colim yLV begin
  s::Sheep; w::Wolf
  sheep_loc(s) == wolf_loc(w)

we_rule = Rule(hom(W, w_eat_l), id(W); expr=(Eng=[vs -> vs[2] + 20],));

wolf_eat = tryrule(RuleApp(:Wolf_eat, we_rule, W));

Wolf eating test

  ex = @acset LV begin
    Sheep=1; Wolf=1; V=3; E=2;
    src=[1,2]; tgt=[2,3]; countdown=[9,10,11]; dir=[:N,:N];
    sheep_loc=2; sheep_eng=[3]; sheep_dir=[:N]
    wolf_loc=[2];  wolf_eng=[16];  wolf_dir=[:S]

  expected = copy(ex)
  expected[1, :wolf_eng] = 36
  rem_part!(expected, :Sheep, 1)

  @test is_isomorphic(rewrite(we_rule,ex), expected)
  rewrite!(we_rule, ex)
  @test is_isomorphic(ex,expected)

Sheep starvation

s_die_l = @acset_colim yLV begin s::Sheep; sheep_eng(s) == 0 end;

sheep_die_rule = Rule(hom(G, s_die_l), id(G))
sheep_starve = (RuleApp(:starve, sheep_die_rule,
  hom(S, s_die_l), create(G))
                (id([I]) ⊗ Weaken(create(S))) ⋅ merge_wires(I));

Sheep starvation test

  ex = @acset LV begin
    V=1; Sheep=1; Wolf=1
    sheep_loc=1; sheep_eng=0; sheep_dir=:W
    wolf_loc=1; wolf_eng=10; wolf_dir=:S
  expected = copy(ex)
  rem_part!(expected, :Sheep, 1)

  @test is_isomorphic(rewrite(sheep_die_rule,ex), expected)
  @test is_isomorphic(ex, expected)


s_reprod_r = @acset_colim yLV begin
  (x, y)::Sheep
  sheep_loc(x) == sheep_loc(y)

sheep_reprod_rule = Rule(
  hom(G, S),
  hom(G, s_reprod_r);
  expr=(Dir=fill(vs->only(vs) ,2),
        Eng=fill(vs -> round(Int, vs[1] / 2, RoundUp), 2),)

sheep_reprod = RuleApp(:reproduce, sheep_reprod_rule,
  id(S), hom(S, s_reprod_r)) |> tryrule;

Reproduction test

begin # test
  ex = @acset LV begin
    Sheep=1; Wolf=1; V=2;
    sheep_loc=1; sheep_eng=10; sheep_dir=:W
    wolf_loc=2; wolf_eng=5; wolf_dir=:N

  expected = copy(ex)
  expected[:sheep_eng] = [5, 5]
  expected[:sheep_loc] = [1, 1]
  expected[:sheep_dir] = [:W, :W]

  @test is_isomorphic(rewrite(sheep_reprod_rule,ex),expected)
  @test is_isomorphic(ex, expected)

Grass increments

g_inc_n = deepcopy(G)
set_subpart!(g_inc_n, 1, :countdown, 0)
rem_part!(g_inc_n, :Time, 1);

g_inc_rule = Rule(id(G), id(G);
  ac=[AppCond(hom(G, g_inc_n), false)],
  expr=(Time=[vs -> only(vs) - 1],));

g_inc = RuleApp(:GrassIncrements, g_inc_rule, G) |> tryrule;

Grass incrementing test

  ex = @acset LV begin
    Sheep = 1; V = 3; E = 2
    src = [1, 2]; tgt = [2, 3]
    sheep_loc = 2; sheep_eng = [3]; sheep_dir = [:N]
    countdown = [1, 10, 2]; dir = fill(:N, 2)
  expected = @acset LV begin
    Sheep = 1; V = 3; E = 2
    src = [1, 2]; tgt = [2, 3]
    sheep_loc = 2; sheep_eng = [3]; sheep_dir = [:N]
    countdown = [0, 10, 2]; dir = fill(:N, 2)
  @test is_isomorphic(rewrite(g_inc_rule, ex), expected)
  rewrite!(g_inc_rule, ex)
  @test is_isomorphic(ex, expected)

Assembling rules into a recipe

Now we can assemble our building block transitions into a large wiring diagram characterizing the flow of the overall ABM simulation. In addition to the blue rewrite rule blocks, we have red (probabilistic) control flow blocks and yellow Query blocks.

general = mk_sched((;), (init=:S,), N, (
    turn=const_cond([1.0, 2.0, 1.0], S; name=:turn),
    maybe=const_cond([0.1, 0.9], S; name=:reprod),
    out_l, out_str, out_r = turn(init)
    moved = fwd([lft(out_l), out_str, rght(out_r)])
    out_repro, out_no_repro = maybe(moved)
    return starve([repro(out_repro), out_no_repro])

view_sched(general; names=N)
Example block output

The above was content common to wolves and sheep. The difference is how they eat.

sheep = sheep_eat ⋅ general;   # executed once per sheep

view_sched(sheep; names=N)
Example block output

We use the swap data migration functor F to translate the sheep routine into a wolf one so that it can be composed with the wolf eating step.

wolf = wolf_eat ⋅ F(general);  # executed once per wolf

view_sched(wolf; names=N)
Example block output

Do all sheep, then all wolves, then all daily operations

cycle = (agent(sheep; n=:sheep, ret=I)
         agent(wolf; n=:wolves, ret=I)
         agent(g_inc; n=:grass))

view_sched(cycle; names=N)

Wrap the whole thing in a while loop. Also apply the F2 migration to give
everything coordinates.

overall = while_schedule(cycle, curr -> nparts(curr, :Wolf) >= 0) |> F2

view_sched(overall; names=F2(N))
Example block output

Running the simulation

X = initialize(3, 0.25, 0.25); # 3 × 3 grid, 2 sheep + wolves

Encourage something exciting to happen by placing a wolf on top of a sheep

X[1, :wolf_loc] = X[1, :sheep_loc]
X[1, :wolf_dir] = X[1, :sheep_dir]


Run the simulation for 100 steps

res = interpret(overall, X; maxstep=100);
┌ Warning: Exceeded maximum number of steps
└ @ AlgebraicRewriting.Schedules.Eval ~/work/AlgebraicRewriting.jl/AlgebraicRewriting.jl/src/schedules/Eval.jl:72

Visualizing the results

Run this line to view the trajectory in the generated traj folder

view_traj(overall, res[1:10], view_LV; agent=true, names=F2(N));