PredatorPrey_step5
This fifth step illustrates how to use parent species. Indeed, prey and predators share a few common features thus we will define a generic species that will regroup all the common elements (variables, behaviors, and aspects) between the prey and the predator species.
- Definition of a new generic species:
generic_species. - Definition of a new species:
predator. -
predatoragents move randomly. - At each simulation step, a
predatoragent can eat apreythat is localized at its grid cell.
We add four new parameters related to predator agents:
- The init number of predator agents.
- The max energy of the predator agents.
- The energy gained by a predator agent while eating a prey agent.
- The energy consumed by a predator agent at each time step.
We define four new global variables in the global section:
global {
...
int nb_predators_init <- 20;
float predator_max_energy <- 1.0;
float predator_energy_transfer <- 0.5;
float predator_energy_consum <- 0.02;
}
We define then the four corresponding parameters in the experiment:
parameter "Initial number of predators: " var: nb_predators_init min: 0 max: 200 category: "Predator" ;
parameter "Predator max energy: " var: predator_max_energy category: "Predator" ;
parameter "Predator energy transfer: " var: predator_energy_transfer category: "Predator" ;
parameter "Predator energy consumption: " var: predator_energy_consum category: "Predator" ;
A species can have a parent species: it automatically gets all the variables, skill and actions/reflex of the parent species.
We define a species called generic_species that is the parent of the species prey and predator:
This species integrates all of the common feature between the prey and predator species:
- the variables:
sizecolormax_energymax_transferenergy_consummy_cellenergy
- the behaviors:
-
basic_movereflex -
diereflex
-
- the aspect:
-
baseaspect
-
species generic_species {
float size <- 1.0;
rgb color ;
float max_energy;
float max_transfert;
float energy_consum;
vegetation_cell my_cell <- one_of (vegetation_cell) ;
float energy <- rnd(max_energy) update: energy - energy_consum max: max_energy ;
init {
location <- my_cell.location;
}
reflex basic_move {
my_cell <- one_of (my_cell.neighbors2) ;
location <- my_cell.location ;
}
reflex die when: energy <= 0 {
do die ;
}
aspect base {
draw circle(size) color: color ;
}
}
We specialize the prey species from the generic_species species:
- definition of the initial value of the agent variables.
- definition of the
eatreflex.
species prey parent: generic_species {
rgb color <- #blue;
float max_energy <- prey_max_energy ;
float max_transfert <- prey_max_transfert ;
float energy_consum <- prey_energy_consum ;
reflex eat when: my_cell.food > 0 {
float energy_transfert <- min([max_transfert, my_cell.food]) ;
my_cell.food <- my_cell.food - energy_transfert ;
energy <- energy + energy_transfert ;
}
}
As done for the prey species, we specialize the predator species from the generic_species species:
- definition of the initial value of the agent variables
- definition of a new variable
reachable_preysconsisting in the list of prey agents contains inmy_cell; compute at each simulation step (when the agent is activated). - definition of the
eatreflex: activated whenreachable_preysis not empty; choose one of the elements of this list, ask it to die; and update thepredatorenergy.
species predator parent: generic_species {
rgb color <- #red ;
float max_energy <- predator_max_energy ;
float energy_transfert <- predator_energy_transfert ;
float energy_consum <- predator_energy_consum ;
list<prey> reachable_preys update: prey inside (my_cell);
reflex eat when: ! empty(reachable_preys) {
ask one_of (reachable_preys) {
do die ;
}
energy <- energy + energy_transfert ;
}
}
Note that we used the ask statement. This statement allows to make a remote agent executes a list of statements.
Use of the ask statement as follows:
ask one_agent { }
or
ask agents_list { }
We used as well the species/agent list inside geometry/agent operator. This operator returns all the agents of the specified species (or from the specified agent list) that are inside the given geometry or agent geometry.
Like in the previous model, in the init block of the model, we create nb_predators_init.
global {
...
init {
create prey number: nb_preys_init ;
create predator number: nb_predators_init ;
}
}
Like in the previous model, we define a monitor to display the number of predator agents.
Definition of a global variable nb_predator that returns the current number of predator agents:
global {
...
int nb_predators -> {length (predator)};
...
}
Definition of the corresponding monitor:
monitor "number of predators" value: nb_predators ;
Also, do not forget to add the line to display predators in your simulation
display main_display {
...
species predator aspect: base ;
}
model prey_predator
global {
int nb_preys_init <- 200;
int nb_predators_init <- 20;
float prey_max_energy <- 1.0;
float prey_max_transfert <- 0.1 ;
float prey_energy_consum <- 0.05;
float predator_max_energy <- 1.0;
float predator_energy_transfert <- 0.5;
float predator_energy_consum <- 0.02;
int nb_preys -> {length (prey)};
int nb_predators -> {length (predator)};
init {
create prey number: nb_preys_init ;
create predator number: nb_predators_init ;
}
}
species generic_species {
float size <- 1.0;
rgb color ;
float max_energy;
float max_transfert;
float energy_consum;
vegetation_cell my_cell <- one_of (vegetation_cell) ;
float energy <- rnd(max_energy) update: energy - energy_consum max: max_energy ;
init {
location <- my_cell.location;
}
reflex basic_move {
my_cell <- one_of (my_cell.neighbors2) ;
location <- my_cell.location ;
}
reflex die when: energy <= 0 {
do die ;
}
aspect base {
draw circle(size) color: color ;
}
}
species prey parent: generic_species {
rgb color <- #blue;
float max_energy <- prey_max_energy ;
float max_transfert <- prey_max_transfert ;
float energy_consum <- prey_energy_consum ;
reflex eat when: my_cell.food > 0 {
float energy_transfert <- min([max_transfert, my_cell.food]) ;
my_cell.food <- my_cell.food - energy_transfert ;
energy <- energy + energy_transfert ;
}
}
species predator parent: generic_species {
rgb color <- #red ;
float max_energy <- predator_max_energy ;
float energy_transfert <- predator_energy_transfert ;
float energy_consum <- predator_energy_consum ;
list<prey> reachable_preys update: prey inside (my_cell);
reflex eat when: ! empty(reachable_preys) {
ask one_of(reachable_preys) {
do die ;
}
energy <- energy + energy_transfert ;
}
}
grid vegetation_cell width: 50 height: 50 neighbors: 4 {
float max_food <- 1.0 ;
float food_prod <- rnd(0.01) ;
float food <- rnd(1.0) max: max_food update: food + food_prod ;
rgb color <- rgb(int(255 * (1 - food)), 255, int(255 * (1 - food))) update: rgb(int(255 * (1 - food)), 255, int(255 *(1 - food))) ;
list<vegetation_cell> neighbors2 <- (self neighbors_at 2);
}
experiment prey_predator type: gui {
parameter "Initial number of preys: " var: nb_preys_init min: 0 max: 1000 category: "Prey" ;
parameter "Prey max energy: " var: prey_max_energy category: "Prey" ;
parameter "Prey max transfert: " var: prey_max_transfert category: "Prey" ;
parameter "Prey energy consumption: " var: prey_energy_consum category: "Prey" ;
parameter "Initial number of predators: " var: nb_predators_init min: 0 max: 200 category: "Predator" ;
parameter "Predator max energy: " var: predator_max_energy category: "Predator" ;
parameter "Predator energy transfert: " var: predator_energy_transfert category: "Predator" ;
parameter "Predator energy consumption: " var: predator_energy_consum category: "Predator" ;
output {
display main_display {
grid vegetation_cell lines: #black ;
species prey aspect: base ;
species predator aspect: base ;
}
monitor "Number of preys" value: nb_preys;
monitor "Number of predators" value: nb_predators;
}
}