6. Breeding

So far we have created agents only during the initialization of the simulation. In this sixth step, we illustrate how to create new agents during a simulation of a dynamic species.

Formulation

• Adding of a reproduce behavior for the prey and predator species:
  • When an agent has enough energy, it has a certain probability to have a certain number of offspring.
  • The energy of the offspring is equal to the parent energy divided by the number of offspring.
  • The parent gets the same energy as its offspring.

Model Definition

parameters

We add six new parameters related to breeding:

• The reproduction probability for prey agents
• The max number of offspring for prey agents
• The minimum energy to reproduce for prey agents
• The reproduction probability for predator agents
• The max number of offspring for predator agents
• The minimum energy to reproduce for predator agents

We define six new global variables in the global section:

global {
    ...
    float prey_proba_reproduce <- 0.01;
    int prey_nb_max_offsprings <- 5; 
    float prey_energy_reproduce <- 0.5; 
    float predator_proba_reproduce <- 0.01;
    int predator_nb_max_offsprings <- 3;
    float predator_energy_reproduce <- 0.5;
    ...
}

We define then the six corresponding parameters in the experiment:

parameter "Prey probability reproduce: " var: prey_proba_reproduce category: "Prey" ;
parameter "Prey nb max offsprings: " var: prey_nb_max_offsprings category: "Prey" ;
parameter "Prey energy reproduce: " var: prey_energy_reproduce category: "Prey" ;
parameter "Predator probability reproduce: " var: predator_proba_reproduce category: "Predator" ;
parameter "Predator nb max offsprings: " var: predator_nb_max_offsprings category: "Predator" ;
parameter "Predator energy reproduce: " var: predator_energy_reproduce category: "Predator" ;

parent species

The reproduction dynamics is the same for both prey and predator species, it can thus be implemented only once inside the parent species. But the values of the parameters will depend on the species, and thus have to be set with different values inside each of them.

We add three new variables for the generic_species:

• proba_reproduce
• nb_max_offsprings
• energy_reproduce

We add as well a new reflex called reproduce:

• this reflex is activated only when:
  • The energy of the agent is greater or equals to energy_reproduce
  • AND according to the probability proba_reproduce: for this second condition, we use the flip(proba) operator that returns true according to the probability proba (false otherwise).
• this reflex creates nb_offsprings (random number between 1 and nb_max_offsprings) new agents of the species of the agent using the create statement: we use a species casting operator on the current agent.
  • the created agents are initialized as follows:
    • my_cell: my_cell of the agent creating the agents,
    • location: location of my_cell,
    • energy: energy of the agent creating the agents (use of keyword myself) divided by the number of offsprings.
• after the agent creation, the reflex updates the energy value of the current agent with the value: energy / nb_offsprings.

species generic_species {
    ...
    float proba_reproduce ;
    int nb_max_offsprings;
    float energy_reproduce;
    ... 
    reflex reproduce when: (energy >= energy_reproduce) and (flip(proba_reproduce)) {
        int nb_offsprings <- rnd(1, nb_max_offsprings);
        create species(self) number: nb_offsprings {
            my_cell <- myself.my_cell ;
            location <- my_cell.location ;
            energy <- myself.energy / nb_offsprings ;
        }
        energy <- energy / nb_offsprings ;
    }
}

Note that two keywords (pseudo-variables) can be used to make explicit references to some agents:

• The agent that is currently executing the statements inside the block (for example a newly created agent): self
• The agent that is executing the statement that contains this block (for instance, the agent that has called the create statement): myself

prey species

We specialize the prey species from the generic_species species as follows:

• definition of the initial value of the agent variables

   species prey parent: generic_species {
      ...
      float proba_reproduce <- prey_proba_reproduce ;
      int nb_max_offsprings <- prey_nb_max_offsprings ;
      float energy_reproduce <- prey_energy_reproduce ;
      ...
   }

predator species

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:

   species predator parent: generic_species {
      ...
      float proba_reproduce <- predator_proba_reproduce ;
      int nb_max_offsprings <- predator_nb_max_offsprings ;
      float energy_reproduce <- predator_energy_reproduce ;
      ...
   }

Complete Model

msi.gama.models/models/Tutorials/Predator%20Prey/models/Model%2006.gaml

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See full example on GitHub