By the Batch People
Philippe Caillou, Thomas Louail, Nicolas Marilleau, Huynh Quang Nghi, Serge Stinckwich, Patrick Taillandier, Jean-Daniel Zucker
Define an experiment, batch mode (but same capabilities as GUI mode)
· Parameter exploration: Param element
o Explicit
o List with step
· Method Element
o Exhaustive
o Hill climbing
o Simulated annealing
o Tabu Search
o Reactive Tabu Search
o Genetic Algorithm
· File export
o File
· The experiment (the plan) creates the simulation (future: Headless mode)
· Export agent/simulation data in the file
o =>Define code in the experiment (loop over agents)
· Put rules for variable exploration
o Ex: explore b={1,2,3}. if a=2 explore b={2,3} only
o How? Put code in the exploring loop => put real GAML code in the agent
o => reflex “method” or “explore”
o Some existing exploration actions can be used with a ParameterSet (Map?) parameter
· Show plots in batch
o Define output with the code/variable of the experiment agent
o Require a getVariable() operator that gets the last simulation variables values
· Compare agent trajectories
o Stats a posteriori
o (not possible inside a simulation because there is no end)
o => action « aftersimulation » défined by the user
o Require a getVariable() operator that gets the last simulation variables values (to be able to loop on agents)
· Define more parameters/variables in the file (CSV)
o => use of the Save already working
· Launch parallel simulation on multicore/clusters
o => special action “runSimulation” parameter to launch 4 in parallel?
· “Generate” agents
o Define agent parameters by using global/previous variables/files
o Use of an “initSimulation” action before the “runSimulation” to be able to change manually the simulation parameters
· Définition graphique de l’espace de valeur, éventuellement en 2D et disjoint
o Specific OutPut
Define a new Experimenter agent and put real code/reflexes/actions… GAML code inside the Experimenter.
experiment InteliExplore type: batch repeat: 2 keep_seed: true until: ( time > 200 ) {
int nbprey init:0;
int nbpredator init:0;
init
{
//for example read experiment plan from file
}
reflex method
{
loop prm over:prey_max
{
do: createsimulation
setParameter("nb_preys_init",pr)
do: initsimulation
if (prey_max<100)
setParameter("nb_predators_init",200)
if (prey_max>100)
setParameter("nb_predators_init",200)
do: runsimulation;
do: aftersimulation;
}
}
action aftersimulation
{
loop over getVariable(agents)
{
log("everything dans file"); //with the save function?
}
}
chart name: 'Species evolution' type: series background: rgb('white') size: {1,0.4} position: {0, 0.05}
{
data initial_number_of_preys value: getValue("nb_preys_init") color: rgb('blue') ;
data final_number_of_predator value: getValue("nb_predators") color: rgb('red') ;
}
file name: 'logsimple' type: text data: 'simulation: '+ step
+'; allparams' + getAllParamters // PROBLEME comment sauver tous les parametres?
+ '; nbPreys: ' + getValue("nb_preys_init")
+ '; finalPredators: ' + getValue("nb_preys") ;
}
And Alexis did almost all this in less than a day……. But how does he do that??
A new type of Custom Experiment
In this experiment block, an ExperimenterAgent manages the simulations
Init() may have to create a Simulation to get a Scheduler (?) and/or a World(?)
He must have a Scheduler to be able to stop/wait in a loop for the end of a Simulation (contradiction with previous point…)
Its most important function is to be able to call Simulation.step()
=>Create a Simulation Type which has a Step() operator
Create a ExperimentatorSkill with a lot of usefull actions (Step(int), …)
Check that it is possible to define plots, logs, …