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It is possible to use in the models a set of built-in agents. These agents allow to directly use some advance features like clustering, multi-criteria analysis, etc. The creation of these agents are similar as for other kinds of agents:
create species: my_built_in_agent returns: the_agent;
So, for instance, to be able to use clustering techniques in the model:
create cluster_builder returns: clusterer;
agent, AgentDB, base_edge, experiment, graph_edge, graph_node, model, physical_world,
agent_init_unknown_step_unknownAgentDBcloseunknownconnectreturns: unknown
→ params (map): Connection parameters
executeUpdatereturns: int
updateComm (string): SQL commands such as Create, Update, Delete, Drop with question markvalues (list): List of values that are used to replace question markgetParameterunknowninsertreturns: int
into (string): Table namecolumns (list): List of column name of tablevalues (list): List of values that are used to insert into table. Columns and values must have same sizeisConnectedboolselectreturns: container
select (string): select stringvalues (list): List of values that are used to replace question markssetParameterreturns: unknown
→ params (map): Connection parameters
testConnectionreturns: bool
→ params (map): Connection parameters
timeStampfloatbase_edgeexperimentupdate_outputsForces all outputs to refresh, optionally recomputing their values
returns: unknown
→ recompute (boolean): Whether or not to force the outputs to make a computation step
graph_edgegraph_noderelated_toreturns: bool
→ other (agent):
modelhaltAllows to stop the current simulation so that cannot be continued after. All the behaviors and updates are stopped.
unknownpauseAllows to pause the current simulation ACTUALLY EXPERIMENT FOR THE MOMENT. It can be set to continue with the manual intervention of the user.
unknownphysical_worldcompute_forcesunknown