Add a single agent to the simulation

Usage

add_agent(
  model,
  group_number = 1,
  goal_number = 5,
  goal_duration = function(x) rnorm(x, 10, 2),
  precompute_goal_paths = TRUE,
  sort_goals = TRUE,
  precomputed_goals = NULL,
  middle_edge = FALSE,
  precomputed_edges = NULL,
  many_nodes = !is.null(precomputed_edges),
  space_between = 1.25,
  position = NULL,
  standing_start = 0.1,
  individual_differences = FALSE,
  return_characteristics = FALSE
)

Arguments

model: Object of the predped-class.
group_number: Numeric denoting the group to which the agent belongs. Defaults to 1.
goal_number: Numeric, vector, or function that defines the number of goals the agents should accomplish. It's exact value is handled by determine_values. Defaults to \(n) rnorm(n, 10, 2).
goal_duration: Numeric, vector, or function that defines the duration of the goals of the agents. Defaults to \(n) rnorm(n, 10, 2).
precompute_goal_paths: Logical denoting whether to run the find_path for each of the generated goals beforehand. Assumes that the agent does all of the goals in the order of the goal stack. Defaults to FALSE.
sort_goals: Logical denoting whether to order the goal stack in a logical way. Currently implemented in the following way. First, we select the first goal as being the one that is closest by the starting position provided in the argument starting_position. Then, we define each of the next goals as being the one that is closest to the position of the previous goal. Defaults to TRUE.
precomputed_goals: List of goal stacks from which the agent can be assigned one. Defaults to NULL, triggering the creation of goal stacks in the simulation.
middle_edge: Logical denoting whether to sample the goals from the middle of the edge of the objects in the link[predped]{background-class} (TRUE) or to allow the goal locations to fall on all points on these edges (FALSE). Defaults to FALSE.
precomputed_edges: Output of compute_edges containing the nodes and edges the agent can use to plan its path. Defauls to NULL, triggering the creation of these edges whenever they are needed.
many_nodes: Logical denoting whether to use many nodes when computing the edges. Defaults to FALSE if precomputed_edges = FALSE.
space_between: Numeric denoting the space that should be left between an object and the created path points for the agents (in radii). Defaults to 2.5, meaning a space of 2.5 * radius(agent) is left between an object and the path points agents use in their strategy. Ignored if precomputed_edges is provided.
position: Numeric denoting the position you would like to assign to the agent. Defaults to NULL, making the agent start at the entrance. Note that this is an experimental feature that has not been tested yet, and therefore might not work for the moment.
standing_start: Numeric denoting the factor of their preferred speed that agents move when they just came from standing still. Defaults to 0.1.
individual_differences: Logical denoting whether to use the standard deviations in the parameter list to create some variation in the parameters. Defaults to FALSE.
return_characteristics: Logical denoting whether to return the characteristics of the generated agents in a list (TRUE) or as part of an agent (FALSE). Defaults to FALSE.

Value

List of instances of the agent-class.

Examples

# Create a setting in which to simulate.
my_background <- background(shape = rectangle(center = c(0, 0),
                                              size = c(2, 2)),
                            objects = list(rectangle(center = c(0, 0),
                                                     size = c(1, 1))))

# Create a model from which to simulate
my_model <- predped(setting = my_background,
                    archetypes = c("BaselineEuropean",
                                   "DrunkAussie"))

# Generate an agent
my_agent <- add_agent(my_model, goal_number = 5)
my_agent
#> Agent Attributes 
#> center: -0.6925 0 
#> cell: 0 
#> color: goldenrod 
#> current_goal: (a) position: -0.5070711 -0.2293804 (b) path: -0.5070711 -0.2293804 
#> goals (number): 4 
#> group: 1 
#> id: aaglv 
#> orientation: 0 
#> parameters: 
#>                          [,1]
#> radius                   0.25
#> slowing_time             1.00
#> preferred_speed          1.00
#> randomness               5.00
#> stop_utility          1000.00
#> reroute                 20.00
#> b_turning                0.20
#> a_turning                2.00
#> b_current_direction      1.00
#> a_current_direction      2.00
#> blr_current_direction    0.10
#> b_goal_direction        10.00
#> a_goal_direction         2.00
#> b_blocked                4.00
#> a_blocked                2.00
#> b_interpersonal          1.00
#> a_interpersonal          2.00
#> d_interpersonal          0.00
#> b_preferred_speed        1.00
#> a_preferred_speed        2.00
#> b_leader                 1.00
#> a_leader                 2.00
#> d_leader                 0.00
#> b_buddy                  0.00
#> a_buddy                  0.00
#> a_group_centroid         0.00
#> b_group_centroid         0.00
#> b_visual_field           0.00
#> central                  0.00
#> non_central              0.00
#> acceleration             0.00
#> constant_speed           0.00
#> deceleration             0.00
#> 
#> radius: 0.25 
#> speed: 0.1 
#> status: move 
#> 
#> For more detailed information, please extract the wanted information from the agent directly.

Add a single agent to the simulation

Usage

Arguments

Value

See also

Examples