Every agent has a private state. Is the agent of height 1 or height 2? Is the agent's name "foo" or is its name = "bar"? These properties are expressed and saved on the agent's state.

Your agents can have any fields you want. Here's an agent that uses the Monte-Carlo method to approximate the value of pi via randomness:

// LeaderAgent
{
    spawned_datapoints: 0,
    neighbor_points: 0,
    pi_estimate: 0,
    behaviors: ["spawn_samples", "estimate_pi"],
    position: [0, 0, 0],
    agent_name: "leader",
    search_radius: 1,
}

Notice how we use multiple custom fields to store data in the agent.

Getting and Setting State

The state object has accessor methods for getting and setting state.

• state.get(field_name) : returns the value of the field

• state.set(field_name, value) : sets the field as the value

Example:

This behavior takes in the current state and context of the agent, adds 1 to the age property stored on the state, and then returns the state.

function behavior(state, context){
    let age = state.get("age");
    age += 1;
    state.set("age", age);
}

def behavior(state, context):
    age = state.get("age")
    age += 1
    state.set("age", age)

Important: Only the agent can modify its own state. If an agent wants to prompt another agent to perform a state change, it can send a message to trigger an update.

Modify

HASH also has a "modify" helper function which allows you to make certain state changes a one-liner:

• state.modify(field_name, function)

will allow you to pass a function that will be run on the given field in the agent's state. The above example can now be written like so:

function behavior(state, context) {
    state.modify("age", age => age + 1)
}

# We haven't yet enabled this helper function for Python

Reserved Fields

While an agent can store arbitrary data in its own state, some state values have special meaning in HASH. The fields below are all reserved, in addition to fields tied to visualization which can be found here.

{ 
  agent_id: string // auto-created identifier. Agents receive messages to their ID.
  agent_name: string // optional identifier. Agent's receive messages to their name. 
  behaviors: [] // Filenames of the behaviors that the agent apply to advance their state every simulation step N to N+1. 
  messages: [] // Outbound messages from agent 
  position: [x, y, z] // display's agents in the viewer and used to calculate neighbors 
  search_radius: i64// context.neighbors() will return a list of all the agents within the search radius
}