This first snippet is comprised of the following functionality:
Creating a multi-agent simulation involves creating multiple agents that interact within a defined environment. The agents can have different strategies and attributes, and they can interact with each other and with the environment according to predefined rules.
**Explanation:**
1. **Agent class**
- `id`: A unique identifier for the agent.
- `position`: The agent’s position in the one-dimensional environment.
- `strategy`: The strategy the agent uses, affecting its movement and interaction behaviors.
- `act`: Defines how the agent behaves during each step of the simulation. Here it moves randomly within the bounds of the environment.
- `interact`: Defines how the agent interacts with another agent.
2. **Environment class**
- `size`: The size of the one-dimensional environment.
- `agents`: A list of agents in the environment.
- `step`: Defines how the environment updates during each step of the simulation. It tells each agent to act and then checks for interactions between all pairs of agents.
3. **Strategy class**
- `stepSize`: The maximum distance an agent can move in one step.
- `interactionRange`: The maximum distance at which agents can interact.
4. **Main function**
- We define two strategies and create four agents using random initial positions and the defined strategies.
- We create an environment using a defined size and the list of agents.
- We run the simulation for 10 steps, with the environment updating during each step.
This is a rudimentary multi-agent simulation; you can expand it by adding more sophisticated behaviours, interaction rules, and environmental features to build a more complex simulation.
Below is a JavaScript source code for a very simple multi-agent simulation where agents move in a one-dimensional space and have a simple interaction rule based on their proximity to one another:
# Source Code Snippet:
class Agent {
constructor(id, position, strategy) {
this.id = id;
this.position = position;
this.strategy = strategy;
}
// Define the action the agent takes each step
act(environment) {
let newPosition = this.position + (Math.random() - 0.5) * 2 * this.strategy.stepSize;
newPosition = Math.max(0, Math.min(newPosition, environment.size - 1));
this.position = newPosition;
}
// Define how agents interact with each other
interact(otherAgent) {
if (Math.abs(this.position - otherAgent.position) < this.strategy.interactionRange) {
console.log(`Agent ${this.id} interacted with Agent ${otherAgent.id}`);
}
}
}
class Environment {
constructor(size, agents) {
this.size = size;
this.agents = agents;
}
// Define how the environment updates each step
step() {
for (let agent of this.agents) {
agent.act(this);
}
for (let i = 0; i < this.agents.length; i++) {
for (let j = i + 1; j < this.agents.length; j++) {
this.agents[i].interact(this.agents[j]);
}
}
}
}
class Strategy {
constructor(stepSize, interactionRange) {
this.stepSize = stepSize;
this.interactionRange = interactionRange;
}
}
function main() {
// Define strategies
let strategyA = new Strategy(1, 2);
let strategyB = new Strategy(2, 4);
// Create agents with different strategies
let agents = [
new Agent(0, Math.random() * 100, strategyA),
new Agent(1, Math.random() * 100, strategyB),
new Agent(2, Math.random() * 100, strategyA),
new Agent(3, Math.random() * 100, strategyB),
];
// Create environment
let environment = new Environment(100, agents);
// Run simulation for 10 steps
for (let i = 0; i < 10; i++) {
console.log(`Step ${i}`);
environment.step();
}
}
main();