To create swarm agents using Tools4AI AI agent framework based on Jade and we can leverage both platforms’ abilities to implement multi-agent systems (MAS) and action-based decision making. A swarm of agents involves multiple agents working together to achieve a common goal, often in a decentralized manner where they follow simple rules to solve complex tasks.
Code examples and agentic framework is here
Key Concepts of Swarm Agents:
Decentralization: Agents operate independently but can collaborate based on simple communication.
Scalability: The system can grow by adding more agents without significantly increasing complexity.
Self-organization: Agents adjust their behavior based on the environment and their peers.
Steps to Create Swarm Agents:
- Set Up Jade Multi-Agent Platform Install Jade: Ensure that Jade is integrated into our project. Create the Agent Classes: In Jade, we define agents by extending the jade.core.Agent class. Each agent can have behaviors like responding to tasks, exploring the environment, or collaborating with other agents. Example: A simple Jade agent
import jade.core.Agent;
import jade.core.behaviours.Behaviour;
public class SwarmAgent extends Agent {
protected void setup() {
System.out.println("Hello! Swarm Agent " + getAID().getName() + " is ready.");
addBehaviour(new ExploreBehaviour());
}
private class ExploreBehaviour extends Behaviour {
public void action() {
// Logic for the agent's exploration behavior
}
public boolean done() {
return false; // Continue indefinitely (for example, exploration)
}
}
}
Implement Swarm Behavior (Coordination & Communication)
In swarm agents, individual agents should be able to communicate with each other. This can be achieved through JADE’s FIPA-compliant messaging.
Broadcasting Messages: Agents send messages to other agents to coordinate, such as sharing task status or updates on the environment.
Example: Sending a message in Jade
import jade.lang.acl.ACLMessage;
public void sendMessageToSwarm() {
ACLMessage msg = new ACLMessage(ACLMessage.INFORM);
msg.setContent("Exploring new region...");
msg.addReceiver(new AID("Agent2", AID.ISLOCALNAME));
send(msg);
}
Receiving and Processing Messages: Agents should also be able to receive messages and act accordingly.
Example: Receiving messages
import jade.lang.acl.ACLMessage;
protected void receiveMessage() {
ACLMessage msg = receive();
if (msg != null) {
System.out.println("Received: " + msg.getContent());
} else {
block(); // Block until a new message is received
}
}
Use Tools4AI for Decision-Making & Actions
Tools4AI framework can add intelligence to these agents by allowing them to perform complex actions based on NLP prompts and predefined actions. Tools4AI’s ability to convert natural language instructions into Java method calls will be beneficial for the agents to dynamically decide actions based on environmental inputs.
@Predict and @Action Annotations in Tools4AI: Agents can call actions based on input from the environment and other agents.
Example: Agent invoking Tools4AI action
@Action
public void exploreNewArea(String area) {
// Logic to explore a specific area
System.out.println("Exploring: " + area);
}
Implement Swarm Logic
Swarm behavior could be often rule-based, where agents follow simple rules to work together toward a goal. Some common strategies include:
Follow the leader: One agent acts as a leader, and others follow.
Distributed Task Allocation: Each agent takes a portion of the work and updates the others about progress.
Search and Rescue: Agents spread out and search the environment, then communicate findings.
Example: Simple swarm behavior (search and notify)
public class ExploreBehaviour extends Behaviour {
public void action() {
// Explore logic
System.out.println("Agent exploring the area...");
sendMessageToSwarm(); // Notify others
}
public boolean done() {
return false; // Continue exploring
}
}
Swarm Communication and Cooperation
We can use JADE’s Directory Facilitator (DF) to register agents and discover other agents in the swarm. This helps in decentralized coordination.
Example: Registering an agent
import jade.domain.DFService;
import jade.domain.FIPAException;
import jade.domain.FIPAAgentManagement.DFAgentDescription;
import jade.domain.FIPAAgentManagement.ServiceDescription;
protected void setup() {
DFAgentDescription dfd = new DFAgentDescription();
dfd.setName(getAID());
ServiceDescription sd = new ServiceDescription();
sd.setType("swarm-agent");
sd.setName("Swarm Task");
dfd.addServices(sd);
try {
DFService.register(this, dfd);
} catch (FIPAException fe) {
fe.printStackTrace();
}
}
Scaling the Swarm
Swarm agents should be easily scalable. We can clone agents or dynamically create new agents in response to tasks or environmental changes.
Example: Cloning a new agent
import jade.wrapper.AgentController;
import jade.wrapper.ContainerController;
ContainerController cc = getContainerController();
AgentController ac = cc.createNewAgent("SwarmAgent", "SwarmAgentClass", null);
ac.start();
Monitor and Control the Swarm
To keep the system under control, we can develop a master agent or supervisor to manage overall swarm behavior. This agent will collect information from the swarm agents and adjust their tasks as needed.
Advanced Considerations:
Distributed Learning: We could use machine learning to make swarm agents smarter over time by allowing them to learn from their actions and the environment.
Agent Autonomy: Using Tools4AI, we can give agents more autonomy in their decision-making process by invoking different action scripts based on real-time conditions.
Safety and Bias Control: Swarm framework can help ensure agents’ actions align with ethical guidelines and standards, preventing harmful or biased behavior.
Summary:
Use JADE to build and manage the core multi-agent behavior.
Use Tools4AI to provide decision-making capabilities, utilizing NLP and action scripts.
Implement swarm logic using simple rules and communication between agents.
Leverage JADE’s messaging and directory features for agent cooperation and communication.
Add control mechanisms for scaling, monitoring, and regulating swarm agents.
By combining the multi-agent capabilities of Jade and the intelligence and action management from Tools4AI, we can create a highly adaptive and scalable swarm agent framework.
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