What is a multi-agent system in AI
multi-agent system in AI is a collection of autonomous agents that interact or collaborate to solve complex tasks or simulate environments. These agents communicate, coordinate, and sometimes compete to achieve individual or shared goals.Multi-agent system (MAS) is an AI architecture that consists of multiple interacting autonomous agents working together or competitively to solve problems or simulate complex behaviors.How it works
A multi-agent system works by having multiple autonomous agents that perceive their environment, make decisions, and act independently or collaboratively. Imagine a team of robots cleaning a building: each robot (agent) has its own sensors and goals but communicates with others to avoid overlap and optimize coverage. Agents can cooperate, negotiate, or compete, depending on the system design.
This decentralized approach allows complex problems to be broken down into smaller tasks handled by individual agents, improving scalability and robustness.
Concrete example
Here is a simple Python example simulating two agents negotiating resource allocation using message passing:
class Agent:
def __init__(self, name, resource):
self.name = name
self.resource = resource
def propose(self, other):
offer = self.resource // 2
print(f"{self.name} proposes {offer} units to {other.name}")
return offer
def respond(self, offer):
if offer >= 3:
print(f"{self.name} accepts the offer of {offer} units")
return True
else:
print(f"{self.name} rejects the offer of {offer} units")
return False
# Create two agents
agentA = Agent("AgentA", 10)
agentB = Agent("AgentB", 5)
# AgentA proposes to AgentB
offer = agentA.propose(agentB)
# AgentB responds
accepted = agentB.respond(offer)
if accepted:
print("Agreement reached.")
else:
print("Negotiation failed.")AgentA proposes 5 units to AgentB AgentB accepts the offer of 5 units Agreement reached.
When to use it
Use a multi-agent system when problems require distributed decision-making, parallel task execution, or simulation of complex interactions, such as:
- Robotics teams coordinating tasks
- Traffic or crowd simulation
- Distributed sensor networks
- Automated trading agents in finance
- Game AI with multiple characters
Do not use MAS when a single centralized model can efficiently solve the problem or when agent coordination overhead outweighs benefits.
Key terms
| Term | Definition |
|---|---|
| Agent | An autonomous entity capable of perceiving and acting in an environment. |
| Multi-agent system (MAS) | A system composed of multiple interacting agents. |
| Coordination | Mechanisms agents use to align their actions towards common goals. |
| Negotiation | Process where agents communicate to reach mutually beneficial agreements. |
| Autonomy | Degree to which an agent operates independently without external control. |
Key Takeaways
- A multi-agent system consists of multiple autonomous agents collaborating or competing to solve complex tasks.
- Agents communicate and coordinate to improve scalability and robustness in distributed environments.
- Use MAS for problems requiring decentralized control, parallelism, or simulation of interactive behaviors.