Single-agent and multi-agent systems are two distinct frameworks used in the design of intelligent systems. A single-agent system involves one autonomous entity that operates independently to achieve its goals. This agent perceives its environment, makes decisions based on its programming and available data, and then acts accordingly. An example of a single-agent system could be a robotic vacuum cleaner. This device navigates through a home, avoiding obstacles and cleaning surfaces based solely on its sensors and preprogrammed strategies, without needing interaction or coordination with other devices.
In contrast, a multi-agent system consists of multiple autonomous agents that can interact and collaborate with one another. These agents may have their individual objectives but work together to achieve a common goal or to improve overall system performance. An illustrative example is a fleet of delivery drones. Each drone can individually assess its path and make decisions about route optimization, but they also communicate with each other to avoid collisions, share load if needed, or redirect based on real-time traffic data. This interaction enhances efficiency and adaptability in dynamic environments.
The complexity of multi-agent systems often requires more sophisticated architecture because the agents must be designed to handle communication, negotiation, and sometimes competition among themselves. In contrast, single-agent systems tend to be simpler in design because there is no need for inter-agent communication. Developers typically choose between these systems based on the problem requirements: if tasks can be effectively solved by a single entity, a single-agent system might be suitable. However, for tasks that benefit from cooperation or that can be divided into multiple concurrent processes, a multi-agent system would be the better choice.