Multi-agent systems (MAS) and single-agent systems (SAS) are both frameworks used in computing and artificial intelligence, but they differ significantly in their structure and functionality. In a single-agent system, there is only one agent that operates independently to perform tasks. This agent has its own goals and operates in a distinct environment where it perceives inputs, processes information, and takes actions to achieve its objectives. In contrast, a multi-agent system consists of multiple agents that interact with each other in the same environment, often working towards coordinated goals while making decisions based on both individual and communal knowledge.
One of the primary differences lies in the complexity of interactions. In a SAS, the agent’s decisions are typically based on its own data and experiences, which makes the control relatively straightforward. For instance, a simple robotic vacuum cleaner operates as a single agent, mapping its environment and avoiding obstacles based on its own sensors. In a multi-agent system, however, agents must communicate and collaborate or compete with each other. An example of this can be seen in traffic management systems where multiple vehicles (agents) communicate their positions and speeds to optimize traffic flow and reduce congestion, requiring them to make decisions based on the actions of other agents.
Another key distinction is adaptability and scalability. In single-agent systems, adding features or scaling up the functionality can often be managed within the constraints of one agent. In a multi-agent system, however, scaling involves managing new agents and ensuring their interactions are efficient and effective. This can introduce challenges related to coordination and conflict resolution, especially if agents have differing goals or priorities. For example, in a supply chain system employing multiple agents (like suppliers and distributors), agents must negotiate and adjust to changes in demand or supply conditions while maintaining overall efficiency. Thus, the design and implementation of multi-agent systems require careful consideration of these interactions and the overall dynamics of the system.