Although it might seem that single agent systems should be simpler than multiagent systems, the opposite is in fact the case (see Figure 2). Distributing control among multiple agents allows each agent to be simpler. No one agent has to be able to complete a given task on its own. Thus single agent systems belong at the end of the progression from simple to complex multiagent systems in Sections 6 - 8. They are described here first because to many people single agent approaches are more intuitive than multiagent ones.
In general, the agent in a single agent system models itself, the environment, and their interactions. Of course the agent is itself part of the environment, but for the purposes of this article, agents are considered to have extra-environmental components as well. They are independent entities with their own goals, actions, and knowledge. In a single agent system, no other such entities are recognized by the agent. Thus, even if there are indeed other agents in the world, they are not modeled as having goals, etc.: they are just considered part of the environment. The point being emphasized is that although agents are also a part of the environment, they are explicitly modeled as having their own goals, actions, and domain knowledge (see Figure 4).
Figure 4: A general single agent framework. The agent models itself,
the environment, and their interactions. If other agents exist,
they are considered part of the environment.
In the pursuit domain, a single agent approach is possible: the agent can observe the positions of all four predators and decide how each of them should move. Since the prey moves randomly rather than intentionally, it is not associated with any agent. Instead it is considered part of the environment as shown in Figure 5.
Figure 5: The pursuit domain with just a single agent. One agent
controls all predators and the prey is considered part of the
environment.