There are several types of AI agents, each designed to perform specific tasks and functionalities based on how they interact with their environment. The main categories include reactive agents, deliberative agents, and hybrid agents. Reactive agents respond to stimuli in their environment without storing past experiences. For instance, a classic example is a simple chess program that only evaluates the current board state to decide on its next move, without considering previous games or positions.
Deliberative agents, on the other hand, have a more complex structure. They maintain an internal model of the world and use it to make decisions. This type of agent can plan ahead since it evaluates potential future states and actions. A well-known example is an autonomous robot navigating through an environment. It not only reacts to obstacles but also plans a path to reach a destination while considering factors like time and resources. This planning capability is crucial for tasks that require foresight and strategic thinking.
Hybrid agents combine features of both reactive and deliberative agents, allowing for more advanced behavior. They can quickly react to immediate situations while also planning for long-term goals. An example of a hybrid agent is an AI system used in video games that needs to adapt to player actions while also following a storyline. By integrating both reactive and planning capabilities, hybrid agents can provide a richer user experience and tackle more complex problems across various domains, from gaming to robotic navigation.