What Are Model-Based Agents?

Model-based agents are intelligent systems that utilize internal memory and percept history to create a model of their environment. This model helps them make informed decisions. These agents follow a structured process involving sensing, modeling, reasoning, and acting. By considering both observable and unobservable aspects of their environment, they can update their internal state and adapt their actions accordingly.

How do model-based agents operate?

Model-based agents operate through a four-stage process: sensing, modeling, reasoning, and acting. They perceive their environment using sensors, construct an internal model from these perceptions, use this model to decide on actions based on predefined rules, and then execute the chosen actions. This cycle allows them to adapt to changes in their environment and make decisions that are informed by both current and past percepts.

What are the advantages of model-based agents?

Model-based agents offer several advantages, including improved decision-making, adaptability, and the ability to handle complex environments. By maintaining an internal model, these agents can make more informed decisions and adapt to changes in their environment. This makes them suitable for applications where the environment is dynamic and unpredictable, such as autonomous vehicles, robotics, and intelligent personal assistants.