Structure of AI Agent
Last updated
Last updated
To understand the structure of an AI agent, it's essential to grasp the concepts of architecture and agent programs. These two elements form the foundation upon which an AI agent operates, defining how it interacts with its environment, processes information, and takes actions.
The primary task of AI is to design an agent program that implements the agent function. The structure of an intelligent agent can be summarized as:
This formula shows that an AI agent is a combination of the physical or virtual machinery it runs on (architecture) and the software that drives its behavior (agent program). Together, these components allow an AI agent to perceive its environment, reason, and act.
There are three key terms involved in understanding the structure of an AI agent:
The architecture refers to the physical or virtual machinery that the AI agent executes on. This includes all the hardware and software systems necessary for sensing, processing, and acting in an environment. It could be as simple as a computer or as complex as a robotic car outfitted with various sensors and actuators.
For example:
A robotic car has sensors like cameras and radar to perceive its surroundings and actuators like motors to move or change direction.
A virtual assistant runs on a computer and takes inputs such as voice commands, then generates outputs like spoken responses or actions.
The architecture provides the foundation that supports the agent's ability to interact with its environment.
The agent function is a formal description of the mapping between the agent’s perceptions and the actions it should take. The agent function takes a sequence of perceptions, known as the percept sequence, and outputs an action.
Mathematically, the agent function can be written as:
Where:
P* represents the percept sequence, which is the complete history of everything the agent has perceived up to the current moment.
A represents the actions the agent can take in response to those perceptions.
In simpler terms, the agent function tells the AI agent how to behave based on its entire history of perceptions.
The agent program is the actual software implementation of the agent function. It runs on the architecture and uses the agent function to map the percept sequence to an action. The agent program processes the inputs from the environment (perceptions) and applies the logic defined by the agent function to determine the appropriate action.
For example:
In a robotic car, the agent program might process sensor data like the distance to obstacles and the speed of the car, and then decide whether to accelerate, turn, or stop.
In a virtual assistant, the agent program might take user commands and map them to actions such as sending a message or setting a reminder.
The agent program executes on the architecture, allowing the AI agent to interact with and adapt to its environment.
The percept sequence is the history of everything the agent has perceived up to the present moment. This is critical because the agent does not make decisions based solely on the current input but also considers all past experiences. This accumulation of information allows the agent to make more informed decisions, which is particularly important in dynamic or unpredictable environments.
For instance:
A robotic car navigating a city street uses its percept sequence to remember the location of obstacles or traffic patterns, helping it to anticipate upcoming events and act more effectively.
Once the agent program processes the percept sequence using the agent function, the agent takes an action via its actuators. The nature of these actions depends on the architecture:
A robotic car might accelerate, turn, or brake based on the processed data.
A virtual assistant might send a message, display information, or execute a function within an application.
This action component closes the perception-action loop, where the agent constantly updates its percept sequence, processes it, and acts accordingly.
To better understand the structure, consider a self-driving car as an AI agent. The car’s architecture includes sensors like cameras and radar, a central computer to process data, and actuators that control the car's steering, acceleration, and braking. The agent program, running on the car’s computer, takes input from the percept sequence — such as current speed, traffic signals, and nearby obstacles—and uses the agent function to determine the best action, like slowing down, speeding up, or turning.
This process happens continuously as the agent navigates through its environment, ensuring the car operates safely and efficiently.
The structure of an AI agent is a combination of its architecture (the machinery it runs on) and its agent program (the software that maps perceptions to actions using the agent function). This combination allows the AI agent to perceive, reason, and act in its environment. By taking into account the percept sequence (the history of all perceptions), the agent can make more informed and intelligent decisions, adjusting its behavior to achieve its goals.
The formula Agent = Architecture + Agent Program serves as a concise representation of how AI agents operate. Designing effective AI systems requires a deep understanding of this structure, as it enables the creation of agents that can learn, adapt, and interact with their environment in meaningful ways.
