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an **Intelligent Agent **is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. Leading AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal-directed behavior is central to intelligence.
A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods, thereby exemplifying a novel form of digital agency.[1]
Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome.[2]
Intelligent agents operate based on an objective function, which encapsulates their goals. They are designed to create and execute plans that maximize the expected value of this function upon completion.[3] For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired behavior.[4] Similarly, an evolutionary algorithm's behavior is guided by a fitness function.[5]
Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, and the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.
Intelligent agents are often described schematically as abstract functional systems similar to computer programs. To distinguish theoretical models from real-world implementations, abstract descriptions of intelligent agents are called abstract intelligent agents. Intelligent agents are also closely related to software agents—autonomous computer programs that carry out tasks on behalf of users. They are also referred to using a term borrowed from economics: a "rational agent".
Agent Function
An intelligent agent's behavior can be described mathematically by an agent function. This function determines what the agent does based on what it has seen.
A percept refers to the agent's sensory inputs at a single point in time. For example, a self-driving car's percepts might include camera images, lidar data, GPS coordinates, and speed readings at a specific instant. The agent uses these percepts, and potentially its history of percepts, to decide on its next action (e.g., accelerate, brake, turn).
The agent function, often denoted as f, maps the agent's entire history of percepts to an action.[16]
Mathematically, this can be represented as:
f : P ∗ → A {\displaystyle f:P^{*}\rightarrow A}
Where:
P\* represents the set of all possible percept sequences (the agent's entire perceptual history). The asterisk (*) indicates a sequence of zero or more percepts.
A represents the set of all possible actions the agent can take.
f is the agent function that maps a percept sequence to an action.
It's crucial to distinguish between the agent function (an abstract mathematical concept) and the agent program (the concrete implementation of that function).
The agent function is a theoretical description.
The agent program is the actual code that runs on the agent. The agent program takes the current percept as input and produces an action as output.
The agent function can incorporate a wide range of decision-making approaches, including:[17]
Calculating the utility (desirability) of different actions.
Using logical rules and deduction.
Employing fuzzy logic.
Other methods.
A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods, thereby exemplifying a novel form of digital agency.[1]
Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome.[2]
Intelligent agents operate based on an objective function, which encapsulates their goals. They are designed to create and execute plans that maximize the expected value of this function upon completion.[3] For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired behavior.[4] Similarly, an evolutionary algorithm's behavior is guided by a fitness function.[5]
Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, and the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.
Intelligent agents are often described schematically as abstract functional systems similar to computer programs. To distinguish theoretical models from real-world implementations, abstract descriptions of intelligent agents are called abstract intelligent agents. Intelligent agents are also closely related to software agents—autonomous computer programs that carry out tasks on behalf of users. They are also referred to using a term borrowed from economics: a "rational agent".
Agent Function
An intelligent agent's behavior can be described mathematically by an agent function. This function determines what the agent does based on what it has seen.
A percept refers to the agent's sensory inputs at a single point in time. For example, a self-driving car's percepts might include camera images, lidar data, GPS coordinates, and speed readings at a specific instant. The agent uses these percepts, and potentially its history of percepts, to decide on its next action (e.g., accelerate, brake, turn).
The agent function, often denoted as f, maps the agent's entire history of percepts to an action.[16]
Mathematically, this can be represented as:
f : P ∗ → A {\displaystyle f:P^{*}\rightarrow A}
Where:
P\* represents the set of all possible percept sequences (the agent's entire perceptual history). The asterisk (*) indicates a sequence of zero or more percepts.
A represents the set of all possible actions the agent can take.
f is the agent function that maps a percept sequence to an action.
It's crucial to distinguish between the agent function (an abstract mathematical concept) and the agent program (the concrete implementation of that function).
The agent function is a theoretical description.
The agent program is the actual code that runs on the agent. The agent program takes the current percept as input and produces an action as output.
The agent function can incorporate a wide range of decision-making approaches, including:[17]
Calculating the utility (desirability) of different actions.
Using logical rules and deduction.
Employing fuzzy logic.
Other methods.