Intelligent agent


In artificial intelligence, 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. 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, expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods.
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.
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. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired behavior. Similarly, an evolutionary algorithm's behavior is guided by a fitness function.
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".

Intelligent agents as the foundation of AI

The concept of intelligent agents provides a foundational lens through which to define and understand artificial intelligence. For instance, the influential textbook Artificial Intelligence: A Modern Approach describes:
  • Agent: Anything that perceives its environment and acts upon it. E.g., a robot with cameras and wheels, or a software program that reads data and makes recommendations.
  • Rational Agent: An agent that strives to achieve the *best possible outcome* based on its knowledge and past experiences. "Best" is defined by a performance measure – a way of evaluating how well the agent is doing.
  • Artificial Intelligence : The study and creation of these rational agents.
Other researchers and definitions build upon this foundation. Padgham & Winikoff emphasize that intelligent agents should react to changes in their environment in a timely way, proactively pursue goals, and be flexible and robust. Some also suggest that ideal agents should be "rational" in the economic sense and capable of complex reasoning, like having beliefs, desires, and intentions. Kaplan and Haenlein offer a similar definition, focusing on a system's ability to understand external data, learn from that data, and use what is learned to achieve goals through flexible adaptation.
Defining AI in terms of intelligent agents offers several key advantages:
  • Avoids Philosophical Debates: It sidesteps arguments about whether AI is "truly" intelligent or conscious, like those raised by the Turing test or Searle's Chinese Room. It focuses on behavior and goal achievement, not on replicating human thought.
  • Objective Testing: It provides a clear, scientific way to evaluate AI systems. Researchers can compare different approaches by measuring how well they maximize a specific "goal function". This allows for direct comparison and combination of techniques.
  • Interdisciplinary Communication: It creates a common language for AI researchers to collaborate with other fields like mathematical optimization and economics, which also use concepts like "goals" and "rational agents."

    Objective function

An objective function specifies the goals of an intelligent agent. An agent is deemed more intelligent if it consistently selects actions that yield outcomes better aligned with its objective function. In effect, the objective function serves as a measure of success.
The objective function may be:
  • Simple: For example, in a game of Go, the objective function might assign a value of 1 for a win and 0 for a loss.
  • Complex: It might require the agent to evaluate and learn from past actions, adapting its behavior based on patterns that have proven effective.
The objective function encapsulates all of the goals the agent is designed to achieve. For rational agents, it also incorporates the trade-offs between potentially conflicting goals. For instance, a self-driving car's objective function might balance factors such as safety, speed, and passenger comfort.
Different terms are used to describe this concept, depending on the context. These include:
  • Utility function: Often used in economics and decision theory, representing the desirability of a state.
  • Objective function: A general term used in optimization.
  • Loss function: Typically used in machine learning, where the goal is to minimize the loss.
  • Reward Function: Used in reinforcement learning.
  • Fitness Function: Used in evolutionary systems.
Goals, and therefore the objective function, can be:
  • Explicitly defined: Programmed directly into the agent.
  • Induced: Learned or evolved over time.
  • * In reinforcement learning, a "reward function" provides feedback, encouraging desired behaviors and discouraging undesirable ones. The agent learns to maximize its cumulative reward.
  • * In evolutionary systems, a "fitness function" determines which agents are more likely to reproduce. This is analogous to natural selection, where organisms evolve to maximize their chances of survival and reproduction.
Some AI systems, such as nearest-neighbor, reason by analogy rather than being explicitly goal-driven. However, even these systems can have goals implicitly defined within their training data. Such systems can still be benchmarked by framing the non-goal system as one whose "goal" is to accomplish its narrow classification task.
Systems not traditionally considered agents, like knowledge-representation systems, are sometimes included in the paradigm by framing them as agents with a goal of, for example, answering questions accurately. Here, the concept of an "action" is extended to encompass the "act" of providing an answer. As a further extension, mimicry-driven systems can be framed as agents optimizing a "goal function" based on how closely the IA mimics the desired behavior. In generative adversarial networks of the 2010s, an "encoder"/"generator" component attempts to mimic and improvise human text composition. The generator tries to maximize a function representing how well it can fool an antagonistic "predictor"/"discriminator" component.
While symbolic AI systems often use an explicit goal function, the paradigm also applies to neural networks and evolutionary computing. Reinforcement learning can generate intelligent agents that appear to act in ways intended to maximize a "reward function". Sometimes, instead of setting the reward function directly equal to the desired benchmark evaluation function, machine learning programmers use reward shaping to initially give the machine rewards for incremental progress. Yann LeCun stated in 2018, "Most of the learning algorithms that people have come up with essentially consist of minimizing some objective function." AlphaZero chess had a simple objective function: +1 point for each win, and -1 point for each loss. A self-driving car's objective function would be more complex. Evolutionary computing can evolve intelligent agents that appear to act in ways intended to maximize a "fitness function" influencing how many descendants each agent is allowed to leave.
The mathematical formalism of AIXI was proposed as a maximally intelligent agent in this paradigm. However, AIXI is uncomputable. In the real world, an IA is constrained by finite time and hardware resources, and scientists compete to produce algorithms that achieve progressively higher scores on benchmark tests with existing hardware.

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.
The agent function, often denoted as f, maps the agent's entire history of percepts to an action.
Mathematically, this can be represented as
where:
  • represents the set of all possible percept sequences. The asterisk indicates a sequence of zero or more percepts.
  • represents the set of all possible actions the agent can take.
  • is the agent function that maps a percept sequence to an action.
It's crucial to distinguish between the agent function and the agent program.
  • 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: