An intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through by acquiring knowledge. 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.
In artificial intelligence, a specialized subset of intelligent agents, AI agents (also known as agentic AI) expand this concept by proactively pursuing goals, making decisions, and taking actions over extended periods.
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".
Agent-based definition of artificial intelligence Russell and Norvig describe artificial intelligence as the study of agents that receive percepts from an environment and perform actions. In this framework, an agent is anything that perceives its environment through sensors and acts upon that environment through actuators. A rational agent selects the action expected to maximize its performance measure, given its percept sequence, prior knowledge, and available actions. Rationality in this sense does not require an agent to be omniscient or always successful; it concerns the expected outcome of an action on the basis of the information available to the agent.
In agent-oriented computing, agents are also commonly characterized by properties such as autonomy, responsiveness to changes in their environment, and goal-directed or proactive behavior. One approach to modeling practical reasoning is the belief–desire–intention (BDI) architecture, which represents an agent in terms of its information about the world, its objectives, and the courses of action to which it has committed. Objective function
An objective function (or goal 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 (error). 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.Lindenbaum, M., Markovitch, S., & Rusakov, D. (2004). Selective sampling for nearest neighbor classifiers. Machine learning, 54(2), 125–152.
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 agent mimics the desired behavior. In generative adversarial networks (GANs) 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.Andrew Y. Ng, Daishi Harada, and Stuart Russell. "Policy invariance under reward transformations: Theory and application to reward shaping." In ICML, vol. 99, pp. 278-287. 1999. Yann LeCun stated in 2018, "Most of the learning algorithms that people have come up with essentially consist of minimizing some objective function."Martin Ford. Architects of Intelligence: The truth about AI from the people building it. Packt Publishing Ltd, 2018. 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 intelligent agent 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 (e.g., accelerate, brake, turn).
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 agent's entire perceptual history). 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 is 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:
Calculating the utility (desirability) of different actions. Using logical rules and deduction. Employing fuzzy logic. Other methods.
Classes of intelligent agents
Russell and Norvig's classification group agents into five classes based on their degree of perceived intelligence and capability:
Simple reflex agents
Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on the condition-action rule: "if condition, then action".
This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.
Infinite loops are often unavoidable for simple reflex agents operating in partially observable environments. If the agent can randomize its actions, it may be possible to escape from infinite loops.
A home thermostat, which turns on or off when the temperature drops below a certain point, is an example of a simple reflex agent.
Model-based reflex agents
A model-based agent can handle partially observable environments. Its current state is stored inside the agent, maintaining a structure that describes the part of the world which cannot be seen. This knowledge about "how the world works" is referred to as a model of the world, hence the name "model-based agent".
A model-based reflex agent should maintain some sort of internal model that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state. Percept history and impact of action on the environment can be determined by using the internal model. It then chooses an action in the same way as reflex agent.
An agent may also use models to describe and predict the behaviors of other agents in the environment.Stefano Albrecht and Peter Stone (2018). Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems. Artificial Intelligence, Vol. 258, pp. 66-95. https://doi.org/10.1016/j.artint.2018.01.002
Goal-based agents
Goal-based agents further expand on the capabilities of the model-based agents, by using "goal" information. Goal information describes situations that are desirable. This provides the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state. Search and planning are the subfields of artificial intelligence devoted to finding action sequences that achieve the agent's goals.
ChatGPT and the Roomba vacuum are examples of goal-based agents.
Utility-based agents