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, AI agents (also known as 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".
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:
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