AI Agents: How They Work
Artificial intelligence agents, often referred to as intelligent agents (IAs), are systems that perceive their environment, take actions autonomously in order to achieve goals, and can improve their performance with learning or acquiring knowledge. They may be as simple as a thermostat or as complex as a human being, essentially any system that meets this definition can be classified as an intelligent agent1.
Intelligent agents, as defined in leading AI textbooks, encapsulate their goals within an "objective function". This allows for the creation and execution of a plan that will maximize the expected value of the objective function upon completion. In the context of AI, for example, a reinforcement learning agent has a "reward function" that shapes its desired behavior, and an evolutionary algorithm's behavior is shaped by a "fitness function"1.
There are several classifications of intelligent agents based on their perceived intelligence and capability:
Simple reflex agents: These agents act only on the basis of the current percept, ignoring the rest of the percept history. They function based on the condition-action rule: "if condition, then action"1.
Model-based agents: These agents can handle partially observable environments. They maintain an internal model of the world that depends on the percept history, reflecting at least some of the unobserved aspects of the current state. They use this internal model to determine the percept history and impact of action on the environment1.
Goal-based agents: These agents use "goal" information, which describes situations that are desirable. This allows them to choose among multiple possibilities, selecting the one which reaches a goal state1.
Utility-based agents: These agents define a measure of how desirable a particular state is, using a utility function. They choose actions that maximize the expected utility of the action outcomes. They need to model and keep track of their environment, tasks that involve a great deal of research on perception, representation, reasoning, and learning1.
Learning agents: These agents can operate in unknown environments and become more competent than their initial knowledge alone might allow. They have a "learning element", which is responsible for making improvements, and a "performance element", which is responsible for selecting external actions. They also have a "problem generator", which suggests actions that will lead to new and informative experiences1.
Weiss (2013) also suggests another classification of intelligent agents: Logic-based agents, Reactive agents, Belief-desire-intention agents, and Layered architectures1.
Intelligent agents today are often organized in a hierarchical structure containing many “sub-agents” that process and perform lower-level functions. This hierarchy of controller layers is often necessary to balance the immediate reaction desired for low-level tasks and the slow reasoning about complex, high-level goals1.
This explanation provides an overview of how AI agents work but the actual implementation details can vary greatly depending on the specific type of agent and the context in which it is used. For an in-depth understanding of the specific algorithms and techniques used, further study in AI textbooks or technical resources would be beneficial.