5 types of agents in artificial intelligence

  • There are five types of AI agents, each with varying levels of complexity and intelligence—simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents.
  • AI agents interact with their environment using sensors to perceive inputs and actuators to perform actions, operating in a cycle of perception, thought, and action to achieve specific goals.
  • Examples of intelligent agents include driverless cars, which use sensors and actuators to navigate, and virtual assistants like Siri, which respond to user queries and perform tasks based on learned behaviour.

Artificial Intelligence is a fascinating field of information technology that permeates many aspects of modern life. Though it may seem complex, we can gain a greater understanding and comfort with AI by exploring its components individually. By learning how these pieces fit together, we can better comprehend and implement AI technologies. This blog introduces the concept of intelligent agents in Artificial Intelligence and delves into the five types of agents in AI.

What is an agent in AI?

In the context of AI, an “agent” is an independent program or entity that interacts with its environment by perceiving its surroundings through sensors and acting through actuators or effectors. Agents operate in a cycle of perception, thought, and action using their actuators. Examples of agents include:

Software agents

These agents use file contents, keystrokes, and received network packages as sensory input and then act on those inputs, displaying the output on a screen.

Human agents

Humans are natural agents, with eyes, ears, and other organs serving as sensors, while hands, legs, mouths, and other body parts function as actuators.

Robotic agents

Robotic agents utilise cameras and infrared range finders as sensors and various servos and motors act as actuators.

Intelligent agents in AI are autonomous entities that interact with their environment using sensors and actuators to achieve specific goals. These agents can also learn from their environment to enhance their performance over time. Examples of intelligent agents in AI include driverless cars and virtual assistants like Siri.

Also read: 5 types of AI hardware driving tomorrow’s intelligent machines

5 Types of Agents in Artificial Intelligence

There are five different types of intelligent agents used in AI, defined by their range of capabilities and intelligence levels:

Simple reflex agents

These agents operate solely based on current perception, without considering any history of perception. They are successful only in fully perceivable environments due to their limited intelligence and capabilities. Simple reflex agents are not adaptive; if something is not perceived in the current state, it will not influence the action. Their responses are essentially triggered by user-initiated events, referring to a list of pre-set rules and pre-programmed outcomes.

Model-based reflex agents

Model-based reflex agents have a significant advantage over simple reflex agents—they consider historical data and can function in partially observable environments. They utilise a model to represent the current state of the world and an internal state to reflect the current condition based on historical perception. While they choose actions similarly to simple reflex agents, their understanding of the environment is more comprehensive.

Also read: Delivering solutions with cognitive computing in AI

Goal-based agents

As the name suggests, these agents use goals to describe desirable outcomes and can choose among various possibilities to achieve them. Building on model-based agents, goal-based agents select the best action from available options to reach their goals, using artificial intelligence to make decisions. This process, known as ‘searching and planning,’ involves evaluating different actions to determine the most effective one.

Utility-based agents

Similar to goal-based agents, utility-based agents provide an additional utility measurement that rates potential scenarios based on desired results. They then choose the action that maximises the outcome. This capability allows them to trade off different factors before making a decision. For instance, a clothing store’s goal may be to maximise profit, but a utility-based agent also considers customer satisfaction. By setting utility as a real number (e.g., a 1-10 scale of customer satisfaction), the agent can make decisions in real-world scenarios based on utility.

Learning agents

Learning agents have an additional learning element, enabling them to gradually improve and become more knowledgeable about their environment over time. They learn from feedback on their actions and adapt accordingly. This process requires four components: the learning element (which learns from experience), the critic (which provides feedback), the performance element (which decides on external actions), and the problem generator (which keeps a history and makes new suggestions).

The rise of artificial intelligence is boundless. With forecasts predicting a 33.2% annual growth rate for the sector between 2020 and 2027 and research showing that 80% of retail executives expect their companies to adopt AI-powered intelligent automation by 2027, organisations that do not explore AI strategies risk being left behind. Understanding the role of intelligent agents is a crucial first step in appreciating AI’s potential.


Crystal Feng

Crystal Feng is an intern news reporter at Blue Tech Wave dedicated in tech trends. She is studying Chinese-English translation at Beijing International Studies University. Send tips to c.feng@btw.media.

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