Different Types of AI Agents: Uses and Functions

Artificial Intelligence (AI) is a leading phenomenon that dominates modern life because it transforms societies, businesses, and human lifestyle patterns. Artificial intelligence advances at an unprecedented rate throughout the 21st century, making it a fashionable trend that modifies our exact world interaction methods.

“Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity,” said Fei-Fei Li, a renowned computer scientist.

The process appears confusing, but it is complex on a factual level. Understanding AI components one by one will help us better grasp this technology and achieve a sense of comprehension. Learning piece interactions will help us understand how to properly use and apply the elements. Before moving forward, we will review AI’s development and key achievements.

Artificial Intelligence – Evolution and Milestones

Stories of mechanical beings are the basis of AI’s beginnings. In the 20th century, however, Alan Turing, amongst other pioneers, made it an official field of study when the development of digital computers made it possible. So, over time, AI has developed and refined itself by developing expert systems and machine learning. By comparison, when the 21st century started, AI was a very limited technology. Still, in the last decade, the power of computers, big data, and smarter algorithms have improved so much that further developments of this technology have become very advanced.

What is an AI Agent?

An AI (Artificial Intelligence) Agent is a program or framework typically set up to perform such tasks and make decisions semi-autonomously or autonomously. Artificial Intelligence has two important parameters: different types of AI agents. AI agents can be as simple as a script that auto-executes a process for you or as complex as a network of machine learning and other AI techniques that improve over time.

The basic functions of AI agents are based on their data perception, processing, and response. This setup assures that the agent operates smoothly in numerous applications.

• Sensors: These are real-time data collected from the environment, such as visual, auditory, or textual data, and fed to the sensor. Agents of this type allow us to see and understand what is happening around us and make decisions based on the collected information.
• Effectors: The AI agent decides these actions and executes them. In robotics, effectors are motors and mechanical arms. Depending on the software-based agent, they can be automated, like sending a response or automatically making calculations.
• Decision-Making Framework: This component processes sensor data, processes algorithms and computes the most applicable action. This helps the AI agent get updated, enhance its response, and improve with time.

What Are the Types of Agents in AI?

Artificial Agents are normally categorised into five different kinds, depending on their perceived intelligence and capabilities. These different types of AI agents in AI exhibit the ability to improve their performance and make uniform decisions with time. Below are the agents in Artificial Intelligence. Read on to know.

●   Simple Reflex Agent

Simple reflex agents typically make decisions based on current percepts, disregarding the percept history. They operate in fully observable environments and follow condition-action rules, mapping the current state to action. However, they have restrictions, such as limited intelligence, a lack of knowledge about non-perceptual elements, and a tendency to be non-adaptive to environmental changes.

●   Model-Based Reflex Agent

Model-based reflex agents operate in moderately observable environments and track percept history. They have two key elements: a model representing knowledge of how events unfold globally and an internal state mirroring the present state depending on perceived history. Unlike simple reflex agents, model-based reflex agents contemplate a broader context, adapt to environmental changes, and exhibit enhanced performance.

●   Goal-Based Agent

Goal-based agents go beyond depending ultimately on the present state and include knowledge of their goals, briefing desirable situations. These agents choose actions to fulfil their goals, radically contemplating an extended sequence of possible actions via searching and planning. Goal-based agents are proactive and execute actions in the environment once a plan is decided.

●  Utility-Based Agents

Utility-based agents are the same as goal-based agents but institute an extra component known as utility measurement. These agents act based on goals and contemplate the most effective way to achieve them. Utility-based agents have exceptional value in scenarios with multitudes of alternatives. They use a utility function to examine how well each action resonates with the goals. Their typical aim is to make rational decisions, maximising expected utility.

●  Learning Agents

Learning agents in Artificial Intelligence can learn from past experiences or through their inherent learning capabilities. Comprising four conceptual elements, learning agents can gradually adapt and enhance their performance. To be precise, learning elements enhance performance by learning and mastering the environment.

The critic provides feedback on agents’ performance against a predefined standard. The performance element helps select external actions based on learned knowledge. Lastly, the problem generator suggests actions that lead to new and informative experiences.

Therefore, learning agents constantly learn, analyse performance, and seek novel ways to improve their effectiveness.

The Functions of AI Agents

Underneath are the functions of types of agents in Artificial Intelligence. Read on to know.

• To resolve intricate issues with the help of intelligent machines.
• To decide what to do in a certain situation.
• To make conclusions and make decisions.
• The perception of dynamic environmental circumstances.
• Utilising logic to interpret perceptions.
• To make an effort to alter environmental conditions.

How AI Agents Work

AI agents perceive, make decisions, and execute them continuously: gathering, processing, deciding what to do, doing it, and modifying.

• Perception and Input Processing: An AI’s environmental information is obtained by combining sensors and user data. The agent then gathers information, but it is structured and transformed to be assimilable to the agent.
• Decision-Making and Planning: To be able to do this, AI agents need to follow predefined goals to guide the basis of AI agents in assessing input through machine learning models to help the agent pick the top answer. AI agents have to decide what to do and evaluate important considerations, needs for fast response, and user purposes before execution and doing that has to be done.
• Action Execution: The AI agent makes decisions and executes predefined responses, such as sending messages, updating the system, automating processes, etc.
• Learning and Adaptation: The advanced AI agents process past feedback, and their learning abilities increase as more advanced AI agents learn from the past feedback process. They are reinforcement learning-based systems to learn the response procedures to make more effective and precise decisions.

Uses of Agents in Artificial Intelligence

Artificial Intelligence agents have been used in many real-life applications, some of which are mentioned below. Have a look.

Medical Evaluation

• The surroundings are regarded as the patient.
• The sensor collecting information on the patient’s complaints is a computer keyboard.
• The intelligent agent utilises this data to determine the best plan of action.
• Actuators in healthcare incorporate tests and therapies.

Automatic Vehicles

• Numerous sensors are used in automatic vehicles to collect data from the surroundings.
• These contain radar, GPS, and cameras.
• These agents’ environments could include people, other cars, roads, or road signs. They commence actions utilising numerous devices. For instance, applying brakes to a car is imperative to bring it to a halt. When intelligent agents assist, autonomous vehicles’ performance is significantly improved.

Office Tasks Automation

• Agents in AI offer a solution to mundane tasks within the workplace.
• Functional domains such as customer service and sales have witnessed automation.
• Some businesses have streamlined administrative processes to lessen operating costs.
• The implementation of intelligent agents has raised overall office efficiency.

The Framework of an Artificial Intelligence Agent

AI aims to curate an agent programme that effectively carries out its functions. An intelligent agent’s structure contains an amalgamation of architecture and agent programme, represented as Agent = Architecture + Agent programme.

The key terms associated with the structure of an AI agent are:

• Architecture: This represents an AI agent’s machinery and is exceptionally beneficial.
• Agent Function: The agent function maps a percept to an action, expressed as f:P* → A.
• Agent Programme: It implements the agent function, with the agent programme executing on the physical architecture to provide the function.

To Wrap It All Up

Agents in Artificial Intelligence play a great role in altering diverse domains, from autonomous vehicles to office automation. Their ability to autonomously perform tasks, make decisions, and adapt via learning contributes to increased efficiency and streamlined processes. The synergy of architecture and agent programmes defines the structure where intelligent agents showcase their expertise. As technology innovates, the impact of AI agents continues to grow, promising further advancements in fields ranging from robotics to customer service.

The evolution and integration of different types of AI agents exemplify the profound influence of artificial intelligence on shaping the future of numerous industries. Suppose you are ready to improvise the big data, algorithmic sophistication, and computing powers. In that case, pursuing an Integrated Program in Data Science, Artificial Intelligence & Machine Learning at Hero Vired will be exceptionally helpful. Offered in collaboration with MIT Open Learning, this programme is designed to give you the right skills to analyse data and develop intricate models to solve business problems.