Different Types of AI Agents – Exploring Intelligent Agents in Artificial Intelligence

In this tech-savvy world, one phenomenon that has taken centre stage and is revamping societies, industries, and how we live is- Artificial Intelligence (AI). The 21st century is encountering an unprecedented upsurge in the adoption and development of AI, making it a trend that is vogue and altering how we precise and interact with the world.

“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.

Nevertheless, it may seem intricate, and honestly, it is; we can get a better familiarity as well as solace with AI by delving into its elements separately. When we learn how the pieces fit together, we can better comprehend and implement them. But before we do that, let’s quickly take a dig at AI’s evolution and milestones.

Artificial Intelligence – Evolution and Milestones

The roots of AI can be traced back to ancient history, with mythological tales of golems and automatons. However, it was only in the midst of the 20th century that AI started to emerge as the formal field of study. The advent of digital computers and the trailblazing work of visionaries such as Alan Turing laid the groundwork for the development of Artificial Intelligence.

The field touched massive milestones in subsequent decades, like the creation of professional systems, rule-based programming, and, of course, the birth of machine learning. The 21st century, however, marked a vital moment in the AI narrative, as innovations in the power of computing, big data, and algorithmic sophistication confluence to propel AI into the latest sphere of capability.

What is an AI Agent?

An AI agent, aka Artificial Intelligence agent, refers to a programme or framework typically designed to perform tasks and come up with decisions in a semi-autonomous/autonomous manner. Different types of AI agents are paramount concepts in Artificial Intelligence. These AI agents can be as easy as a script, automating a specific task or as intricate as a sophisticated system, employing machine learning, along with other AI techniques adapting and enhancing over time.

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

Simple Reflex Agent



Simple reflex agents make decisions typically based on current percepts, disregarding the percept history. They operate in fully observable environments and go with condition-action rules, mapping the current state to action. However, they have restrictions, such as limited intelligence, 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 percept history. Unlike simple reflex agents, they contemplate a broader context, adapt to environment changes, as well as generally 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, which is known as utility measurement. These agents not only act based on goals but also contemplate the most effective way to nail them. Utility-based agents have exceptional value in scenarios with multitudes of alternatives, utilising a utility function to examine how well each and every action resonates with the goals. Their typical aim is to make rational decisions, maximising expected utility.




Learning Agents



Learning agents in Artificial Intelligence come with the capacity to learn from past experiences or via their inherent learning capabilities. Comprising four conceptual elements, learning agents can adapt and enhance their performance gradually. To be precise, learning elements are responsible for making enhancements by learning and mastering from the environment.

Secondly, the critic! It provides feedback based on agents’ performance against a predefined standard. Thirdly, the performance element! It helps select external actions based on learned knowledge. Lastly, the problem generator! It suggests actions leading to new and informative experiences.

 

Therefore, learning agents constantly learn, analyse performance, and seek novel ways to improve their overall 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 take decisions.
• The perception of dynamic environmental circumstances.
• Utilising logic to interpret perceptions.
• To make an effort to alter environmental conditions.

Uses of Agents in Artificial Intelligence

Artificial Intelligence agents have been used in multitudes of real-life applications; some of them 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.
• The environment in these agents could contain people, other cars, roads, or road signs. Actions are commenced utilising numerous devices. For instance, the application of brakes in a car is imperative for bringing it to a halt. Autonomous vehicles’ performance is significantly improved when intelligent agents provide assistance.

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

The objective of AI is to curate a programme for an agent that effectively carries out its functions. The structure of an intelligent agent contains an amalgamation of architecture and agent programme, which is actually represented as Agent = Architecture + Agent programme.

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

• Architecture: This represents the machinery on which an AI agent operates and is exceptionally beneficial.
• Agent Function: The agent function is employed to map a percept to an action, expressed as f:P* → A.
• Agent Programme: It serves as the implementation of 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 precisely 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 AI agents exemplify the profound influence of artificial intelligence on shaping the future of numerous industries. If you are ready to improvise the big data, algorithmic sophistication, and computing powers, then 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.