Types of Agents in AI (Artificial Intelligence)

Agents use algorithms and frequently use machine learning techniques to improve their performance over time, making them useful tools. This article explores what agents in AI actually are and their different types, along with examples and use cases. 

What are Agents in AI?

Agents use artificial intelligence (AI) to perceive their context , and based on that they make choices, and eventually carry out actions to fulfill certain objectives. The core idea in artificial intelligence is that agents are used in a variety of AI systems, such as autonomous robots, chatbots, and game-playing AI.

Different Types of Agents in AI

Artificial intelligence agents are classified into five major categories. These are:

1. Simple Reflex Agent
2. Model-Based Reflex Agent
3. Goal-Based Agent 
4. Utility-Based Agent
5. Learning Agent

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1. Simple Reflex Agent

• The simplest kinds of agents are simple, reactive ones. All of their decisions are purely based on the sensory information they are receiving.
• The Simple Reflex Agent makes decisions and takes actions without considering  perceptual history.
• These agents  work under the condition-action rule, which allows them to translate the current state into an action. For example, a light only works when the room is dark.
• Issues with the straightforward reflex agent design strategy:
  •  They are not very smart.
  •  They are unaware of the non-perceptual elements of the current situation.
  •  Most of the time, they are too enormous to produce and store.
  •  Not able to adjust to environmental changes
• Although they are unable to learn from past mistakes, they can still enhance their performance by refining their pre-set rules and behaviors to react more effectively to certain circumstances.

2. Model-Based Reflex Agent

• Model-based agents have the ability to keep a model inside their surroundings. 
• They can use this to design various actions and their potential results before making judgments. These agents can improve their models over time, which will improve their ability to adapt and make decisions.
• Two most important components that makes up a model-based agent:
  • Model: A model-based agent is one that has knowledge of “how things happen in the world, which is why it is given that name.
  • Internal State: This portrays that the present condition is based on perceptual history.
• These agents possess a model, “which is knowledge of the world,” and behave in accordance with the model.

3. Goal-Based Agent

• Goal-based agents make decisions in accordance with predetermined goals.
• By developing more effective methods for achieving these objectives, they can enhance their performance. They can develop and modify their strategies over time with the use of machine learning techniques like reinforcement learning.
• Knowing the environment’s current state is not always enough for an agent to make decisions.
• The agent must be aware of its goal that outlines suitable circumstances.
• By possessing the “goal” knowledge, goal-based agents enhance the model-based agent’s capabilities.
• They choose a plan of action to achieve their goal.
• Before confirming whether the objective is achieved or not, these agents must consider numerous potential actions. This collection of scenario assessments is known as ‘searching and preparing,’ which enhances an agent’s proactivity.

4. Utility-Based Agent

• Utility-based agents consider not only what they want but also think about the good and bad things that can happen with other choices.
• They give outcomes utility values, which enable them to choose options that will maximize expected utility.
• Utility-based agents are like goal-based agents, but they have an extra feature. They use a special tool to show how well they’re doing at a particular point in their journey. This tool helps them stand out and make smarter decisions.
• To assess how well each action achieves its goals, the utility function translates every state into a numerical value.
• When an agent has to choose between different good options to do something in the right way, utility-based agents come into the picture.

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5. Learning Agent

• The greatest probability for improvement over time is with learning agents. 
• Through multiple learning algorithms, they are able to modify and improve their judgement  process. Learning agents constantly  collect data and alter their techniques to get better results as they gain experience, whether through supervised learning, unsupervised learning, or reinforcement learning.
• A learning agent primarily comprises of these four parts:
  •  Learning component: It is for producing advancements by taking signals from the environment
  •  Critic: The learning component uses critical feedback to determine how well the agent is performing in relation to previously established criteria.
  •  Element of Performance: It is in charge of choosing an external action.
  •  Problem Generator: This aspect makes recommendations for actions that will result in new and useful experiences.

Examples of Agents

Agents are used in wide applications, and some examples of agents are given below: 

• Virtual Personal Assistants: Virtual assistants are one of the most used implementations of these agents. They respond to voice commands and questions, such as Siri, Alexa, and Google Assistant, 
• Autonomous Robots: Agent-like autonomous robots, like self-driving cars and home appliances like vacuum cleaners, sense their surroundings and decide how to perform a specific task
• Chatbots: Chatbots are artificial intelligence (AI) programs that can communicate with people via text or voice. They work in information retrieval, e-commerce, and customer service.
• Characters in the game: AI agents control non-player characters (NPCs) in video games. Within the game environment, they act on behaviors, make choices, and communicate with other players.
• Recommendation Systems: Recommendation agents look at user preferences to make recommendations for goods, movies, music, or other content that are more towards the user perspective. Examples include purchase recommendations from Amazon and Netflix.
• Search Engines: Web Crawlers and ranking algorithms for search engines work as agents to index web pages, retrieve search results, and give users relevant data.
• Autonomous Drones: Drones with AI agents can perform jobs like surveillance, deliveries, and aerial photography by navigating their environment and avoiding objects.

Uses of Agents

AI agents are like smart helpers that can be used in many different situations. They’re useful because they can see, think, and do things to get stuff done. Here are some examples of where we can use these agents.

• Healthcare: Patients can be monitored, specific therapy treatments can be given, and the best use of healthcare resources may be made because of agents.
• Robotics: Some of the major tasks in the industrial, transportation, and other industries can be automated and controlled by agents.
• Finance: In the financial sector, agents can be used for automated trading, fraud detection, and risk management.
• Games: Intelligent opponents can be engineered using agents in games and simulations, giving players a more competitive and realistic experience.
• Smart Houses and Buildings: Using agents we can manage heating, lighting, and other HVAC systems and to increase energy efficiency 
• Transportation Systems: AI Agents can be helpful for managing traffic flow, planning routes for AI-powered Automobiles, and increasing logistics supply by route optimization and supply chain management can all be done with the help of agents.
• Natural Language Processing (NLP): NLP agents are useful for sentiment analysis, language generation, and machine translation as they can understand, generate, and translate human language

Conclusion

In the world of AI, agents are like smart helpers who can see, think, and take action to get things done. They are used in many ways, from virtual assistants on our phones to robots in data warehouses. These agents make our lives easier, help us find information, and even drive cars. With their ability to learn and adapt, they are becoming increasingly valuable in solving complex problems and making our technology more helpful and efficient. For more exiciting AI stuff, check out this amazing Artificial Intelligence Course