An AI agent is a program that performs actions in the place of its user, or other agents. It may sense the environment around itself, and then act accordingly. It may also interact with other agents, either directly or through a third party. Agents can be used to automate processes, or even to create new ones.
What are agents in Artificial Intelligence?
An AI system is a collection of components that interact with each other to solve problems. A robot may be considered an example of an AI system.
An AI system consists of three main parts: perception, action, and learning. Perception refers to what the AI system does when interacting with the environment. Action refers to what the AI does to change the environment. Learning refers to how the AI learns to accomplish its goals.
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Function of AI agent
AI Agents perceive their environment, act to change it, reason about what happened, interpret their own actions, and plan ahead. The term “intelligent virtual assistant” or “artificial intelligence” may also be used to describe intelligent agents.
An intelligent agent continuously performs the following functions. Perception of dynamic conditions in the physical environment. Action to affect conditions in the physical environment, including manipulation of objects, communication, and navigation. Reasoning to interpret perceptions. Solve problems. Draw conclusions. Determine actions.
Intelligent agents are systems that can interact with the world around them. They rely on sensors to gather data about their surroundings, actuators to adjust their behavior in response to that data, and effectors to take actions based on that behavior. These three components are the foundation of intelligent agent technology, and understanding them can help us better understand how they work.
Sensors: These are devices that detect any change in the environment. They tell the agent what is happening around them. For example, if you were walking down the street, your eyes would pick up the objects around you. Your ears would hear the sounds. You could feel the ground under your feet. All these senses help you understand what is going on around you. When you combine all of these different sources of information, you get a full picture of what is happening. This is called perception.
Actuators: These are components that convert energy into motion. They are like the muscles in your body. They move something. Some examples are wheels, pistons, and levers.
Effectors: These are the parts of an intelligent agent that interact with its environment. They are like hands and feet. They allow the agent to manipulate objects. An intelligent agent may have many effectors. For example, a robot arm might have a gripper to grab objects. A robot leg might have a foot to step on objects. A robot eye might have a camera to take pictures.
Inputs (Perceptions): The environment is affected by inputs. These inputs include the senses like eyes, ears, nose, touch etc. Sensors detect signals coming from the environment and provide them to the agent. The agent then processes the inputs and takes an action.
Percept History: If the agent performs well, it will receive positive feedback.
Past Actions: If the agent performs poorly, it will receive negative feedback. This helps the agent to learn from its mistakes.
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Types of Agents
There are 5 different types of agents: simple reflex, model-based reflex, goal-based, utility-based, and learning agent. These categories are based on the type of information an agent needs to act upon.
1. Simple Reflex agent
Simple reflexes are the simplest kind of agent. They tend to focus solely on what they see right now. Their behavior is determined by a single condition-action rule. This rule determines whether the agent will move forward, turn left or right, or stop. Simple reflex agents ignore the past because they cannot remember anything. They do not consider any information about the future. These agents are unable to solve problems that require them to reason about the past and plan ahead.
Simple reflex agents have limitations. They are incapable of understanding the world around them. They also tend to be very large, making them hard to create and update. As a result, simple reflex agents often fail when faced with changing situations.
2. Model-based reflex agents
A model-based agent keeps track of its internal states using a model. These models are often called representations because they represent an idea of what the environment looks like.
For example, a car might have a representation of its surroundings that describes the road, the buildings, and other cars on the road. When the agent senses something, it updates its internal representation of the world. To update the representation, it needs information about the world. The information comes from sensory inputs and actions taken by the agent. Sensory inputs tell the agent what the world looks like at any given moment. Actions tell the agent what it should do next. If you think of your body as a model-based agent, then your brain is updating its internal representation of the external world based on sensory inputs and motor outputs.
How the world changes depends on what you do. If you plant seeds, then the plants will grow. If you don’t, they won’t. And if you go out and pick them, they will grow. What happens when you don’t plant any seeds? That’s right — nothing. You can’t change the past, but you can change your future.
3. Goal-based agents
An AI agent is an intelligent entity that learns and acts autonomously. An AI agent takes actions based on what it knows about the environment. These actions will help it achieve its goals. To do this, it needs to reason about its surroundings and act accordingly. A typical AI agent will need to plan ahead and consider many possible futures. It might also need to search for information relevant to its current situation.
4. Utility-based agents
Utility-based agents are designed to solve problems when there are multiple possible solutions.
An example of this could be choosing the right route to get to your destination. If you are driving, you might consider factors like traffic jams, road conditions, weather, etc. It might also be important to consider your mood, the time of day, or whether you need to go to work or if you just want to relax. These are all examples of preferences that an agent might have.
To choose the best course of action, we must determine what the agent wants, and then figure out which option will give them what they want. For instance, suppose you want to drive to work, but you also want to enjoy yourself. Then you might prefer to take the scenic route, because it will allow you to stop at interesting places along the way.
However, if you are in a hurry, you might prefer taking the fastest route. In this case, you might want to calculate the expected utility of each choice and select the option with the highest value.
5. Learning Agents
A learning agent is an intelligent system that learns from experience. It starts out with very simple rules and gradually becomes more complex. It then improves itself by learning from mistakes. A critic provides feedback about how well the agent is performing. It tells the agent what to change to get better at achieving goals.
The Performance Element selects an external action. The Problem Generator suggests actions that will lead to novel and informative experiences.
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Applications of intelligent agents
Artificial Intelligence has been used in many different areas of life. Artificial Intelligence has been used in robotics, natural language processing, video games, social media, and other areas.
Repetitive office activities
Many companies have automated certain administrative functions to reduce operating costs. These include customer service and sales. Intelligent agents can help employees get more done during the day. Examples of intelligent agents include chatbots and virtual assistants like Siri.
Medical diagnosis
In the medical context, an intelligent agent is a software application that interacts with its environment using sensors and actuators to achieve goals. Intelligent agents can be used to provide assistance to doctors, nurses, and other healthcare professionals. These systems are often referred to as “digital assistants” or “medical chatbots.”
Autonomous driving
Intelligent agents can be used to control autonomous vehicles. They can make decisions about where to go and how to reach their destinations safely. This includes things such as avoiding obstacles and deciding when to turn.
Vacuum cleaning
An intelligent vacuum cleaner can learn how to clean your home efficiently. Using sensors, it can detect objects and determine where to start cleaning. It can also use cameras to see into hard-to-reach corners.
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Conclusion
AI has a wide range of applications. We discussed several ways to classify AI. One of those was based on the performance element. We talked about an intelligent agent’s ability to perform tasks. Another classification scheme is based on the problem generator. Finally, we mentioned that intelligent agents can be classified according to the type of domain they’re applied in based on which elements are included in the category.
