Introduction to Intelligent Agent in AI
As we know, ideas don’t have any value until they are put into action. So, to put artificial intelligence into action, we have an intelligent agent. This agent makes a decision and acts. An AI system consists of two things, first is an intelligent agent, and the second is its environment. An agent requires an environment to carry out its actions. Certainly, in an environment, there can be other agents too. As human beings, we have hands, ears, eyes, tongue to perceive things and make decisions. In the same way, the intelligent agent will have sensors to perceive the environment. Once we have taken the decision, the next thing is to act upon it. We human beings perform different actions using our expressions, hands, legs, etc. In the same way for AI agent, we have actuators which would perform actions based on a decision made by artificial intelligence.
What is the Intelligent Agent in AI?
In Artificial Intelligence, an AI agent is an acting entity that performs actions to achieve goals, which are set by decisions made using artificial intelligence. Intelligent agents are also called as intelligent because they may also learn in the process of achieving goals. In a simple agent, two main functionalities are to percept through sensors and act through actuators. An agent could be very simple as well as very complex, too, it depends on the problem statement.
Depending on the problem statement and ability to perceive the agents can be categorized into 5 categories.
- Simple Reflex agent: works on current perception
- Model-based reflex agents: represents the current state based on history.
- Goal-based agents: They are proactive agents and works on planning and searching.
- Utility-based agents: Have extra component of utility measurement over goal-based agent
- Learning agent: able to learn and adapt the new decision-making capabilities based on experience.
1. Simple Reflex agent
This type of agent works on current perception and does not consider the history of perception. And that is why these are only successful when they have an environment that can be perceived fully. Based on this perception agent creates condition action rule and then according to current perception takes action. Simple reflex agents are limited because of their limited intelligence. If something is not perceived in the current state, it will not be part of the action. The agent is not adaptive to the environment.
2. Model-based reflex agent
The advantage of a model-based agent over simple is that it considers history. Which makes it work even in an environment which is not fully observed. Model-based has the model and internal state, the model will tell about the current state of the world, and on the other hand, the Internal state will tell about the current state based on the history of perception. For each current state, an agent must be updated with How the world is evolving and how the actions performed by agents are affecting it.
3. Goal-based agents
These types of agents need a goal towards which action should be performed, so in addition to the current state of the environment, then another input it needs a goal. This agent is an extension of the model-based agents. Here the agent chooses the best action from the available option (decisions made by artificial intelligence) which will help to reach the goal. Because these agents do make a choice, it is referred to as searching and planning to make an action.
4. Utility-based agents
Utility-based agents have utility measurement as an extra component which gives them an edge over goal-based agents. For situations where it is possible to have more than one option to choose from, and then utility measurement helps an agent to select the best among them. Sometimes we have to trade-off between goals and the utility for example in a cloth store, the goal is to sell and make a profit on clothes, but the utility is customer satisfaction, so sometimes it is needed to trade-off with customer satisfaction making a profit. It makes agents capable of deciding for real-world problems based on utility. The utility can be set as a real number, for example, on a scale of 10 how much customer is satisfied with the services of the agent.
5. Learning Agents
This agent is capable of learning from the experience that is whatever the actions it has performed; it takes feedback and adapts accordingly. For a Learning agent to work the way, it has four components. First is the learning element, which learns from experience. Second is the critic, which is a feedback system about how well the agent is doing. The third is the performance element, which decides what external action should be taken. The last one is a problem generator which is a feedback agent that keeps history and makes new suggestions.
Process of Intelligent Agent in AI
An AI agent is one who perceives the environment using its sensors and then with its artificial intelligence makes a decision and via actuators perform actions. Sensors are the medium to provide input to an agent for humans, input sensors are eyes, ears, touch, tongue, etc., in the same way for AI agent it could be cameras, NLP, or output from various sensors. As actuators we humans have hands, legs, expression, and mouth to perform actions, the same way the AI agents have robotic arms, motors, or performing any software integral action. An AI agent is a combination of architecture (the machinery part) and an agent program (functions and conditions).
The following steps are involved in the process of AI agents:
- An AI agent shall take inputs from the environment using sensors.
- These observations are then considered for making decisions using artificial intelligence.
- The decision shall trigger action from agents through actuators.
- Depending on the agent, it will keep the history of past action and will use the feedback of past actions in deciding for future events.
Benefits of using Intelligent Agent in AI
The following are the benefits of using an intelligent agent.
- The agent performs actions based upon decisions made by AI. That is to convert ideas into action.
- An intelligent agent can work on simple commands like a human voice to perform actions, e.g., Alexa, Siri.
- Intelligent agents do evolve with time, unlike classic agents who can perform a set of predefined actions.
- AI agents can take utility measurements into account, which makes them more realistic.
An AI agent is action taking entity that precepts the environment as input and then with its artificial intelligence makes a decision. Generally, to perceive the environment, it uses a sensor and based on intelligence, it chooses an action item and performs it through actuators. We have discussed the types of agents that are available. We have discussed the process that agents follow, and at last, we have discussed the benefits of the same too.
This is a guide to Intelligent agents in AI. Here we discuss the introduction to Intelligent agents in AI, what it is, the Process, and the benefits of using it. You can also go through our other related articles to learn more –