5 Types of Intelligent Agents in Artificial Intelligence

Artificial Intelligence works on a few concepts and the environment surrounding them, to be a functional field of study. These concepts are Agents. Now, what are agents? Are they highly qualified and intelligent spies that work in the field of AI, or are they electronic devices that we work upon?

 

There might be a lot of questions from your side. In this article, we will explain what AI agents are and learn their types.

 

What is an Agent?

 

An "agent" is an automated entity in the context of artificial intelligence (AI) that interacts with its surroundings by sensing its surroundings using sensors, then acting with actuators or effectors. 

 

Actuators are the elements that translate energy into motion. They take on the function of directing and moving a system. A few examples include gears, motors, and railroads.

 

In simple words, it is a decision-making entity that enables the AI to do some work. AI is all about studying rational agents. 

 

Anything that makes decisions, whether it be a person, business, computer, or piece of software, can be a rational agent. These rational agents will always have an environment that might contain another agent. Since these agents have the power to automatically learn and process things, these agents are known as Intelligent Agents (IA).

 

Generally speaking, following are the examples of intelligent agents:

 

• Humans: Yes, all humans are agents. Humans have hands, legs, mouths, and other bodily parts that serve as actuators in addition to their eyes, hearing, and other sensors.

 

• Software: This Agent receives sensory information from file contents, keystrokes, and network packages, acts on that input, and then displays the result on a screen.

 

• Robotic: Robotic agents feature sensors like cameras and infrared range finders and actuators like different servos and motors.

 

How are the IAs structured?

 

Whatever the IA is, it has a particular structure. It consists of the following parts.

 

• Architecture: This describes equipment or gadgets with actuators and sensors. This equipment is operated by an intelligent agent. Personal computers, automobiles, and cameras are a few examples.

 

• Agent Function: Actions are mapped from a certain perceptual sequence in the agent function. A history of what the intelligent agent has perceived is referred to as a percept sequence.

 

• Agent program: This is how the agent function is implemented or carried out. The agent program's execution on the physical architecture results in the agent function.

 

How do the IAs interact with their environment?

 

As mentioned above, all intelligent agents have an environment, and they constantly interact with their environments. There are mainly two ways how IAs interact with their environment:

 

1. Perception:

 

In a passive interaction called perception, the agent learns about its surroundings without making any changes to them. The robot's sensors enable it to gather information about its surroundings without having an impact on them. Consequently, receiving information from sensors is referred to as perception.

 

2. Action:

 

A proactive contact in which the environment is altered is called an action. It is referred to as an action when the robot moves an obstruction with its arm because the environment has altered. As it completes the task, the robot's arm is referred to as an "Effector".

 

Examples of Effectors include legs, fingers, wheels, display screens, and arms.

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Components of IAs (source) 

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How should the Agents Act in AI?

 

The following details how an agent should behave:

 

• A logical actor makes the proper decision. The course of action that makes the agent the most successful is the right one.

 

• An omniscient agent may anticipate the effects of a decision and take appropriate action, but this is not practical in everyday life.

 

• The level of achievement as determined by the performance measure

 

• The agent's full series of perceptions up to the present is known as the percept sequence.

 

• The agent's understanding of the surroundings

 

• What operations is the agent capable of?

 

What are the types of Agents used in AI?

 

Depending on how intelligent and capable they are believed to be, agents may be divided into five groups. Over time, each of these substances might enhance their effectiveness and produce greater results.

 

Here are the five types of agents:

 

1. Simple-Reflex Agent:

 

The simplest agents are those with basic reflexes. These agents disregard the remainder of the perception history and base their judgments only on their present perceptions. These agents only work well in environments that are completely visible. The Simple Reflex Agent makes decisions and does actions without taking any account of perceptual history.

 

The Condition-action rule under which the Simple Reflex Agent operates allows it to translate the current state into action. Like a room cleaner, it only functions if there is filth in the space.

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Simple reflex agent design approach (source)

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Here are some disadvantages of the simplex reflex agent:

 

• These agents have very limited intelligence.

• They are unaware of non-perceptual elements of the current situation.

• Most of the time, it is too enormous to produce and store.

• These agents are not able to adjust to environmental changes.

 

2. Model-based Reflex Agent:

 

The model-based agent can track the situation and operate in a partially observable environment.

 

Two crucial components make up a model-based agent:

 

• Model: A model-based agent is one that has knowledge of "how things happen in the world."

 

• Internal State: This portrayal of the present condition is based on perceptual history.

 

It operates by identifying a rule whose condition corresponds to the present circumstance. By using a model of the world, a model-based agent may manage partially observable settings. The agent must monitor its internal state, which is altered by each perception and is influenced by its prior perceptions. The agent stores the current state and keeps some sort of structure characterizing the invisible portion of the universe.

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The model-based reflex agent design approach (source)  

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3. Goal-Based Reflex Agents:

 

They decide what they will do in order to accomplish their objectives. Since the knowledge underpinning a choice is clearly represented in a goal-based approach, it is more adaptable than the reflex agent.

 

This gives the agent the ability to pick from a variety of options, choosing the one that leads to the desired state. These agents are more adaptable because the knowledge that underpins their decisions is expressed directly and is modifiable. Typically, they call for research and preparation. The behavior of the goal-based agent is easily modifiable.

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Goal-based agents (source)

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4. Utility-Based Agents:

 

Knowing the environment's present condition is not always enough for an agent to make decisions. The agent must be aware of its aim, which outlines ideal circumstances. By possessing the "target" knowledge, goal-based agents enhance the model-based agent's capabilities.

 

They decide on a course of action in order to fulfill their objective. Before determining whether the aim has been accomplished or not, these agents may have to take into account a lengthy list of potential activities. Such analyses of many scenarios are referred to as seeking and planning, which empowers an agent to take the initiative.

 

When goals are insufficient.

 

• There are many competing objectives, but only a select number can be accomplished.

• Goals have a chance of achievement, therefore you must balance their importance with the possibility of accomplishment.

 

5. Learning Agents:

 

An AI agent with learning capabilities or the ability to learn from its experiences in the past is referred to as a "learning agent." Beginning with fundamental information, it may then act and adapt on its own through learning.

 

A learning agent primarily consists of these four conceptual parts:

 

• Element of learning: It is accountable for producing advancements by absorbing environmental knowledge.

 

• Critic: The learning component uses criticism to gauge how well the agent is performing in relation to a predetermined performance benchmark.

 

• Element of performance: It is in charge of choosing an external action.

 

• Problem Generator: This element is in charge of making recommendations for activities that will result in novel and educational experiences.

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Learning agents (source)

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Applications of Intelligent Agents:

 

Artificial intelligence-based intelligent agents have been used extensively in daily life:

 

1. Information retrieval, navigation, and search:

 

Intelligent agents improve information access and navigation. This is accomplished by employing search engines to look for information. Users may have to spend a lot of time searching through the internet's numerous data objects in order to find a particular data item. This work is quickly completed on behalf of users by intelligent agents.

 

2. Monotonous office tasks:

 

To cut operational expenses, several businesses have automated a few administrative duties. Customer service and sales are two functional domains that have been mechanized. Office productivity has also been increased with intelligent agents.

 

3. Medical evaluation:

 

To help patients' health, intelligent agents have also been used in healthcare services. The patient is regarded as the environment in this situation. The sensor that collects information on the patient's symptoms is a computer keyboard. This data is used by the intelligent agent to choose the optimal course of action. Actuators used in healthcare include testing and treatments.

 

Also Read: AI in Healthcare

 

4. Vacuum cleaning:

 

AI agents are also utilized to improve vacuum cleaning's effectiveness and cleanliness. The surroundings in this scenario might be a table, carpet, or room. Cameras, bump sensors, and sensors for dirt detection are a few of the sensors used in vacuum cleaning. Actuators like brushes, wheels, and vacuum extractors start the action.

 

5. Autonomous vehicle:

 

Self-driving automobiles operate better with the assistance of intelligent agents. Various sensors are used in autonomous driving to get data from the surroundings. These consist of radar, GPS, and cameras. The environment in this application might consist of pedestrians, other cars, roadways, or road signs. Actions are started using a variety of actuators. For instance, the car's brakes are used to stop it.

 

Also Read | Confidential Computing in AI Autonomous Vehicles

 

How to improve the performance of Intelligent Agents?

 

We only need to question ourselves, "How can we increase our performance in a task?" while confronting the problem of how to improve intelligent Agent performances. Clearly, the solution is straightforward. We carry out the task, recall the outcomes, and make adjustments in light of our memories of prior tries.

 

In the same manner that humans improve, so do AI agents. By storing its previous responses and attempts, the Agent improves and learns how to react more effectively in the future. Artificial intelligence and machine learning converge here.

 

Problem-solving Artificial intelligence agents use a variety of algorithms and analyses  to provide answers. As follows:

 

1. Search Algorithms:

 

The use of search strategies is seen to be a universal approach to issue resolution. These algorithms and tactics are used by rational or problem-solving agents to solve issues and produce the best outcomes.

 

• Uninformed search algorithms, often known as blind searches, operate via brute force and lack any subject expertise.

 

• Informed search algorithms, often referred to as heuristic searches, employ domain expertise to identify the search tactics required to locate the solution to a problem.

 

2. Hill Climbing Algorithms: 

 

Hill climbing algorithms are local search algorithms that go continually upward, increasing their value or height until they locate the ideal answer to the issue or the top of the mountain.

 

Hill climbing algorithms are very good at maximizing the solution of mathematical problems. Because it only considers its good near neighbor, this algorithm is also known as a "greedy local search."

 

3. Mean-Ends Analysis:

 

The means-end analysis is a method of problem-solving that combines backward and forward search approaches to restrict searches in artificial intelligence applications.

 

The means-end analysis determines the optimal operators to employ for each difference after evaluating the differences between the Initial State and the Final State. The analysis then reduces the difference between the current and desired states by applying the operators to each matched difference.

 

Conclusions:

 

By doing some time-consuming and difficult tasks on behalf of systems or users, intelligent agents facilitate work. Certain chores may now be automated thanks to these agents.

 

There will be a greater creation of intelligent agents as technology advances. Complex AI-driven gadgets that address today's global concerns will result from this. There doesn't appear to be any end to this fascinating technology.