Exploring Intelligent Agents in Artificial Intelligence

What Is an Agent in AI?

Okay, did anyone, upon hearing the term “intelligent agent,” immediately picture a well-educated spy with a high IQ? No? Anyway, in the context of the AI field, an “agent” is an independent program or entity that interacts with its environment by perceiving its surroundings via sensors, then acting through actuators or effectors.

Agents use their actuators to run through a cycle of perception, thought, and action. Examples of agents in general terms include:

• Software: This Agent has file contents, keystrokes, and received network packages that function as sensory input, then act on those inputs, displaying the output on a screen.
• Human: Yes, we’re all agents. Humans have eyes, ears, and other organs that act as sensors, and hands, legs, mouths, and other body parts act as actuators.
• Robotic: Robotic agents have cameras and infrared range finders that act as sensors, and various servos and motors perform as actuators.

Intelligent agents in AI are autonomous entities that act upon an environment using sensors and actuators to achieve their goals. In addition, intelligent agents may learn from the environment to achieve those goals. Driverless cars and the Siri virtual assistant are examples of intelligent agents in AI.

These are the main four rules all AI agents must adhere to:

• Rule 1: An AI agent must be able to perceive the environment.
• Rule 2: The environmental observations must be used to make decisions.
• Rule 3: The decisions should result in action.
• Rule 4: The action taken by the AI agent must be a rational. Rational actions are actions that maximize performance and yield the best positive outcome.

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The Functions of an Artificial Intelligence Agent

Artificial Intelligence agents perform these functions continuously:

• Perceiving dynamic conditions in the environment
• Acting to affect conditions in the environment
• Using reasoning to interpret perceptions
• Problem-solving
• Drawing inferences
• Determining actions and their outcomes

The Number and Types of Agents in Artificial Intelligence

There are five different types of intelligent agents used in AI. They are defined by their range of capabilities and intelligence level:

• Reflex Agents: These agents work here and now and ignore the past. They respond using the event-condition-action rule. The ECA rule applies when a user initiates an event, and the Agent turns to a list of pre-set conditions and rules, resulting in pre-programmed outcomes.
• Model-based Agents: These agents choose their actions like reflex agents do, but they have a better comprehensive view of the environment. An environmental model is programmed into the internal system, incorporating into the Agent's history.
• Goal-based agents: These agents build on the information that a model-based agent stores by augmenting it with goal information or data regarding desirable outcomes and situations.
• Utility-based agents: These are comparable to the goal-based agents, except they offer an extra utility measurement. This measurement rates each possible scenario based on the desired result and selects the action that maximizes the outcome. Rating criteria examples include variables such as success probability or the number of resources required.
• Learning agents: These agents employ an additional learning element to gradually improve and become more knowledgeable over time about an environment. The learning element uses feedback to decide how the performance elements should be gradually changed to show improvement.

The Structure of Agents in Artificial Intelligence

Agents in Artificial Intelligence follow this simple structural formula:

Architecture + Agent Program = Agent

These are the terms most associated with agent structure:

• Architecture: This is the machinery or platform that executes the agent.
• Agent Function: The agent function maps a precept to the Action, represented by the following formula: f:P* - A
• Agent Program: The agent program is an implementation of the agent function. The agent program produces function f by executing on the physical architecture.

Many AI Agents use the PEAS model in their structure. PEAS is an acronym for Performance Measure, Environment, Actuators, and Sensors. For instance, take a vacuum cleaner.

• Performance: Cleanliness and efficiency
• Environment: Rug, hardwood floor, living room
• Actuator: Brushes, wheels, vacuum bag
• Sensors: Dirt detection sensor, bump sensor

Here’s a diagram that illustrates the structure of a utility-based agent, courtesy of Researchgate.net.

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What Are Agents in Artificial Intelligence Composed Of?

Agents in Artificial Intelligence contain the following properties:

• Environment

The agent is situated in a given environment.

• Autonomous

The agent can operate without direct human intervention or other software methods. It controls its activities and internal environment. The agent independently which steps it will take in its current condition to achieve the best improvements. The agent achieves autonomy if its performance is measured by its experiences in the context of learning and adapting.

• Flexibility

• Reactive: Agents must recognize their surroundings and react to the changes within them.
• Proactive: Agents shouldn’t only act in response to their surroundings but also be able to take the initiative when appropriate and effect an opportunistic, goal-directed performance.
• Social: Agents should work with humans or other non-human agents.

• Reactive

• Reactive systems maintain ongoing interactions with their environment, responding to its changes.
• The program’s environment may be guaranteed, not concerned about its success or failure.
• Most environments are dynamic, meaning that things are constantly in a state of change, and information is incomplete.
• Programs must make provisions for the possibility of failure.

• Pro-Activeness

Taking the initiative to create goals and try to meet them.

• Using Response Rules

The goal for the agent is directed behavior, having it do things for the user.

• Mobility: The agent must have the ability to actuate around a system.

• Veracity: If an agent’s information is false, it will not communicate.

• Benevolence: Agents don’t have contradictory or conflicting goals. Therefore, every Agent will always try to do what it is asked.

• Rationality: The agent will perform to accomplish its goals and not work in a way that opposes or blocks them.

• Learning: An agent must be able to learn.

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How to Improve the Performance of Intelligent Agents

When tackling the issue of how to improve intelligent Agent performances, all we need to do is ask ourselves, “How do we improve our performance in a task?” The answer, of course, is simple. We perform the task, remember the results, then adjust based on our recollection of previous attempts.

Artificial Intelligence Agents improve in the same way. The Agent gets better by saving its previous attempts and states, learning how to respond better next time. This place is where Machine Learning and Artificial Intelligence meet.

All About Problem-Solving Agents in Artificial Intelligence

Problem-solving Agents in Artificial Intelligence employ several algorithms and analyses to develop solutions. They are:

• Search Algorithms: Search techniques are considered universal problem-solving methods. Problem-solving or rational agents employ these algorithms and strategies to solve problems and generate the best results.

Uninformed Search Algorithms: Also called a Blind search, uninformed searches have no domain knowledge, working instead in a brute-force manner.

Informed Search Algorithms: Also known as a Heuristic search, informed searches use domain knowledge to find the search strategies needed to solve the problem.

• Hill Climbing Algorithms: Hill climbing algorithms are local search algorithms that continuously move upwards, increasing their value or elevation until they find the best solution to the problem or the mountain's peak.

Hill climbing algorithms are excellent for optimizing mathematical problem-solving. This algorithm is also known as a "greedy local search" because it only checks out its good immediate neighbor.

• Means-Ends Analysis: The means-end analysis is a problem-solving technique used to limit searches in Artificial Intelligence programs, combining Backward and Forward search techniques.

The means-end analysis evaluates the differences between the Initial State and the Final State, then picks the best operators that can be used for each difference. The analysis then applies the operators to each matching difference, reducing the current and goal state difference.

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