In artificial intelligence (AI), agents represent the dynamic entities that interact with their environment to achieve specific goals. These intelligent agents are pivotal in simulating human-like decision-making and problem-solving capabilities across various applications—from autonomous vehicles navigating city streets to chatbots assisting users online. Understanding the architecture, behavior, and classifications of agents unveils the core principles driving advancements in AI and shaping our future interactions with technology.
In artificial intelligence, in order to perceive and interpret information using simulations which are identical to the working of a human brain, we require a medium or a “human-like agent” which can derive useful information from the environment and make sensible decisions based on that information. Therefore, the role of an agent comes into play. Now, let us understand further what we imply by the term “agent” in artificial intelligence.
What is an agent in artificial intelligence?
An Artificial Intelligence system is composed of an agent and its environment. The agents act as mediators for information in their environment, and an environment can consist of multiple agents.
Human-Agent: The human agent has eyes, ears, and other organs which act as sensors. Similarly, the hands, legs, and other organs act as actuators.
Robotic Agent: The robotic agent has cameras and IR range finders which act as sensors. Similarly, various motors of robotic agent act as actuators.
Software Agent: It has keystrokes, file contents, etc., which act as sensors and files, and displays on the screen act as actuators.
Before moving forward, we should first know about sensors, effectors, and actuators.
Sensors: It is a device that can detect the change in the environment and send the information to other devices in the environment. An agent observes its surroundings with the help of sensors.
Actuators: They are the components of the machines in the environment that convert energy into motion. They are responsible for moving and controlling a system and can be in the forms of electric motors, gears, rails, etc.
Effectors: These devices affect the environment through actions. An agent can perform any action through effectors and they can be in the form of legs, wheels, arms, display screen, etc.
Now, when we discuss about an agent, there are a few properties of the corresponding environment in which an agent can act and they are as follows:
The environment in which there are a limited number of distinct and clearly defined states is a discrete environment, for example, chess, else it is a continuous environment such as driving.
An environment that does not undergo changes when an agent is working is a static environment else it is dynamic.
The environment can consist of multiple agents that may or may not be of the same kind.
The environment is accessible to an agent if the agent’s sensory apparatus can access the complete state of the environment.
If the next state of the environment is completely determined by the current state and the actions of the agent, then the environment is deterministic else it is non-deterministic.
In an episodic environment, an agent does not need to think ahead of time for its actions as it perceives and acts within the same episode, and subsequent episodes are not dependent upon the actions of the previous episodes.
How does an AI agent work?
An AI agent works by perceiving its environment through sensors, processing that information internally, and then taking actions to achieve specific goals. Here's a more detailed breakdown of how an AI agent operates:
Perception: The agent receives input from its environment through sensors. These sensors can vary widely depending on the application—cameras for visual data, microphones for audio, or even software interfaces for data inputs.
Processing: Once the agent receives sensory input, it processes this information using algorithms and models. This processing typically involves:
Interpreting Data: Converting raw sensor data into a format that the agent can understand and manipulate.
Decision Making: Using various AI techniques such as machine learning, logic-based reasoning, or neural networks to analyze the interpreted data and make decisions.
Action: Based on the analysis and decision-making process, the agent selects actions to perform. These actions are chosen to achieve specific goals or objectives set for the agent. Actions can range from simple commands (like moving a robotic arm) to complex sequences (like navigating a maze or responding to a user query).
Feedback Loop: After taking actions, the agent receives feedback from the environment through sensors again. This feedback helps the agent adjust its future actions, improving its performance over time through learning and adaptation.
Goal Achievement: Throughout this process, the agent's overarching objective is to achieve predefined goals or objectives. These goals could be set by human programmers or autonomously learned through interaction with the environment.
Structure of an agent in artificial intelligence
Artificial intelligence is utilized to design agent programs that can implement the agent function.
The structure of an AI agent is composed of the architecture and an agent program. The architecture is the machinery on which the AI agents perform execution, and the agent function is used to map a percept to an action.
Functions of an Artificial Intelligence Agent
AI agents are software programs that can interact with their environment and learn. Here are some key functions of an AI agent:
Perception: AI agents can sense and interpret their environment through various means. This could involve cameras, microphones, or even access to databases.
Learning: AI agents can improve their performance through different learning techniques. This includes machine learning, deep learning, and reinforcement learning, allowing them to adapt to new situations.
Reasoning and decision-making: AI agents can analyze information and make choices based on their goals. They use algorithms and learned patterns to determine the best course of action.
Action: AI agents can take actions in the environment to achieve their goals. This might involve controlling robots, generating text, or recommending products.
Challenges of Using AI Agents
While AI agents offer many benefits, there are also challenges to consider:
Data and Bias: AI agents are only as good as the data they are trained on. Biases in the data can lead to biased decisions by the AI agent.
Explainability: It can be difficult to understand how an AI agent arrives at a decision, making it challenging to trust or debug their reasoning.
Safety and Security: AI agents that control physical systems or have access to sensitive information need to be secure from hacking or misuse.
Ethical Considerations: The development and use of AI agents raise ethical concerns, such as job displacement and the potential for autonomous weapons.
Types of Agents in Artificial Intelligence
Agents are categorized into five types based on their degree of perceived intelligence and capabilities. Let us discuss each type of agent in detail.
1. Simple Reflex Agents
These are the simplest forms of agents and they make decisions based on the current percepts and ignore the rest of the percept history. They can only be successful in a fully observable environment. They function on the condition-action rule, which means it maps the current state to action, for example, a Room Cleaner agent will only work in a room if there is dirt.
Problems that simple reflex agents face:
They have limited intelligence.
They do not have knowledge of non-perceptual parts of the current state.
They are mostly too big to generate and store.
They are not adaptive to the changes in the environment.
2. Model-Based Reflex Agents
Model-based reflex agents are responsible for dealing with partial accessibility. This goal can be achieved by keeping track of the currently visible world and for executing it, these agents find a rule whose condition matches the current situation and then work. They can handle partially observable environments with the help of a model about the world. It keeps an internal state that depends on what it has perceived before and therefore, it holds information on the unobserved aspects of the current state. Updating these agents requires information about how the world evolves and how the agent’s actions affect the world.
3. Goal-Based Agents
These agents make decisions based on how far they are currently from their set goal. Their action is intended to reduce the distance between the current position and the goal. This allows the agent to choose an action which reaches a goal among multiple possible actions. The knowledge that supports its decisions is represented explicitly and can be modified, which makes these agents more flexible. They require search and planning and their behaviour can be easily changed.
4. Utility-Based Agents
These agents use their end uses as building blocks and are used when there are multiple possible alternatives to decide the best one. They choose actions based on a preference for each state. A utility agent selects the action that maximizes the expected utility. A state is mapped onto a real number using a utility function, which describes the associated degree of happiness.
5. Learning Agent
This agent can learn from its past experiences or it has learning capabilities. In the beginning, it starts to act with basic knowledge and later on it acts and adapts automatically through learning. A learning agent has four conceptual components, which are: Learning element, critic, performance element and problem generator. A learning element makes improvement, a critic takes feedback to describe how the agent is performing, a performance element selects external actions and a problem generator suggests actions that will lead to successful experiences.
PEAS Representation
PEAS representation is used to define the characteristics of an agent in artificial intelligence. Whenever an AI agent is defined, its properties can be grouped under PEAS representation. Each component of PEAS representation has a key aspect of the agent's behaviour. The PEAS is made of below words:
Performance measure (P): It is the unit which is used to define how successful the agent is.
Environment (E): It is the surrounding of the agent at every instance.
Actuators (A): The actuators deliver the output of the agent's action to the environment.
Sensors (S): These are the receiving points of an agent. All the inputs to the agent are taken by the sensors.
Example of Agents with their PEAS representation
The below table shows some examples of agents with their peas components:
Agent
Performance Measure
Environment
Actuators
Sensors
Medical Diagnose
Patient's health, cost
Patient, doctor, nurse, hospital
Test, treatment, diagnosis
Symptoms
Vacuum cleaner
Efficiency, cleanliness, battery
Room, carpet, floor
Wheels, brushes
Camera, different sensors
Automated vehicles
Safety, time, comfort, distance
Roads, traffic, signals, vehicles
Break, accelerator, horn, mirror
GPS, camera
Tutoring
Marks, attendance
Classroom, desk, chair, students
Displays, corrections
Notebook, eyes, ears
Part-picking robot
Percentage of parts in right bins
Bins
Joined arms, hand
Camera, sensors
Frequently Asked Questions
What is an intelligent agent?
An intelligent agent (IA) is an autonomous system that can perceive its environment, learn from it, and take actions to achieve its goals. It essentially acts intelligently within its surroundings.
What is an example of an intelligence agent?
A self-learning thermostat is a simple example of an intelligent agent. It perceives the room temperature (sensing), learns user preferences over time (learning), and adjusts the temperature accordingly (acting) to maintain a comfortable environment (goal).
What are agents and environments?
In the context of AI, an agent is a program that can interact with its surroundings. This environment could be the physical world (a robot vacuum) or a digital space (a chess-playing program). The agent uses sensors to perceive the environment and takes actions to influence it.
How to create an AI agent?
Creating an AI agent involves programming it with four key capabilities:
Perception: Ability to sense the environment through sensors (cameras, microphones, etc.)
Learning: Ability to improve its performance over time through techniques like machine learning.
Reasoning and Decision-Making: Ability to analyze information and make choices based on its goals.
Action: Ability to take actions in the environment to achieve its goals (controlling robots, generating text, etc.).
Conclusion
In this blog, we understood what an agent is in an artificial intelligence system and what the role of an agent is. We also discussed different types of agents along with the structure of an AI agent.
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