AI Agents and Their Environments

- Overview

In artificial intelligence (AI), an agent's environment is its surroundings. Agents interact with their environments in two main ways: perception and action. 

An agent is anything that can be thought of as: sensing its environment through sensors and taking actions on that environment through actuators.

Here's some information about AI agents and their environments: 

• Agents: AI agents can be simple systems that follow rules or complex entities that learn and adapt. They can have mental properties like knowledge, beliefs, and intentions. Agents can perceive their environment through sensors, process information using models or algorithms, and then take action using actuators or other means.
• Environments: There are several types of environments, including fully observable vs partially observable, and deterministic vs stochastic. For example, a deterministic environment is considered strategic because the output is determined based on a specific state. An environment is dynamic if it changes while an agent is responding to a percept sequence. It is static if it does not change while the agent is deciding on an action.

Here are some examples of human-agent and robotic-agent sensors and actuators: 

• Human-agent: Eyes, ears, and other organs act as sensors, and hands, legs, mouth, and other body parts act as actuators
• Robotic-agent: Cameras and infrared range finders act as sensors, and various motors act as actuators

- Nature of Environments

When designing AI solutions, we spend a lot of time focusing on aspects such as the nature of learning algorithms [ex: supervised, unsupervised, semi-supervised] or the characteristics of the data [ex: classified, unclassified…]. 

However, little attention is often provided to the nature of the environment on which the AI solution operates. As it turns out, the characteristics of the environment are one of the absolutely key elements to determine the right models for an AI solution. 

An environment is everything in the world which surrounds the agent, but it is not a part of an agent itself. An environment can be described as a situation in which an agent is present. The environment is where agent lives, operate and provide the agent with something to sense and act upon it. 

The agent takes input from the environment through sensors and delivers the output to the environment through actuators. For example, program a chess bot, the environment is a chessboard and creating a room cleaner robot, the environment is Room. Each environment has its own properties and agents should be designed such as it can explore environment states using sensors and act accordingly using actuators. 

- Intelligent Agents

Intelligent agents (IAs) are software programs that can autonomously make decisions or take actions to achieve a goal. They are commonly used in AI to solve complex tasks that are difficult or impossible for humans to do. 

IAs use sensors to perceive the environment, make a decision, and act upon that information using actuators. They can be characterized by various attributes including: Autonomous, Adaptive, Collaborative, Communicative, Mobile, Reactive. 

Some characteristics of IAs include: 

• They have a learning ability that enables them to learn even as tasks are carried out.
• They can interact with other entities such as agents, humans, and systems.
• New rules can be accommodated by intelligent agents incrementally.
• They exhibit goal-oriented habits.

Some types of intelligent agents include: 

• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
• Learning agents

- Rational Agents vs. Intelligent Agents

A rational agent is a type of intelligent agent. 

An intelligent agent is a system that can perceive its environment and take actions to achieve a specific goal. It can be a robot, machine, or even a human or an animal. 

A rational agent is an intelligent agent that makes decisions based on logical reasoning and optimizes its behavior to achieve a specific goal. It can be anything that makes decisions, typically a person, firm, machine, or software. 

Here are some examples of rational agents: a vacuum cleaner, driverless cars, the Siri virtual assistant.

- AI Systems, Intelligent Agents and Their Environments

Artificial Intelligence (AI) is defined as the study of rational agents. A rational agent can be anyone who makes a decision, such as a person, a company, a machine, or software. It performs actions with optimal results after taking into account past and current perceptions. 

An AI system consists of agents and their environments. An intelligent (rational) agent performs an action with the best outcome after taking into account past and current perceptions (the agent's sensory input at a given instance). 

One of the important characteristics of an intelligent agent is the ability to evaluate its environment in order to decide on the correct action to take. Doing so is always difficult because many factors, including uncertain information, knowledge, and limited time, affect how an agent perceives its environment. 

Intelligent Agents (IA) can make the right decisions in any situation. Performance measurement should be based on the agent's expected impact on the environment. Performance measurement is a set of criteria/testbed for successful agent behavior. 

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- Agent Based Intelligent Systems

In an Artificial Intelligence (AI) world, Agent-based technology is one of the most vibrant and important areas of R&D in the industry in recent years. Intelligent Agent (IA) is an autonomous entity which observes, analyses and responds to an environment appropriate to achieve the expected objective. 

The IA posses several categories such as Coordination, Integration, Mobility, Believable Agent and Assistance in achieving its expectancy. Agent Program is a tool/process which supports the IA Implementation. Agent program is defined briefly as a mathematical function of an IA which maps all the possible sequences of perceptions in every action. IA can respond either to a resulted coefficients or feedback elements or even to a function or constant which affects eventual actions.  

- Perception and Action

For purposes of AI, perception is the process of transforming something from the environment into internal representations (memories, beliefs, etc.). Action done when the agent, by doing something, changes the environment. 

In AI, perception is the process by which a system can interpret data from its environment. This includes understanding information, recognizing objects, and identifying patterns. 

For example, if a robot uses its camera to determine that there is a wall in front of it, then it is using perception. In this example, the camera is a "sensor." In general, sensors are what agents use to get things from the environment to do perception. 

Human sensors include eyes, ears, and the nose. AIs can have sensors of many types, including ones analogous to human perception, but also including some that humans do not have, such as sonar, infrared, GPS signals, etc.

- Machine Perception

Machine perception is when a machine uses input data from sensors to learn about the world around it. For example, machine perception can tell an object's position or movement trajectory in a scene. 

Perception in AI is important for many applications, including: 

• Self-driving cars
• Virtual assistants
• Speech recognition
• Facial recognition
• Object recognition
• Music recording and compression
• Speech synthesis
• Perception helps machines and robots react like humans