Artificial Intelligence Tutorial

Artificial Intelligence Tutorial

Artificial intelligence (AI) is significant since it enhances many facets of society and makes life easier for us. An introduction to artificial intelligence is given in the AI tutorial, which will aid in your understanding of the underlying ideas.

Introduction to Artificial Intelligence

The process of teaching a computer, a robot under computer control, or software to think intelligently and similarly to intelligent humans is known as artificial intelligence. In this artificial intelligence tutorial, we cover the following:

• Overview of AI
• Intelligent Systems
• Agents and Environments

Overview of AI

The process of creating artificial intelligence (AI) involves first understanding how the human brain functions and how people learn, make decisions, and collaborate to solve problems. Subsequently, the research findings are applied to develop intelligent software and systems. 

The Goal of AI

The goal of AI development from the outset was to imbue computers with a level of intellect comparable to that we value highly in humans.

• To Develop Expert Systems: Systems that possess intelligent behavior, are able to learn, show, clarify, and provide guidance to their users.
• To Incorporate Human Intelligence into Machines: Constructing entities with human-like comprehension, cognition, learning, and behavior.

AI Technique

The AI technique is a way to efficiently organize and use knowledge so that:

• The individuals who give it ought to be able to perceive it.
• It should be simple to adjust in order to fix mistakes.
• Despite being erroneous or partial, it ought to be helpful in a variety of circumstances.
• AI approaches accelerate the sophisticated programs that they are supplied with.

Applications of Artificial Intelligence

AI has dominated a number of fields, including:

• Gaming: To make large number of possible positions in strategic-based games.
• NLP (natural language processing): To interact easily with humans and machines.
• Expert Systems: To offer suggestion to users
• Vision Systems: Various professionals, such as law enforcement and medical personnel, have several objectives.
• Speech Recognition: To handle various accents, noises, slang words, etc.
• Handwriting Recognition: To convert the shapes to text.
• Intelligent Robots: To perform various tasks of humans efficiently.

Intelligent Systems

The capacity of a system to perform calculations, reason, recognize connections and analogies, learn from mistakes, store and recall data from memory, solve issues, understand complicated concepts, speak naturally, categorize, generalize, and adjust to novel circumstances. 

Types of Intelligence

According to American developmental psychologist Howard Gardner, intelligence has multiple uses.

Linguistic Intelligence

The capacity for speaking, understanding, and using phonological (speech sound), syntax (grammar), and semantic (meaning) procedures.

Example: Narrators, orators.

Musical Intelligence

The capacity to produce, convey, and comprehend meaning through sound, pitch, and rhythm.

Example: musicians, singers, and composers.

Logical-mathematical Intelligence

The capacity to use and comprehend relationships when there are no objects or actions present. grasping abstract and difficult concepts.

Example: mathematicians, scientists.

Spatial Intelligence

The capacity to perceive, alter, and recreate visual images without reference to things; to build three-dimensional images; and to move and rotate those images.

Example: map readers, astronauts, and physicists.

Bodily-Kinesthetic intelligence

The capacity to use all or a portion of the body to control fine and coarse motor abilities, handle objects, solve problems, or create goods.

Example: players, dancers.

Intra-personal intelligence

The capacity to differentiate between one’s own intents, motives, and feelings.

Example: Gautam Buddhha.

Interpersonal intelligence

The capacity to identify and distinguish between the intentions, feelings, and beliefs of another.

Example: mass communicators and interviewers.

When a machine or system possesses at least one and up to all intelligences, you can call it artificially intelligent.

The Components of Intelligence

There is no tangible intellect. It consists of:

• Reasoning
• Learning
• Problem Solving
• Perception
• Linguistic Intelligence

Reasoning

This group of procedures can form the foundation for judging, choosing, and forecasting. In general, there are two kinds:

Inductive reasoning: To draw broad, general conclusions, it makes specific observations. Inductive reasoning permits a false conclusion even in cases where all of the premises are true.

Deductive reasoning: It begins with a broad assertion and considers all options in order to arrive at a precise, make sense conclusion. All members of a class of things are also subject to the general truths about them.

Learning

Learning is the process of acquiring knowledge or skills through study, practice, instruction, or experience. Gaining knowledge increases one’s awareness of the study’s subjects.

Humans, as well as some animals and AI-enabled systems, are capable of learning. Learning is classified as:

• Auditory Learning
• Episodic Learning
• Motor Learning
• Observational Learning
• Perceptional Learning
• Relational Learning
• Spatial Learning
• Stimulus-Response Learning

Problem-Solving

It’s the process of seeing a situation as it is and trying to find a way around known or unknown obstacles to reach a desired outcome.

Making decisions, which is the process of choosing the most appropriate option from a range of options to accomplish a goal, is another aspect of problem-solving.

Perception

It is the procedure for gathering, deciphering, choosing, and arranging sensory data.

Sensing is necessary for perception. In humans, sensory organs facilitate perception. Within the field of artificial intelligence, the perception mechanism integrates the data collected by the sensors in a significant way.

Linguistic Intelligence

It is the capacity to utilize, understand, write, and speak both written and spoken words. In interpersonal communication, it is crucial.

Agents and Environments

The components of an AI system are an agent and its environment. The agents behave within their environment.

Agents

Anything that has the ability to use sensors to sense its environment and effectors to manipulate that environment is considered an agent.

Human Agent: In addition to other organs like hands, legs, and mouth for effectors, a human agent possesses sensory organs including eyes, ears, nose, tongue, and skin parallel to the sensors.

Robotic Agents: Robotic agents serve as sensors in place of cameras and infrared range finders, and other motors and actuators serve as effectors in place of cameras.

Software Agent: Encoded bit strings make up a software agent’s programs and behaviors.

Popular Terms in Agents

Sensors: A sensor is an apparatus that senses changes in its environments and communicates this information to other electrical equipment. An agent uses sensors to monitor its environment.

Actuators: The parts of machines that transform energy into motion are called actuators. Actuators are the parts of a system that can move and be controlled. An electric motor, gears, railroads, etc. can all function as actuators.

Effectors: These are the gadgets that have an impact on the environment. Legs, wheels, arms, fingers, wings, fins, and display screens are examples of effectors.

Intelligent Agents

An intelligent agent is an independent being that uses sensors and actuators to interact with its environment to accomplish objectives. An intelligent agent could use environmental cues to help them accomplish their objectives. One instance of an intelligent agent is a thermostat.

The primary four guidelines for an AI agent are as follows:

• Rule 1: An AI agent needs to be able to sense its environments.
• Rule 2: Decisions must be based on observation.
• Rule 3: An action must come after a decision.
• Rule 4: An AI agent can only act sensibly. 

Rational Agents

An agent that models uncertainty, has a definite preference, and maximizes its performance measure by taking all feasible actions is said to be rational.

A sensible agent is said to act morally. In artificial intelligence (AI), rational agents are created for use in game theory and decision theory in a variety of real-world contexts.

The logical course of action is crucial for an AI agent since, according to the AI reinforcement learning algorithm, an agent receives a positive reward for every optimal action and a negative reward for every incorrect action.

Rationality

An agent’s performance metric indicates how rational it is. To determine rationality, the following standards can be applied:

• A performance metric that establishes the success standard.
• Preexisting environmental knowledge of the agent.
• The best conceivable action an agent can take.
• The perceptions are in order. 

Structure of Agent

Creating an agent program that carries out the agent function is AI’s task. An intelligent agent’s architecture and agent program combine to form its structure. It is observable as:

Agent = Architecture + Agent program  

The three key terms that make up an AI agent’s structure are as follows:

Architecture: Architecture is the gear that an artificial intelligence agent operates.

Agent Function: An action can be mapped to a perception using the agent function. 

f:P* → A  

Agent Program: An application of the agent function is called an agent program. To generate function f, an agent program runs on the physical architecture.

PEAS Representation

An AI agent works with models of the PEAS kind. Once an AI agent or rational agent has been defined, its properties can be grouped under the PEAS representation model. There are four words in it: 

• P: Performance measure
• E: Environment
• A: Actuators
• S: Sensors

Types of Agents

Based on how much intelligence and capability they are thought to possess, agents can be divided into five groups. With time, all of these agents will be able to perform better and produce greater results. 

These are listed below:

Simple Reflex Agent

The most basic agents are the Simple reflex agents. These agents disregard the remainder of the percept history and base their decisions just on their current perceptions.

• These agents are only effective in environments that are completely observable.
• The Simple Reflex Agent makes decisions and takes actions without taking into account any aspect of the perception’s past.
• By mapping the current state to an action, the Simple Reflex Agent operates according to the Condition-Action Rule.

Model-based Reflex Agent

The model-based agent is capable of tracking the situation and operating in a partially visible environment. A model-based agent possesses two crucial components:

• Model: Known as “how things happen in the world” information, this type of agent is referred to as model-based.
• Internal State: Drawing from perceptual history, it is a depiction of the present state. 

These agents behave under the model, “which is knowledge of the world,” and possess the model. Information about the following is needed to update the agent state: 

• How the world changes
• The world’s reaction to the agent’s action. 

Goal-based Agent

It’s not always possible for an agent to decide what to do based only on information about the environment in its current condition.

• The agent must be aware of its objective, which denotes favorable circumstances.
• With the “goal” information at their disposal, goal-based agents enhance the capabilities of model-based agents.
• They decide to accomplish the objective.
• These agents might have to weigh a lengthy list of potential courses of action before determining whether or not the objective is accomplished. 

These kinds of scenario-based thinking are referred to as planning and searching, and they are what makes an agent proactive. 

Unity-based Agent

These agents are comparable to goal-based agents, but they differ by offering a measure of success in a specific stage and an additional utility measurement component.

• Utility-based agents behave according to both objectives and the most effective path to accomplish them.
• The utility-based agent comes in handy when an agent needs to make a decision on which course of action to take out of several options.
• To determine how well each action accomplishes the objectives, the utility function converts each state into a real value. 

Learning Agent

In artificial intelligence, a learning agent is an agent that possesses the ability to learn from its past experiences. It begins with rudimentary information and gains the ability to act and adapt on its own through learning.

The four primary conceptual components of a learning agent are as follows:

• Learning element: It is in charge of advancing by picking up knowledge from the environment.
• Critic: The learning element receives criticism that indicates how well the agent is performing in relation to a predetermined performance criterion.
• Performance element: It is in charge of deciding which outside action to take.
• Problem generator: This part is in charge of making recommendations for activities that will result in novel and educational encounters. 

As a result, learning agents are capable of learning, evaluating performance, and identifying fresh approaches to raise performance. 

Environments

Certain programs function only in virtual environments, limited to keyboard input, databases, computer file systems, and screen output of characters.

Properties of Environment

Environments possess multiple attributes:

Discrete vs. Continuous: An environment is discrete if there are only a few distinct, well-defined states in it; otherwise, it is continuous.

Observable/partially observable: An observation is made if the entire condition of the environment can be ascertained from the perceptions at each time point; it can be observed.

Static / Dynamic: An environment is considered static if it remains unchanged while an agent is operating; in other words, it is dynamic.

One agent or several agents: Other agents in the environment could be the same kind or distinct from the agent.

Accessible / inaccessible: An agent can access the environment if its sensory apparatus can perceive the entire state of the environment.

Deterministic / non-deterministic: The environment is deterministic if the agent’s actions and the present state fully dictate the environment’s upcoming state; otherwise, it is non-deterministic.

Episode-based / non-episodic: In an episodic environment, the agent perceives and acts in each episode. Its action’s quality is solely dependent on the particular episode. The events of one episode do not affect the activities of subsequent episodes. Because the agent doesn’t have to plan ahead, episodic situations are considerably easier to understand.

Conclusion

Artificial intelligence has a bright future ahead of it and will be highly intelligent. We hope you find this artificial intelligence tutorial useful. Learn how to be an expert with our artificial intelligence training in Chennai.