Introduction to Artificial Intelligence Tutorial

 

What is Artificial Intelligence?

The term Artificial Intelligence comprises of two words ‘Artificial’ and ‘Intelligence’, where, Artificial means ‘copy of something natural’ and ‘Intelligence’ means ‘able to think.’

So, Artificial Intelligence can be defined as a copy of a human brain with thinking ability.

According to John McCarthy, who is known as the father of AI,

“AI is the science and engineering of making intelligent machines, especially computer programs.”

The objective of AI is to explore the ways onto a machine that can reason like a human, think like a human and act like a human. Its approach is to train a machine (i.e., a computer or a robot) with the same capabilities as of a human brain. In the future, AI will prove itself as an excellent helping hand.

Need for Artificial Intelligence

Consider a scenario where a human brain may fail to take an intelligent decision and need someone who can make a wise and intelligent decision for him. In such a situation, we can understand the need for AI.

Therefore, AI has groomed the world with its exploring intelligent power, and the following points will make us understand the need for AI more effectively:

• AI can be used to make an intelligent decision as compared to human beings.
• AI can be used to analyze data more deeply.
• To maintain security and privacy of data more effectively and efficiently.
• To create expert systems having the capability to explain and give advice to users.
• AI can also be used to speed up the work performance.

How Artificial Intelligence came into existence?

Earlier Greeks used to discuss Artificial Intelligence in rumors or stories. As a result, In the 1940s and 50s, a group of classical philosophers and mathematicians decided to convert the myth of Artificial Intelligence into reality.

Turing Machine and Turing Test

1936: Alan Turing created a Turing machine which formalized the concept of algorithm and computation. Turing machine was highly influential in the development of theoretical computer science.

1950: Alan Turing published a seminal paper on “Computing Machinery Intelligence” in which he described the “Turing Test” to determine whether a machine is intelligent or not.

The term AI was coined

1956: Several scientists attended the Dartmouth Summer Conference at New Hampshire.  During the conference, it was claimed that “every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it” and finally it was named “A.I.”

First Chatbot

1966:  Joseph Weizenbaum, a German-American computer scientist, invented ‘ELIZA’, which is a computer program that communicates with humans.

AI in the medical field.

1972: Ted Shortliffe developed an expert system named ‘MYCIN’ which is used for the treatment of illnesses.

Voice of the Computer: NETtalk

1986: Terrence J. Sejnowski and Charles Rosenberg developed an artificial neural network,’ NETtalk.’ It was able to read words and pronounce them correctly and could apply what it learned for understanding more new words.

Victory over champions

1997: Deep Blue from IBM became the first computerized chess-playing system to defeat the world chess champion, Garry Kasparov.

2005: A robot from Stanford University won the DARPA Challenge. It drove autonomously for 131 miles across an unrehearsed desert trail.

2007: A team from CMU won the DARPA Urban Challenge by autonomously navigating 55 miles in an urban environment by following all traffic laws.

2011: IBM’s question answering system, Watson, defeated the two greatest Jeopardy Champions, Brad Rutter and Ken Jennings in a Jeopardy! Quiz exhibition match.

AI Today

Now a days, faster computers and advanced machine learning techniques have been introduced to access a large amount of data. It has resolved many economical and financial problems. Currently, experts are working on Deep Learning, Big Data, Machine learning, and several other techniques and taking the world to a highly advanced level.

Artificial Intelligence: The Superset.

AI provides ways to make machines intelligent. AI uses algorithms and expert systems to make the artificial brain. ML is the subset of artificial intelligence because ML makes AI algorithms more advance so that machines may automatically improve through experiences without manual intervention. Thus, Machine learning is an application/component of Artificial Intelligence.

Components of Artificial Intelligence

• Reasoning, problem-solving: Researchers had developed machines with algorithms that enable machines to solve puzzles or quiz similar to humans. AI can also deal with uncertain or incomplete information through advanced algorithms.
• Knowledge Representation: It is the representation of all the knowledge which is stored by an agent to make an expert system. Knowledge can be a set of objects, relations, concepts, or properties.
• Planning: Intelligent agents should be able to set goals and make plans to achieve those goals. They should be able to visualize the future and make predictions about their actions taken for achieving the goal.
• Learning: It is the study of the computer algorithms which improve automatically through experiences. This concept is known as Machine Learning.
• Natural Language Processing: This processing enables a machine to read and understand human language by processing the human language into machine language.
• Perception: An ability of the machine to use input from sensors, microphones, wireless signals, etc. for understanding different aspects of the world.

Types of Artificial Intelligence

Classification of AI can be done in several ways:

• Weak AI: It is also known as narrow AI, which is designed to perform a specific task. It acts like it can ‘think’.
• Strong AI: It is also known as artificial general intelligence which has generalized human cognitive abilities. It is intelligent enough to find a solution.

Arend Hintze, an assistant professor of integrative biology and computer science, classified Artificial Intelligence into four types:

• Reactive Machines: These machines are designed for small purposes, but it has no memory and cannot use past experience for future decision. An example of a reactive machine is Deep Blue from IBM.
• Limited Memory: This system uses past experience for future decisions, for example, autonomous vehicles.
• Theory of Mind: This is a psychological term which refers to the understanding that “Every mindset is different, so is the decision.”At present, this type does not exist.
• Self-awareness: In this type, machines have self-awareness ability to understand their current state and can predict what others feel. Currently, this type of AI does not exist.

Recent Tools and Technologies

• Speech Recognition: It recognizes the human voice and transforms into the format, which can be understood by different computer applications.
• Natural Language Generation: It is a tool which produces text from the computer data.
• Virtual Agent: The agent serves as an online customer service representative. It behaves intelligently with the customer and responses well.
• Machine learning: ML provides a platform to develop algorithms and APIs for the improvement of the machine and to make machines self-supervised.
• Bio-metrics: It is used for identifying access management and access control. It is also used to identify the person under surveillance.

Applications of Artificial Intelligence

AI in Business

• AI has become a supporting tool for the growth of the business.
• AI helps in determining the consequences of each action for decision making.
• AI can also make decisions on its own and can act in situations not foreseen by the person.
• Machine learning algorithms are integrated with CRM (Customer Relationship Management) to provide better services to customers.
• Chatbots used by e-companies provide immediate responses to the customers.

AI in Healthcare

• Hospitals use ML algorithms for better and fast diagnosis than humans. IBM’s Watson (a question-answering system) used to form a hypothesis from the patient’s data.
• AI can assist both the patients and doctors well.
• Autonomous robots help surgeons in surgery.
• It helps doctors for the right treatments of Cancer.
• It provides a way to try and monitor multiple high risks patients by interrogating them.
• It provides a laboratory for examination and representation of medical information.

AI in Education

• It provides a platform for the students to learn and grab things quickly.
• It automates grading systems that help staff to monitor marks easily.
• AI saves much time of students and teachers.

AI for Robotics

With the help of AI, it becomes easy to take care of the aging population and can see a drastic reduction in the death rate of people.

AI in Autonomous Vehicles

• AI has automated the systems of cars and other vehicles.
• AI has provided sensors to understand the world around them and learn from the environment

AI in Agriculture

• AI showed improvements in gaining yield and increased research and development of growing crops.
• Crop and soil monitoring has become easy.
• AI has made farming easier for farmers to know when the fruit or vegetable be ready to ripe.

Advantages of Artificial Intelligence

• Chances of the error have approximately become negligible and achieved higher accuracy.
• Intelligent robots have explored the world.
• AI has become a helping hand for humans in laborious work.
• AI also helps in making the best decision.
• AI has made fraud detection on smart card possible.
• Provide in-depth analyses of data.

Disadvantages of Artificial Intelligence

• It can cost lot of money to build, rebuild or repair machines.
• Robots can replace humans and take off their jobs.
• AI can cause unemployment.
• If given in wrong hands, machines may lead to destruction.
• AI will make human dependent on it which lead to rust on their brains.

Artificial Intelligence Topics

Artificial Intelligence Introduction

• Introduction to Artificial Intelligence
• Intelligent Agents

Search Algorithms

• Problem-solving
• Uninformed Search
• Informed Search
• Heuristic Functions
• Local Search Algorithms and Optimization Problems
• Hill Climbing search
• Differences in Artificial Intelligence
• Adversarial Search in Artificial Intelligence     
• Minimax Strategy
• Alpha-beta Pruning
• Constraint Satisfaction Problems in Artificial Intelligence
• Cryptarithmetic Problem in Artificial Intelligence

Knowledge, Reasoning and Planning

• Knowledge based agents in AI
• Knowledge Representation in AI
• The Wumpus world
• Propositional Logic
• Inference Rules in Propositional Logic
• Theory of First Order Logic
• Inference in First Order Logic
• Resolution method in AI
• Forward Chaining
• Backward Chaining
• Classical Planning

Uncertain Knowledge and Reasoning

• Quantifying Uncertainty
• Probabilistic Reasoning
• Dynamic Bayesian Networks
• Utility Functions in Artificial Intelligence

intelligent agent in artificial intelligence

 

What is an Agent?

An agent can be viewed as anything that perceives its environment through sensors and acts upon that environment through actuators.

For example, human being perceives their surroundings through their sensory organs known as sensors and take actions using their hands, legs, etc., known as actuators.

Diagrammatic Representation of an Agent

Agents interact with the environment through sensors and actuators

Intelligent Agent

An intelligent agent is a goal-directed agent. It perceives its environment through its sensors using the observations and built-in knowledge, acts upon the environment through its actuators.

Rational Agent

A rational agent is an agent which takes the right action for every perception. By doing so, it maximizes the performance measure, which makes an agent be the most successful.

Note: There is a slight difference between a rational agent and an intelligent agent.

Omniscient Agent

An omniscient agent is an agent which knows the actual outcome of its action in advance. However, such agents are impossible in the real world.

Note: Rational agents are different from Omniscient agents because a rational agent tries to get the best possible outcome with the current perception, which leads to imperfection. A chess AI can be a good example of a rational agent because, with the current action, it is not possible to foresee every possible outcome whereas a tic-tac-toe AI is omniscient as it always knows the outcome in advance.

Software Agents

It is a software program which works in a dynamic environment. These agents are also known as Softbots because all body parts of software agents are software only. For example, video games, flight simulator, etc.

Behavior of an Agent

Mathematically, an agent behavior can be described by an:

• Agent Function: It is the mapping of a given percept sequence to an action. It is an abstract mathematical explanation.
• Agent Program: It is the practical and physical implementation of the agent function.

For example, an automatic hand-dryer detects signals (hands) through its sensors. When we bring hands nearby the dryer, it turns on the heating circuit and blows air. When the signal detection disappears, it breaks the heating circuit and stops blowing air.

Rationality of an agent

It is expected from an intelligent agent to act in a way that maximizes its performance measure. Therefore, the rationality of an agent depends on four things:

• The performance measure which defines the criterion of success.
• The agent’s built-in knowledge about the environment.
• The actions that the agent can perform.
• The agent’s percept sequence until now.

For example: score in exams depends on the question paper as well as our knowledge.

Note: Rationality maximizes the expected performance, while perfection maximizes the actual performance which leads to omniscience.

Task Environment

A task environment is a problem to which a rational agent is designed as a solution. Consequently, in 2003, Russell and Norvig introduced several ways to classify task environments. However, before classifying the environments, we should be aware of the following terms:

• Performance Measure: It specifies the agent’s sequence of steps taken to achieve its target by measuring different factors.
• Environment: It specifies the interaction of the agent with different types of environment.
• Actuators: It specifies the way the agent affects the environment by taking expected actions.
• Sensors: It specifies the way the agent gets information from its environment.

These terms acronymically called as PEAS (Performance measure, Environment, Actuators, Sensors). To understand PEAS terminology in more detail, let’s discuss each element in the following example:

Agent Type	Performance	Environment	Actuators	Sensors
Taxi Driver	Safe, fast, correct destination	Roads, traffic	Steering, horn, breaks	Cameras, GPS, speedometer
PEAS summary for an automated taxi driver

Properties/Classification of Task Environment

Fully Observable vs. Partially Observable:

When an agent’s sensors allow access to complete state of the environment at each point of time, then the task environment is fully observable, whereas, if the agent does not have complete and relevant information of the environment, then the task environment is partially observable.

Example: In the Checker Game, the agent observes the environment completely while in Poker Game, the agent partially observes the environment because it cannot see the cards of the other agent

Note: Fully Observable task environments are convenient as there is no need to maintain the internal state to keep track of the world.

Single-agent vs. Multiagent

When a single agent works to achieve a goal, it is known as Single-agent, whereas when two or more agents work together to achieve a goal, they are known as Multiagents.

Example: Playing a crossword puzzle – single agent

Playing chess –multiagent (requires two agents)

Deterministic vs. Stochastic

If the agent’s current state and action completely determine the next state of the environment, then the environment is deterministic whereas if the next state cannot be determined from the current state and action, then the environment is Stochastic.

Example: Image analysis – Deterministic

Taxi driving – Stochastic (cannot determine the traffic behavior)

Note: If the environment is partially observable, it may appear as Stochastic

Episodic vs. Sequential

If the agent’s episodes are divided into atomic episodes and the next episode does not depend on the previous state actions, then the environment is episodic, whereas, if current actions may affect the future decision, such environment is sequential.

Example:  Part-picking robot – Episodic

Chess playing – Sequential

Static vs. Dynamic

If the environment changes with time, such an environment is dynamic; otherwise, the environment is static.

Example: Crosswords Puzzles have a static environment while the Physical world has a dynamic environment.

Discrete vs. Continuous

If an agent has the finite number of actions and states, then the environment is discrete otherwise continuous.

Example: In Checkers game, there is a finite number of moves – Discrete

A truck can have infinite moves while reaching its destination –           Continuous.

Known vs. Unknown

In a known environment, the agents know the outcomes of its actions, but in an unknown environment, the agent needs to learn from the environment in order to make good decisions.

Example: A tennis player knows the rules and outcomes of its actions while a player needs to learn the rules of a new video game.

Note: A known environment is partially observable, but an unknown environment is fully observable.

Structure of agents

The goal of artificial intelligence is to design an agent program which implements an agent function i.e., mapping from percepts into actions. A program requires some computer devices with physical sensors and actuators for execution, which is known as architecture.

Therefore, an agent is the combination of the architecture and the program i.e.

agent = architecture + program

Note: The difference between the agent program and agent function is that an agent program takes the current percept as input, whereas an agent function takes the entire percept history.

Types of Agent Programs

Varying in the level of intelligence and complexity of the task, the following four types of agents are there:

• Simple reflex agents: It is the simplest agent which acts according to the current percept only, pays no attention to the rest of the percept history. The agent function of this type relies on the condition-action rule – “If condition, then action.” It makes correct decisions only if the environment is fully observable. These agents cannot ignore infinite loop when the environment is partially observable but can escape from infinite loops if the agents randomize its actions.

Example: iDraw, a drawing robot which converts the typed characters into

writing without storing the past data.

Note: Simple reflex agents do not maintain the internal state and do not depend on the percept theory.

• Model-based agent: These type of agents can handle partially observable environments by maintaining some internal states. The internal state depends on the percept history, which reflects at least some of the unobserved aspects of the current state. Therefore, as time passes, the internal state needs to be updated which requires two types of knowledge or information to be encoded in an agent program i.e., the evolution of the world on its own and the effects of the agent’s actions.

Example: When a person walks in a lane, he maps the pathway in his mind.

• Goal-based agents: It is not sufficient to have the current state information unless the goal is not decided. Therefore, a goal-based agent selects a way among multiple possibilities that helps it to reach its goal.

Note: With the help of searching and planning (subfields of AI), it becomes easy for the Goal-based agent to reach its destination.

• Utility-based agents: These types of agents are concerned about the performance measure. The agent selects those actions which maximize the performance measure and devote towards the goal.

Example: The main goal of chess playing is to ‘check-and-mate’ the king, but the player completes several small goals previously.

Note: Utility-based agents keep track of its environment, and before reaching its main goal, it completes several tiny goals that may come in between the path.

• Learning agents: The main task of these agents is to teach the agent machines to operate in an unknown environment and gain as much knowledge as they can. A learning agent is divided into four conceptual components:
  • Learning element: This element is responsible for making improvements.
  • Performance element: It is responsible for selecting external actions according to the percepts it takes.
  • Critic: It provides feedback to the learning agent about how well the agent is doing, which could maximize the performance measure in the future.
  • Problem Generator: It suggests actions which could lead to new and informative experiences.

Example: Humans learn to speak only after taking birth.

Note: The objective of a Learning agent is to improve the overall performance of the agent.

 

Working of an agent program’s components

The function of agent components is to answer some basic questions like “What is the world like now?”, “what do my actions do?” etc.

We can represent the environment inherited by the agent in various ways by distinguishing on an axis of increasing expressive power and complexity as discussed below:

• Atomic Representation: Here, we cannot divide each state of the world. So, it does not have any internal structure. Search, and game-playing, Hidden Markov Models, and Markov decision process all work with the atomic representation.
• Factored Representation: Here, each state is split into a fixed set of attributes or variables having a value. It allows us to represent uncertainty. Constraint satisfaction, propositional logic, Bayesian networks, and machine learning algorithms work with the Factored representation.

Note: Two different factored states can share some variables like current GPS location, but two different atomic states cannot do so.

• Structured Representation: Here, we can explicitly describe various and varying relationships between different objects which exist in the world. Relational databases and first-order logic, first-order probability models, natural language understanding underlie structured representation.