Chapter 1-Intro to Artificial Intelligence CoSc-4142 2022

Chapter 1 – Introduction to Artificial Intelligence

Since the invention of computers or machines, their capability to perform various tasks went on
growing exponentially. Humans have developed the power of computer systems in terms of
their diverse working domains, their increasing speed, and reducing size with respect to time. A
branch of Computer Science named Artificial Intelligence pursues creating the computers or
machines as intelligent as human beings.
1. What is Artificial Intelligence?
According to the father of Artificial Intelligence, John McCarthy, it is “The science and
engineering of making intelligent machines, especially intelligent computer programs”.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software

think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and
work while trying to solve a problem, and then using the outcomes of this study as a basis of
developing intelligent software and systems.

1.1 Goals of Artificial Intelligence

 To Create Expert Systems − the systems which exhibit intelligent behavior, learn,
demonstrate, explain, and advice its users.

 To Implement Human Intelligence in Machines − Creating systems that understand, think,

learn, and behave like humans.

1.2 What is Artificial Intelligence Technique?

In the real world, the knowledge has some unwelcomed properties −
 Its volume is huge, next to unimaginable.
 It is not well-organized or well-formatted.
 It keeps changing constantly.
AI Technique is a manner to organize and use the knowledge efficiently in such a way that −
 It should be perceivable by the people who provide it.
 It should be easily modifiable to correct errors.
 It should be useful in many situations though it is incomplete or inaccurate.
AI techniques elevate the speed of execution of the complex program it is equipped with.

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1.3 Programming with and without Artificial Intelligence

Programming without AI Programming with AI

A computer program without AI can answer A computer program with AI can answer
the specific questions it is meant to solve. the generic questions it is meant to solve.

AI programs can absorb new modifications by putting

Modification in the program leads to change highly independent pieces of information together.
in its structure. Hence you can modify even a minute piece of
information of program without affecting its structure.

Modification is not quick and easy. It may

Quick and Easy program modification.
lead to affecting the program adversely.

1.4 Applications of Artificial Intelligence

AI has been dominant in various fields such as −

 Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc.,
where machine can think of large number of possible positions based on heuristic
knowledge.

 Natural Language Processing − It is possible to interact with the computer that

understands natural language spoken by humans.

 Expert Systems − there are some applications which integrate machine, software, and
special information to impart reasoning and advising. They provide explanation and advice to
the users.

 Vision Systems − these systems understand, interpret, and comprehend visual input on the
computer. For example,

 A spying aeroplane takes photographs, which are used to figure out spatial information
or map of the areas.

 Doctors use clinical expert system to diagnose the patient.

 Police use computer software that can recognize the face of criminal with the stored
portrait made by forensic artist.

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 Speech Recognition − Some intelligent systems are capable of hearing and

comprehending the language in terms of sentences and their meanings while a human talks
to it. It can handle different accents, slang words, noise in the background, change in
human’s noise due to cold, etc.

 Handwriting Recognition − the handwriting recognition software reads the text written on
paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and
convert it into editable text.

 Intelligent Robots − Robots are able to perform the tasks given by a human. They have
sensors to detect physical data from the real world such as light, heat, temperature,
movement, sound, bump, and pressure. They have efficient processors, multiple sensors
and huge memory, to exhibit intelligence. In addition, they are capable of learning from their
mistakes and they can adapt to the new environment.

1.5 Objectives of Artificial Intelligence

Artificial Intelligence is a science and engineering to develop intelligent machines that works like
human to do tasks such as speech recognition, decision-making, reasoning, learning, problem
solving and translation between languages.

In simple words, it is developed to make human life easy in different aspects and perform
enormous works with more accuracy. It has become an intelligent part of today’s industry which
is crucial as data is growing Big. It can perform tasks such as identifying patterns in data more
effectively than humans thus making business more profitable. Knowledge Engineering
and Machine learning is a core part of AI. Some of the objectives of AI are:

 Know the difficulties that arise from attempting to define AI.

 Know the three areas of research of AI, and give examples of problems from each area.
 Understand in a general way how a neural network is designed and trained.
 Know the components of a formal system.
 Understand how depth first, breadth first, and bi-directional searches are performed.
 Use evaluation functions to expedite the search process.

1.6 Approaches of Artificial Intelligence

Following are the approaches to the goal of AI:

 Computer systems that act like humans,

 Programs that simulate the human mind,
 Knowledge representation and mechanistic reasoning, and

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 Intelligent or rational agent design.

The first two approaches focus on studying humans and how they solve problems, while the
latter two approaches focus on studying real-world problems and developing rational solutions
regardless of how a human would solve the same problems.

Programming a computer to act like a human is a difficult task and requires that the computer
system be able to understand and process commands in natural language, store knowledge,
retrieve and process that knowledge in order to derive conclusions and make decisions, learn to
adapt to new situations, perceive objects through computer vision, and have robotic capabilities
to move and manipulate objects. Although this approach was inspired by the Turing Test, most
programs have been developed with the goal of enabling computers to interact with humans in a
natural way rather than passing the Turing Test.

The Turing Test is used as a theoretical standard to determine whether a human judge can
distinguish via a conversation with one machine and one human which a human is and which is
a machine. If a machine can trick the human judge into thinking it is human then it passes the
Turing Test

1.7 History of Artificial Intelligence

The concept of AI began around 1943 and became a field of study in 1956 at Dartmouth. AI is
not limited to the Computer Sciences disciplines, but can be seen in countless disciplines such
as Mathematics, Philosophy, Economics, Neuroscience, psychology and various other areas.
The areas of interest in the Computer Science and Engineering field are focused on how we can
build more efficient computers. Great advancements have been made in the area of hardware
and software. Here is the history of AI during 20th century:

Year Milestone / Innovation

KarelČapek play named “Rossum's Universal Robots” (RUR) opens in London, first use
1923
of the word "robot" in English.

1943 Foundations for neural networks laid.

1945 Isaac Asimov, a Columbia University alumni, coined the term Robotics.

Alan Turing introduced Turing Test for evaluation of intelligence and

1950 published Computing Machinery and Intelligence. Claude Shannon published Detailed
Analysis of Chess Playing as a search.

1956 John McCarthy coined the term Artificial Intelligence. Demonstration of the first running

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AI program at Carnegie Mellon University.

1958 John McCarthy invents LISP programming language for AI.

Danny Bobrow's dissertation at MIT showed that computers can understand natural
1964 language well enough to solve algebra word problems correctly.

Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a

1965 dialogue in English.

Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with

1969 locomotion, perception, and problem solving.

The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish
1973 Robot, capable of using vision to locate and assemble models.

1979 The first computer-controlled autonomous vehicle, Stanford Cart, was built.

1985 Harold Cohen created and demonstrated the drawing program, Aaron.

Major advances in all areas of AI −

 Significant demonstrations in machine learning

 Case-based reasoning
 Multi-agent planning
1990  Scheduling
 Data mining, Web Crawler
 natural language understanding and translation
 Vision, Virtual Reality
 Games

1997 The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.

Interactive robot pets become commercially available. MIT displays Kismet, a robot with
2000 a face that expresses emotions. The robot Nomad explores remote regions of Antarctica
and locates meteorites.

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Chapter 2 – Introduction to Intelligent Agents

2.1 An agent is anything that can perceive its environment through sensors and acts upon that
environment through effectors.
 A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel
to the sensors, and other organs such as hands, legs, mouth, for effectors.
 A robotic agent replaces cameras and infrared range finders for the sensors, and
various motors and actuators for effectors.
 A software agent has encoded bit strings as its programs and actions.

An intelligent agent is an AI hardware and/or software system with some degree of autonomy
and the capacity to make decisions and take actions. Intelligent agents are more advanced than
conventional agents whose actions are completely programmed. An agent program might, for
example, use human-defined parameters to search a knowledge base or the Internet and
organize that information for presentation to the user.
Intelligent agents, at their most complexes, include physical systems, such as AI- equipped
android (humanoid) robots. Although it has not yet been demonstrated, some believe that a
future system could not only look like a human but could also replicate human cognitive powers
in artificial general intelligence (AGI) or even overwhelmingly surpass it in artificial super
intelligence (ASI). At the lower end of the scale lie more task-specific software programs, such
as expert systems that work similarly to conventional agent programs but nevertheless some
degree of autonomy and physical agents that take in sensor data and act on it.
2.2 Agent Terminology
 Performance Measure of Agent − It is the criteria, which determines how successful an
agent is.
 Behavior of Agent − It is the action that agent performs after any given sequence of
percepts.
 Percept − It is agent’s perceptual inputs at a given instance.
 Percept Sequence − It is the history of all that an agent has perceived till date.
 Agent Function − It is a map from the precept sequence to an action.

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Agent Rationality
Rationality is nothing but status of being reasonable, sensible, and having good sense of
judgment.
Rationality is concerned with expected actions and results depending upon what the agent has
perceived. Performing actions with the aim of obtaining useful information is an important part
of rationality.

Ideal Rational Agent

An ideal rational agent is the one, which is capable of doing expected actions to maximize its

performance measure, on the basis of:

 Its percept sequence

 Its built-in knowledge base

Rationality of an agent depends on the following:

 The performance measures, which determine the degree of success.

 Agent’s Percept Sequence till now.

 The agent’s prior knowledge about the environment.

 The actions that the agent can carry out.

A rational agent always performs right action, where the right action means the action that
causes the agent to be most successful in the given percept sequence. The problem the agent
solves is characterized by Performance Measure, Environment, Actuators, and Sensors
(PEAS).
2.3 Structure of Intelligent Agents
Agent’s structure can be viewed as:
 Agent = Architecture + Agent Program

 Architecture = the machinery that an agent executes on.

 Agent Program = an implementation of an agent function.

Types of Agents
Agents can be grouped into four classes based on their degree of perceived intelligence and
capability:

 Simple Reflex Agents

 Model-Based Reflex Agents
 Goal-Based Agents

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 Utility-Based Agents
Simple Reflex Agents
Simple reflex agents ignore the rest of the percept history and act only on the basis of
the current percept. Percept history is the history of all that an agent has perceived till date.
The agent function is based on the condition-action rule. A condition-action rule is a rule that
maps a state i.e, condition to an action. If the condition is true, then the action is taken, else not.
This agent function only succeeds when the environment is fully observable. For simple reflex
agents operating in partially observable environments, infinite loops are often unavoidable. It
may be possible to escape from infinite loops if the agent can randomize its actions. Problems
with Simple reflex agents are:

 They choose actions only based on the current percept.

 They are rational only if a correct decision is made only on the basis of current precept.
 Their environment is completely observable.
 Very limited intelligence.
 No knowledge of non-perceptual parts of state.
 Usually too big to generate and store.
 If there occurs any change in the environment, then the collections of rules need to be
updated.

Model Based Reflex Agent

It works by finding a rule whose condition matches the current situation. A model-based agent
can handle partially observable environments by use of model about the world. The agent
has to keep track of internal state which is adjusted by each percept and that depends on the
percept history. The current state is stored inside the agent which maintains some kind of
structure describing the part of the world which cannot be seen. Updating the state requires the
information about:
 how the world evolves in-dependently from the agent, and
 how the agent actions affects the world.

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Goal Based Agent

These kinds of agents take decision based on how far they are currently from their goal
(description of desirable situations). Their every action is intended to reduce its distance from
goal. This allows the agent a way to choose among multiple possibilities, selecting the one
which reaches a goal state. The knowledge that supports its decisions is represented explicitly
and can be modified, which makes these agents more flexible. They usually require search and
planning. The goal based agent’s behavior can easily be changed.

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Utility Based Agents

The agents which are developed having their end uses as building blocks are called utility
based agents. When there are multiple possible alternatives, then to decide which one is best,
utility based agents are used. They choose actions based on a preference (utility) for each
state. Sometimes achieving the desired goal is not enough. We may look for quicker, safer,
cheaper trip to reach a destination. Agent happiness should be taken into consideration. Utility
describes how “happy” the agent is. Because of the uncertainty in the world, a utility agent
chooses the action that maximizes the expected utility. A utility function maps a state onto a real
number which describes the associated degree of happiness.

Goals are inadequate when:

 There are conflicting goals, out of which only few can be achieved.

 Goals have some uncertainty of being achieved and you need to weigh likelihood of
success against the importance of a goal.

2.4 Nature of Environment

Some programs operate in the entirely artificial environment confined to keyboard input,
database, computer file systems and character output on a screen.
In contrast, some software agents (software robots or softbots) exist in rich, unlimited softbots
domains. The simulator has a very detailed, complex environment. The software agent
needs to choose from a long array of actions in real time. A softbot designed to scan the online
preferences of the customer and show interesting items to the customer works in the real as
well as an artificial environment.

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The most famous artificial environment is the Turing Test environment, in which one real
and other artificial agent are tested on equal ground. This is a very challenging environment as
it is highly difficult for a software agent to perform as well as a human.

Turing Test
The success of an intelligent behavior of a system can be measured with Turing Test. Two
persons and a machine to be evaluated participate in the test. Out of the two persons, one
plays the role of the tester. Each of them sits in different rooms. The tester is unaware of who is
machine and who is a human. He interrogates the questions by typing and sending them to
both intelligences, to which he receives typed responses.
This test aims at fooling the tester. If the tester fails to determine machine’s response from the
human response, then the machine is said to be intelligent.

Properties of Environment
The environment has multifold properties:

 Discrete / Continuous − If there are a limited number of distinct, clearly defined, states
of the environment, the environment is discrete (For example, chess); otherwise it is
continuous (For example, driving).

 Observable / Partially Observable − If it is possible to determine the complete state of

the environment at each time point from the percepts it is observable; otherwise it is
only partially observable.

 Static / Dynamic − If the environment does not change while an agent is acting, then it
is static; otherwise it is dynamic.

 Single agent / Multiple agents − The environment may contain other agents which
may be of the same or different kind as that of the agent.

 Accessible / Inaccessible − If the agent’s sensory apparatus can have access to the
complete state of the environment, then the environment is accessible to that agent.

 Deterministic / Non-deterministic − 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; otherwise it is non-deterministic.

 Episodic / Non-episodic − in an episodic environment, each episode consists of the

agent perceiving and then acting. The quality of its action depends just on the episode
itself. Subsequent episodes do not depend on the actions in the previous episodes.
Episodic environments are much simpler because the agent does not need to think
ahead.

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Assignment-1

1. What is “Intelligence” in Artificially Intelligent Machines?

2. What is the main difference between Speech and Voice Recognition Technology
3. What is an Agent and describe characteristics of a good agent?
4. What is a Rational Agent? Compare Rationality vs Omniscience, Rationality vs
Clairvoyance, Rationality vs Successful and Rationality vs Autonomy
5. Write a PEAS description of the task environment for the following agents:
a) Medical Diagnosing System
b) Interactive Course Tutor System
c) Internet Shopping Agent
6. Compare the task environment for the above agents as well
7. Is there a difference between an Agent and A Program?
8. List and explain in depth the various types of Agents.
9. What is a Learning Agent?
10. Compare programming with AI and without AI.

 To be submitted after two weeks starting from our last time encounter/session.
 Don’t copy from one another because I hate those guys.
 At the end everyone’s work will be collected and presented in group of five
peoples according to your alphabets.

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