CONCEPT OF AI:

In today's world, technology is growing very fast,

and we are getting in touch with different new
technologies day by day.
• Here, one of the booming technologies of
computer science is Artificial Intelligence which
is ready to create a new revolution in the world by
making intelligent machines. The Artificial
Intelligence is now all around us. It is currently
working with a variety of subfields, ranging from
general to specific, such as self-driving cars,
playing chess, proving theorems, playing music,
Painting, etc

Artificial Intelligence is composed of two words

Artificial and Intelligence, where Artificial defines
"man-made," and intelligence defines "thinking
power" , hence AI means "a man-made thinking
power."
• So, we can define AI as: • "It is a branch of
computer science by which we can create
intelligent machines which can behave like a
human, think like humans, and able to make
decisions."Artificial Intelligence exists when a
machine can have human based skills such as
learning, reasoning, and solving problems.

IMPORTANCE OF AI:
Why Artificial Intelligence?
• Before Learning about Artificial Intelligence, we
should know that what is the importance of AI and
why should we learn it. Following are some main
reasons to learn about AI:
• With the help of AI, you can create such
software or devices which can solve real-world
problems very easily and with accuracy such as
health issues, marketing, traffic issues, etc. •
With the help of AI, you can create your personal
virtual Assistant, such as Cortana, Google
Assistant, Siri, etc.
• With the help of AI, you can build such Robots
which can work in an environment where survival
of humans can be at risk.
• AI opens a path for other new technologies, new
devices, and new Opportunities
Advantages of Artificial Intelligence:
1. High Accuracy with less errors: AI machines or
systems are prone to less errors and high
accuracy as it takes decisions as per pre-
experience or information.

2. High-Speed: AI systems can be of very high-

speed and fast-decision making, because of that
AI systems can beat a chess champion in the
Chess game.

3. High reliability: AI machines are highly reliable

and can perform the same action multiple times
with high accuracy.

4.Useful for risky areas: AI machines can be

helpful in situations such as defusing a bomb,
exploring the ocean floor, where to employ a
human can be risky.
5.Digital Assistant: AI can be very useful to
provide digital assistant to the users such as AI
technology is currently used by various E-
commerce websites to show the products as per
customer requirement.

6. Useful as a public utility: AI can be very useful

for public utilities such as a self-driving car which
can make our journey safer and hassle-free, facial
recognition for security purpose, Natural
language processing to communicate with the
human in human-language, etc.

7. Enhanced Security: AI can be very helpful in

enhancing security, as It can detect and respond
to cyber threats in real time, helping companies
protect their data and systems.

8.Aid in Research: AI is very helpful in the

research field as it assists researchers by
processing and analyzing large datasets,
accelerating discoveries in fields such as
astronomy, genomics, and materials science.
Disadvantages of Artificial Intelligence;
1• High Cost: The hardware and software
requirement of AI is very costly as it requires lots
of maintenance to meet current world
requirements.

2• Can't think out of the box: Even we are making

smarter machines with AI, but still they cannot
work out of the box, as the robot will only do that
work for which they are trained, or programmed.

3 • No feelings and emotions: AI machines can be

an outstanding performer, but still it does not
have the feeling so it cannot make any kind of
emotional attachment with human, and may
sometime be harmful for users if the proper care
is not taken.

4• Increase dependency on machines: With the

increment of technology, people are getting more
dependent on devices and hence they are losing
their mental capabilities.

HISTORY OF AI:

The history of artificial intelligence (AI) includes

ideas from ancient times, the development of
early AI programs, and more recent advances.
Ancient ideas ;
• Myths and legends about automatons and
thinking machines date back to ancient
times
In Greek mythology, Talos was a giant made of
bronze who guarded the island of Crete .
Early AI programs ;
• In 1952, Arthur Samuel developed a program
to play checkers that could learn
independently
In the 1960s, John McCarthy developed the
first AI programming language, LISP
•

AI winters;
• AI faced challenges and slow periods called
"AI winters"
The AI winter that began in the 1970s
continued throughout much of the following two
decades
The field gained more R&D funding in the late
1990s
•

Modern AI :
• Today, AI is used in various fields, driven by
machine learning and big data
 AI creative writing assistants have their origin
in the spell checkers used by PC owners in the
early 1980s
•

Notable people :
• Alan Turing is considered the “father of AI” for
his work introducing the Turing Test in 1950
John McCarthy coined the term “artificial
intelligence” in 1955
Ada Lovelace is often celebrated as an early
pioneer for her work on Charles Babbage's
Analytical Engine .

CURRENT STATUS OF AI:

Artificial intelligence (AI) is a rapidly evolving
technology that has become a reality for many
businesses and organizations. AI has improved
efficiency, reduced errors, and extracted insights
from data.
AI in business :
. AI is used in many enterprise applications, such
as customer relationship management,
recruiting, and workforce productivity.

. AI-powered chatbots and virtual assistants can

handle customer inquiries and support tickets.

. AI has become a sought-after skill for

professionals in developed countries.
•

AI in society:
• AI has improved social wellbeing in areas like
precision medicine, environmental
sustainability, education, and public welfare.
• AI has transformed everyday lives through
voice-assisted smartphones, handwriting
recognition, and more.
AI in research :
• Industry is dominating frontier AI research.
. The training costs of state-of-the-art AI models
have reached unprecedented levels.
. More organizations are releasing open-source
foundation models, which can be freely used and
modified by anyone.
•

AI in general :
• AI has surpassed human performance on
some tasks, but trails behind on more
complex tasks.
. AI is expected to become even more powerful
in the coming decades.

SCOPE OF AI:
Exploring the Scope of Artificial Intelligence in
Depth
1. Healthcare: Diagnosis, personalized
treatments, drug discovery, robotic surgeries, and
patient care management.
2. Education: Personalized learning, virtual
tutors, and improving access to educational
resources.
3. Business & Industry: Automating workflows,
predictive analytics, customer service (chatbots),
and supply chain optimization.

4. Transportation: Autonomous vehicles, traffic

management, and route optimization.

5.Entertainment: Content recommendations,

virtual reality experiences, and creating art or
music.
6. Agriculture: Precision farming, crop
monitoring, and resource optimization.

7. Environment & Sustainability: Predicting

natural disasters, managing renewable energy,
and conservation efforts.
AGENTS:
In artificial intelligence, an agent is a computer
program or system that is designed to perceive its
environment, make decisions and take actions to
achieve a specific goal or set of goals. The agent
operates autonomously, meaning it is not directly
controlled by a human operator.
Agents can be classified into different types
based on their characteristics, such as whether
they are reactive or proactive, whether they have
a fixed or dynamic environment, and whether
they are single or multi-agent systems.

An agent can be anything that perceive its

environment through sensors and act upon that
environment through actuators. An Agent runs in
the cycle of perceiving , thinking , and acting . An
agent can be:
• Human-Agent: A human agent has eyes, ears,
and other organs which work for sensors and
hand, legs, vocal tract work for actuators.
• Robotic Agent: A robotic agent can have
cameras, infrared range finder, NLP for sensors
and various motors for actuators.
• Software Agent: Software agent can have
keystrokes, file contents as sensory input and act
on those inputs and display output on the screen.

Before moving forward, we should first

know about sensors, effectors, and
actuators:
• Sensor: Sensor is a device which detects the
change in the environment and sends the
information to other electronic devices. An agent
observes its environment through sensors.

• Actuators: Actuators are the component of

machines that converts energy into motion. The
actuators are only responsible for moving and
controlling a system. An actuator can be an
electric motor, gears, rails, etc.
• Effectors: Effectors are the devices which affect
the environment. Effectors can be legs, wheels,
arms, fingers, wings, fins, and display screen
Types of Agents
Agents can be grouped into five classes based on
their degree of perceived intelligence and
capability :
• Simple Reflex Agents
• Model-Based Reflex Agents
• Goal-Based Agents
• Utility-Based Agents
• Learning Agent

• 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 to date. The agent function is
based on the condition-action rule. A condition-
action rule is a rule that maps a state i.e., a
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.
Problems with Simple reflex agents are :
• Very limited intelligence.
• No knowledge of non-perceptual parts of the state.
• Usually too big to generate and store.
• If there occurs any change in the environment, then
the collection of rules needs to be updated.

• Goal-Based Agents:
• These kinds of agents take decisions based on how
far they are currently from their goal(description of
desirable situations). Their every action is intended
to reduce their distance from the 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.

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 a 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.

Learning Agent
A learning agent in AI is the type of agent that can learn
from its past experiences or it has learning
capabilities. It starts to act with basic knowledge and
then is able to act and adapt automatically through
learning. A learning agent has mainly four conceptual
components, which are:
1. Learning element: It is responsible for making
improvements by learning from the environment.

2. Critic: The learning element takes feedback from

critics which describes how well the agent is doing
with respect to a fixed performance standard.

3. Performance element: It is responsible for

selecting external action.

4. Problem Generator: This component is

responsible for suggesting actions that will lead to
new and informative experiences.

Types of Environments in AI:

An environment in artificial intelligence is the
surrounding of the agent. The agent takes input
from the environment through sensors and
delivers the output to the environment through
actuators.
 There are several types of environments:
• Fully Observable vs Partially Observable
• Deterministic vs Stochastic
• Competitive vs Collaborative
• Single-agent vs Multi-agent
• Static vs Dynamic
• Discrete vs Continuous
• Episodic vs Sequential
• Known vs Unknown

1. Fully Observable vs Partially Observable

• When an agent sensor is capable to sense or
access the complete state of an agent at each
point in time, it is said to be a fully observable
environment else it is partially observable.
2. Deterministic vs Stochastic
• When a uniqueness in the agent’s current state
completely determines the next state of the
agent, the environment is said to be
deterministic.
• The stochastic environment is random in nature
which is not unique and cannot be completely
determined by the agent.

3. Competitive vs Collaborative
• An agent is said to be in a competitive
environment when it competes against another
agent to optimize the output.
• The game of chess is competitive as the agents
compete with each other to win the game which
is the output.
• An agent is said to be in a collaborative
environment when multiple agents cooperate to
produce the desired output.
• When multiple self-driving cars are found on
the roads, they cooperate with each other to
avoid collisions and reach their destination
which is the output desired.
4. Single-agent vs Multi-agent
• An environment consisting of only one agent is
said to be a single-agent environment.
• A person left alone in a maze is an example of
the single-agent system.
• An environment involving more than one agent
is a multi-agent environment.
• The game of football is multi-agent as it involves
11 players in each team.

5. Dynamic vs Static
• An environment that keeps constantly changing
itself when the agent is up with some action is
said to be dynamic.
• A roller coaster ride is dynamic as it is set in
motion and the environment keeps changing
every instant.
• An idle environment with no change in its state
is called a static environment.
• An empty house is static as there’s no change in
the surroundings when an agent enters.
6. Discrete vs Continuous
• If an environment consists of a finite number of
actions that can be deliberated in the
environment to obtain the output, it is said to be
a discrete environment.
• The environment in which the actions are performed cannot be
numbered i.e. is not discrete, is said to be continuous.

7.Episodic vs Sequential
• In an Episodic task environment, each of the
agent’s actions is divided into atomic incidents
or episodes. There is no dependency between
current and previous incidents. In each incident,
an agent receives input from the environment
and then performs the corresponding action.

• In a Sequential environment, the previous

decisions can affect all future decisions. The
next action of the agent depends on what action
he has taken previously and what action he is
supposed to take in the future.
 8. Known vs Unknown
• In a known environment, the output for all
probable actions is given. Obviously, in case
of unknown environment, for an agent to
make a decision, it has to gain knowledge
about how the environment works.

Understanding Problem Formulation:

• Problem formulation is the process by which an
agent defines the task it needs to solve. This involves
specifying the initial state, goal state, actions,
constraints, and the criteria for evaluating solutions.
• Effective problem formulation is crucial for the
success of the agent in finding optimal or
satisfactory solutions.
Steps in Problem Formulation:
• Define the Initial State : The initial state is the
starting point of the agent. It includes all the
relevant information about the environment that the
agent can perceive and use to begin the problem-
solving process.
– Example: In a navigation problem, the initial state
could be the agent's starting location on a map.
Specify the Goal State : The goal state defines the
desired outcome that the agent aims to achieve.
It represents the condition or set of conditions
that signify the completion of the task. – Example:
For the navigation problem, the goal state is the
destination location.

• Determine the Actions : Actions are the set of

operations or moves that the agent can perform
to transition from one state to another. Each
action should be well-defined and feasible within
the given environment. – Example: In a robot
navigation scenario, actions could include
moving forward, turning left, or turning right.

• Define the Cost Function (if applicable) : The

cost function evaluates the cost associated with
different actions or paths. It helps the agent to
optimize its strategy by minimizing or maximizing
this cost. – Example: In route planning, the cost
function could represent the distance traveled,
time taken, or energy consumed.
• Criteria for Success : The criteria for success
determine how the agent evaluates its progress
and final solution. This includes metrics for
measuring the effectiveness and efficiency of the
solution. – Example: For a puzzle-solving agent,
success criteria could be the completion of the
puzzle within the shortest time or the fewest
moves.

IMPORTANCE OF PROBLEM FORMULATION:

Effective problem formulation is essential
because:
• Clarity : It provides a clear understanding of the
problem, making it easier to devise a solution.

• Efficiency : Proper formulation can significantly

reduce the computational resources required to
solve the problem.

• Optimal Solutions : It helps in finding the most

optimal or satisfactory solution by accurately
defining the goals and constraints.
Challenges in Problem Formulation:
Incomplete Information : The agent may not have
access to all the necessary information about the
environment.
• Dynamic Environments : The environment may
change unpredictably, requiring the agent to
adapt its problem formulation.
• Complex Constraints : Managing and
incorporating complex constraints can be
challenging.

SEARCH GRAPH:
A search graph represents the problem as a
collection of nodes (states) and edges (actions or
transitions between states). It can include cycles
and multiple paths between nodes.

Key Features:
 Nodes : Represent states or configurations of
the problem. Example: In a path finding
problem, a node could represent a location on a
map.

Edges : Represent actions or transitions from

one state to another. Example: Moving from one
location to another in a path finding problem.

Cycles : A search graph can contain cycles,

meaning you can revisit the same state multiple
times.

Multiple Paths : There can be multiple paths

between two nodes.

Search Tree :
A search tree is a specific representation of a
search graph where: Each node has exactly one
parent (except the root node). There are no
cycles. It represents the exploration of states in a
hierarchical manner.
Key Features:
Root Node : Represents the initial state of the
problem.

Branches : Represent actions or transitions from

one state to another.
Leaf Nodes : Represent goal states or dead ends
(states with no further actions).
No Cycles : A search tree cannot have cycles
because each node has only one parent.

Applications : Used in algorithms like DFS, BFS,

and Minimax (for game trees). Page 13 of 84

Feature SEARCH GRAPH SEARCH TREE

Structure Can have cycles No cycles; each

and multiple node has one
paths. parent
Memory More efficient Less efficient
Efficiency (avoids (may have
duplicates duplicates)
Complexity Handles Exponential
overlapping growth with
states well. depth
Use Case Used when Used when
states can be states are
revisited. unique

State space representation :

In artificial intelligence (AI), state-space
representation is a way to model and solve
problems by breaking them down into a
series of states and actions. It forms the
foundation for many AI algorithms,
especially in areas like search, planning, and
decision-making.
Components of State-Space Representation:
1. States: These are the possible
configurations or conditions of the
system or problem at a given time.
 o Example: In a chess game, a state
could be the current arrangement of
pieces on the board.
2. Initial State: The starting point or the
initial configuration of the system.
o Example: The chessboard at the start
of the game.
3. Actions (Operators): The set of
possible actions or moves that can be
taken from a given state to transition to
another state.
o Example: Moving a pawn or a knight in
chess.
4. Goal State: The target or desired state
that the system tries to achieve.
o Example: Checkmating the
opponent's king in chess.
5. State Transition Function: Rules that
define how an action transforms one
state into another.
 o Example: The legal moves in chess
that dictate how pieces can be
moved.
How It Works in AI:
State-space representation is often used in
search problems, where an AI agent explores
possible states to reach the goal state. For
instance:
• In pathfinding, the agent searches for the
shortest path from a start location to a
destination.
• In puzzle-solving, the agent explores
possible moves to solve a puzzle (e.g., the
8-puzzle or Rubik's cube).
This representation is fundamental for
algorithms like breadth-first search (BFS),
depth-first search (DFS), and A* search,
among others.