Module 1 - VTU-Introduction to AI and Applications-BETC105205.pdf

I N T R O D U C T I O N T O
A I A N D A P P L I C A T I O N S
BOS (CS/IS)
VTU Belagavi
B E T C 1 0 5 / 2 0 5
Prepared by
Module 1
Dr. Likewin Thomas,
BOS Members (CS/IS) - VTU Belagavi
Prof. & Head, Dept of AIML,
PESITM Shivamogg
Dr. Pavan Kumar M P,
BOS Members (CS/IS) - VTU Belagavi
Assoc. Prof., Dept of ISE,
JNNCE Shivamogg
Dr. Demian Antony D'Mello,
BOS Chairman (CS/IS) - VTU Belagavi
Vice Principal and Dean - Academics
Canara Engineering College

1. Introduction to Artificial Intelligence
1.Definition of Artificial Intelligence
2.How Does AI Work?
3.Advantages and Disadvantages of Artificial Intelligence
4.History of Artificial Intelligence
5.Types of Artificial Intelligence:
a.Weak AI vs. Strong AI
b.Reactive Machines
c.Limited Memory
d.Theory of Mind
e.Self-Awareness
6.Is Artificial Intelligence the Same as Augmented
Intelligence and Cognitive Computing?
7.Introduction to Machine Learning and Deep Learning
Outline
Module 1: Introduction to Artificial Intelligence
2. Machine Intelligence
1.Defining Intelligence
2.Components of Intelligence
3.Differences Between Human and Machine Intelligence
4.Agent and Environment in AI
5.Search Algorithms:
a.Uninformed Search Algorithms
b.Informed Search Algorithms:
i.Pure Heuristic Search
ii.Best-First Search Algorithm (Greedy Search)

1. Introduction to Prompt Engineering
Overview of Prompt Engineering
The Evolution of Prompt Engineering
Types of Prompts
How Does Prompt Engineering Work?
The Role of Prompt Engineering in Communication
The Advantages of Prompt Engineering
The Future of Large Language Model (LLM)
Communication
Outline
Module 2: Introduction to Prompt Engineering
2. Prompt Engineering Techniques for ChatGPT
Introduction to Prompt Engineering Techniques
Instructions Prompt Technique
Zero, One, and Few Shot Prompting
Self-Consistency Prompt
3. Prompts for Creative Thinking
Introduction to Creative Thinking with Prompts
Unlocking Imagination and Innovation
4. Prompts for Effective Writing
Introduction to Writing with Prompts
Igniting the Writing Process with Prompts

1. Machine Learning in AI
Overview of Machine Learning Techniques
Introduction to Machine Learning Models
Outline
Module 3: Machine Learning
2. Regression Analysis in Machine Learning
Basics of Regression
Linear and Non-Linear Regression Techniques
3. Classification Techniques
Overview of Classification Algorithms
Naïve Bayes Classification
Support Vector Machine (SVM)
4. Clustering Techniques
Introduction to Clustering
Types of Clustering Algorithms
5. Neural Networks
Basics of Neural Networks
Types and Applications of Neural Networks

1. AI and Ethical Concerns
Introduction to AI Ethics
Ethical Implications in AI Development
Addressing Bias and Fairness in AI
Outline
Module 4: Trends in AI
2. AI as a Service (AIaaS)
Overview of AI as a Service
Benefits and Challenges of AIaaS
Popular AIaaS Platforms
3. Recent Trends in AI
Overview of Current AI Trends
Key Developments in AI Research and Applications
4. Expert Systems
Introduction to Expert Systems
Components of Expert Systems
Applications of Expert Systems
5. Internet of Things (IoT)
Introduction to IoT
IoT Architecture and Components
IoT Applications in Various Industries
6. Artificial Intelligence of Things (AIoT)
AIoT: Combining AI and IoT
Applications of AIoT in Smart Cities, Healthcare, and
Industry 4.0

1. Robotics: An Application of AI
Introduction to Robotics
Robotics and AI Integration
Robotics as an Application of AI
Outline
Module 5: Robotics and Industrial Applications of AI
2. Drones Using AI
Introduction to AI in Drones
AI Technologies Powering Drones
Applications of AI in Drone Technology
3. No Code AI and Low Code AI
Overview of No Code and Low Code AI
Benefits and Use Cases of No Code AI
Developing AI Solutions with Low Code Platforms
4. Industrial Applications of AI
AI in Healthcare
Applications of AI in Healthcare Diagnostics and Treatment
AI in Finance
AI in Risk Management, Fraud Detection, and Algorithmic Trading
AI in Retail
AI in Inventory Management, Customer Personalization, and Sales
Forecasting
AI in Agriculture
Precision Agriculture and AI for Crop Management
AI in Education
AI in Adaptive Learning, Content Recommendation, and Student
Assessment
AI in Transportation
Autonomous Vehicles and AI for Traffic Management
AI in Experimentation and Multi-disciplinary Research
AI in Scientific Research and Innovation

Definition of Artificial Intelligence
Artificial Intelligence (AI)
is the science and
engineering of making
intelligent machines,
especially intelligent
computer programs, John
McCarthy (2004).
Historical Background: The concept
of AI was first explored in 1950 by Alan
Turing, a British mathematician and
computer scientist, who asked the
question "Can machines think?" This
was a groundbreaking idea and led him
to propose the Turing Test.
The Role of John McCarthy (2004):
Later, in 2004, John McCarthy
defined AI as the science and
engineering of making intelligent
machines—basically, how we can
program computers to act smart like
humans.
Understanding AI from a Simple
View: Think of AI as machines or
software that are designed to learn
from their environment, just like
humans learn from their experiences.
For example, an AI program can be
trained to recognize your face.
A Researcher’s Perspective:
For researchers, AI refers to
a set of algorithms (step-by-
step instructions) that help
a machine make decisions
and act without being
explicitly told what to do
each time.
Everyday AI Examples: Some well-
known examples of AI include chess-
playing computers or self-driving cars.
These systems depend on deep learning
(a type of AI that mimics the human
brain) and natural language processing
(helping computers understand human
language, like Siri or Google Assistant).

How Does AI Work?
AI works by processing large datasets, recognizing patterns, and making decisions using
algorithms.
It involves learning, reasoning, and self-correction:
a.Learning - AI learns from data
b.Reasoning - AI chooses the correct algorithm
c.Self-Correction - AI refines algorithms for accuracy

Advantages and Disadvantages of AI
Advantages:
1.Performs well on tasks that uses detailed data.
2.Takes less time to perform tasks that needs to
process huge volumes of data.
3.Generates consistent and accurate results.
4.Can be used 24 X 7.
5.Optimizes tasks by better utilizing resources.
6.Automates complex processes.
7.Minimizes downtime by predicting
maintenance needs.
8.Enables companies to produce new products
having better quality and speed.
Disadvantages:
1.Involves more cost.
2.Technical expertise required to develop and
use AI applications.
3.Lack of trained professionals.
4.Incomplete or inaccurate data may result in
disastrous results.
5.Lacks the capability to generalize tasks

History of AI
1943 - First Neural Network Model Proposed: By
Warren McCullough and Walter Pitts to lay the
foundation for artificial neural networks and machine
learning.
1950 - Turing Test Introduced: By Alan Turing to
measure a machine's ability to exhibit intelligent
behavior indistinguishable from humans.
1956 - John McCarthy Coins the Term 'Artificial
Intelligence': By John McCarthy during the Dartmouth
Conference, marking the birth of AI as a formal field.
1997 - IBM Deep Blue Defeats Chess Champion Garry
Kasparov: By IBM's team to demonstrate AI’s capability
in strategic decision-making through computational
power.
2011 - Siri Introduced by Apple: By Apple to
revolutionize personal assistants using natural language
processing and AI for everyday tasks.

Types of AI
AI can be categorized into:
Weak AI (Narrow AI)
Weak AI, also known as narrow AI, is designed to do
one specific task.
Siri and Alexa are examples.
Strong AI (Artificial General Intelligence)
Strong AI, also called Artificial General Intelligence (AGI) or Superintelligence (ASI), tries to mimic human
thinking.
Can perform tasks it hasn’t been specifically trained for.
Requires abilities like visual perception, speech recognition, decision-making, and language translation.
Reactive Machines
Reactive Machines are the simplest type of AI that react to situations based on immediate input, but they
have no memory or ability to learn from past experiences.
Examples: IBM’s Deep Blue (chess-playing computer) is a reactive machine. It makes decisions based on the
current state of the game but doesn’t remember past games.

Types of AI
Based on Capabilities
Weak AI (Narrow AI)
Weak AI, also known as narrow AI, is
designed to do one specific task.
Strong AI
Strong AI, also called Artificial
General Intelligence (AGI) or
Superintelligence (ASI), tries to mimic
human thinking

Types of AI
Based on Functionalities
Reactive Machines
Reactive Machines are the simplest type of AI that react to situations based on immediate
input, but they have no memory or ability to learn from past experiences.
Limited Memory
Limited memory AI systems can remember data for a short time and use it to make
decisions, but they don’t keep data permanently.
Theory of Mind
The Theory of Mind in AI aims to create machines that can understand thoughts, emotions,
and memories—just like humans.
Self-Awareness
Self-awareness in AI means machines that have a human-level consciousness—they can
understand their own existence and feelings.

Types of AI
1.Weak AI (Narrow AI)
Weak AI, also known as narrow AI, is designed to do one specific task.
Examples:
Siri and Alexa are examples. When you tell Alexa to play a song, it does so because it’s trained to
understand that specific command.
Other examples include weather forecasting, predicting stock prices, and Google search.
How it works: These systems are great at doing one thing really well, but they don’t work outside their
specific task. For example, Alexa can’t drive a car; it’s just built for voice commands.
Why it's important: Weak AI has helped make many tasks easier and more efficient, and it is the most
common type of AI in use today.

Types of AI
2. Strong AI (Artificial General Intelligence - AGI)
Strong AI, also called Artificial General Intelligence (AGI) or Superintelligence (ASI), tries to mimic
human thinking.
Can perform tasks it hasn’t been specifically trained for.
Requires abilities like visual perception, speech recognition, decision-making, and language translation.
Future potential: Experts believe that Strong AI might one day surpass human intelligence, but it’s not
expected to happen anytime soon.
Concerns: While some fear that Strong AI could be dangerous, experts say we don’t need to worry about
it in the near future, as it’s still far from becoming a reality.

Types of AI
3. Reactive Machines
Reactive Machines are the simplest type of AI that react to situations based on immediate input, but they
have no memory or ability to learn from past experiences.
Examples: IBM’s Deep Blue (chess-playing computer) is a reactive machine. It makes decisions based
on the current state of the game but doesn’t remember past games.

Types of AI
4. Limited Memory
What it is: Limited memory AI systems can remember data for a short time and use it to make decisions, but
they don’t keep data permanently.
Examples:
Autonomous vehicles use limited memory to track information like speed of nearby cars, distance
between cars, and speed limits to navigate safely.
AlphaGo, the AI that defeated the world champion in the game Go, also used limited memory to play
and improve during the game.
These systems learn and improve continuously by analyzing new data and adjusting based on feedback.
Key Models:
Reinforcement Learning: AI learns by trial and error, improving over time.
Long Short-Term Memory (LSTM): AI uses past data to predict the next step, but it focuses more on
recent data.
Evolutionary GANs (E-GAN): The AI evolves over time, using data and feedback to make better
decisions and predict outcomes.

Types of AI
5 Theory of Mind
The Theory of Mind in AI aims to create machines that can understand thoughts, emotions, and memories—
just like humans.
How it works: AI would need to understand feelings and emotions that influence decisions. These machines
would make choices by considering both reason and emotional context.
Current Status: This is still theoretical, meaning it’s an idea for the future, but it could become a reality soon.

Types of AI
6 Self-Awareness
What it is: Self-awareness in AI means machines that have a human-level consciousness—they can understand
their own existence and feelings.
How it works: These machines would not only understand what someone says but also how they feel based
on the way they communicate. They could learn and adapt their responses to the emotions of others.
Current Status: Self-awareness in AI doesn’t exist yet, but it might happen in the future.

Is AI the Same as Augmented Intelligence and Cognitive Computing?
AI vs Augmented Intelligence:
Some people think AI and augmented intelligence are the same, but they are different.
Augmented intelligence is a type of weak AI that assists humans to improve tasks or decisions.
Example: Automatically highlighting important information in a report.
True AI / Strong AI / AGI is future AI that aims to surpass human intelligence, capable of performing tasks
that humans can do, like reasoning and decision-making.
AI in Machines: AI makes machines simulate human intelligence by learning, sensing, processing, and
reacting to information.
Cognitive Computing: This refers to systems or products that mimic human thought processes to enhance
decision-making.

Machine Learning is a branch of AI that teaches machines to learn from data and make decisions without
being explicitly programmed.
How it works:
a.Finding patterns: ML algorithms analyze data to identify patterns.
b.Learning from experience: Machines improve automatically by learning from their past output.
c.Self-correction: If the machine makes a mistake, it adjusts and learns to improve accuracy over time.
Real-life examples:
Number series problem: Finding the missing number in 10, 20, 30, 40 →50. Machines learn patterns just
like humans do.
Machine Learning (ML)

Relationship between Artificial Intelligence, Machine Learning, Deep
Learning and Natural Language Processing

HOW IS AI RELATED TO MACHINE LEARNING?
AI vs ML:
AI is the larger goal (the superset) that aims to
make machines intelligent.
Machine Learning is a subset of AI, used specifically
for learning from data to make decisions.
In simple terms, ML helps achieve AI by making
machines learn and adapt.
Example:
If you want a robot that can see, talk, walk, and learn, you would use AI because it requires many different
technologies.
Machine Learning would only be used to help the robot learn from its environment or past experiences.

Traditional Programming vs Machine Learning
Traditional Programming:
Manual Coding: In traditional programming, the
programmer manually writes code that accepts input
data and returns output based on pre-defined rules.
Languages Used: It uses procedural programming
languages like C, C++, Java, Python, etc., where the logic
and rules are explicitly coded by the programmer.
Algorithm-Dependent: The program is created based on
specific algorithms chosen by the programmer, who also
analyzes their performance to pick the best one for the
task.
Static Rules: The rules and logic of the program are
fixed and cannot change unless modified by the
programmer.
Machine Learning Programming:
Data-Driven Approach: Machine learning
programming learns automatically from data
Predictive Modeling: For example, if we input
customer data and transactions, machine learning
can create a predictive model to forecast
Automated Learning: Adapt and improve over
time as more data is processed.
Embedded Analytics: Machine learning
introduces embedded analytics, like natural
language processing, automatic anomaly
detection, and recommendation systems, to make
intelligent predictions

Traditional Programming vs Machine Learning
Key Differences:
Traditional Programming: It uses fixed logic and pre-written rules defined by the programmer.
Machine Learning: It learns from data, automatically creates its own rules, and improves over time based on
experience.
Example:
Traditional Programming: To filter images manually, you would have to write code comparing each pixel value in
the image. This approach can be slow and inaccurate.
Machine Learning: You simply provide photos of a person, and the model learns to recognize that person based
on patterns in the images, making the task much easier and more efficient.

Machine Learning and Deep Learning
Artificial Intelligence (AI):
AI is the broad concept
of creating machines or
systems that mimic
human intelligence. It
involves a variety of
algorithms and
approaches, one of
which is machine
learning.
Machine Learning (ML):
ML is a specific subset of AI that
involves teaching machines to learn
from data.
ML techniques can be divided into:
Supervised Learning: Uses
labeled data (data with known
outcomes) to train the model.
Unsupervised Learning: Uses
unlabeled data (data without
predefined outcomes) to
discover patterns or structure in
the data.
Deep Learning (DL):
Deep learning is a subset of machine
learning that uses neural networks with
multiple layers (known as deep neural
networks) to analyze and process large
amounts of data. These networks are
inspired by the way the human brain
processes information.
Deep learning allows machines to learn in
much more complex ways, making
connections between layers of data to
improve decision-making.
It is particularly powerful for tasks like image
recognition, speech processing, and other
complex tasks.

How Does AI Work?
Study Large Data: AI analyzes large amounts of data to find patterns.
Make Predictions: AI makes predictions based on the data it has studied.
Autonomous Decision Making: AI can make decisions on its own, learning from past data and experiences.
Adapt and Improve: AI systems continuously adapt and learn from new data to improve over time.
React to Problems: AI systems perceive problems and respond accordingly, using the patterns they have
learned.
Data-Driven: AI relies on large amounts of data to make decisions, with cheap data storage and fast
processors making it easier to use.
Make Accurate Predictions: AI can make accurate predictions based on past data and experience.
Growing and Evolving: AI is being applied in many different areas and its scope is continuously expanding.

Chapter 3: Artificially Intelligent Machine
3.1 Defining Intelligence,
3.2 Components of Intelligence,
3.3 Differences Between Human and Machine Intelligence,
3.4 Agent and Environment,
3.5 Search,
3.6 Uninformed Search Algorithms,
3.7 Informed Search Algorithms:

3.1 Defining Intelligence
Linguistic Intelligence:
The ability to speak and understand language (e.g., narrators, orators).
Musical Intelligence:
The ability to create and understand music, like recognizing rhythm and pitch (e.g., musicians, singers).
Logical-Mathematical Intelligence:
The ability to understand abstract concepts and use logic (e.g., mathematicians, scientists).
Spatial Intelligence:
The ability to visualize and manipulate images in your mind (e.g., map readers, astronauts).
Bodily-Kinesthetic Intelligence:
The ability to use your body to solve problems or manipulate objects (e.g., dancers, athletes).
Intrapersonal Intelligence:
The ability to understand your own feelings and motivations (e.g., spiritual leaders, philosophers).
Interpersonal Intelligence:
The ability to understand other people’s feelings and intentions (e.g., mass communicators, interviewers).
Artificial Intelligence (AI): A system or machine is said to be artificially intelligent if it can exhibit one or more of these
types of intelligence.

3.2 Components of Intelligence
Reasoning
Learning
Problem solving
Perception
Linguistic intelligence

3.2 Components of Intelligence - 1. Reasoning
Reasoning is the process used to make decisions and predictions. It involves analyzing information and
drawing conclusions based on evidence. There are two main types of reasoning:
1. Inductive Reasoning:
What it is: Making generalizations based on specific observations or examples.
How it works: Starts with specific facts or observations and moves to a general conclusion.
Example: If you see 10 white swans, you might conclude that all swans are white.
2. Deductive Reasoning:
What it is: Drawing a specific conclusion based on general principles or facts.
How it works: Starts with a general statement or premise, and moves to a specific conclusion.
Example: All swans are birds. A swan is a bird. Therefore, the swan is a bird.

3.2 Components of Intelligence - 2. Learning
Learning is the process of gaining knowledge or skills by studying, practicing, or experiencing
something. It helps humans, animals, and even AI systems to improve their understanding of different
subjects.
There are different types of learning:
1.Auditory Learning:
2.Episodic Learning:
3.Motor Learning:
4.Observational Learning:
5.Perceptual Learning:
6.Relational Learning:
7.Spatial Learning:
8.Stimulus-Response Learning:

There are different types of learning:
1. Auditory Learning:
Learning by hearing and listening.
Example: Listening to recorded lectures to understand a concept.
2. Episodic Learning:
Learning by remembering events or experiences in a specific order.
Example: Recalling what happened in a sequence, like remembering steps in a recipe.
3. Motor Learning:
Learning through physical movement of muscles.
Example: Learning how to pick up objects correctly.
4. Observational Learning:
Learning by watching and imitating others.
Example: Children learn by copying their parents’ actions.

5. Perceptual Learning:
Learning by recognizing things that have been seen before.
Example: Identifying objects and situations based on prior experiences.
6. Relational Learning:
Learning by recognizing patterns in relationships between things.
Example: Adjusting the amount of spices in a dish after remembering how much was used last time.
7. Spatial Learning:
Learning through visual stimuli like images, maps, and colors.
Example: Creating a mental map of a route before actually driving.
8. Stimulus-Response Learning:
Learning by reacting to a specific stimulus.
Example: Shouting when touching a hot pan, because it causes pain.

Problem solving is the process of finding a solution to a challenge or issue. It involves:
Identifying the problem: Understanding the situation and recognizing the obstacles (either known or
unknown).
Making decisions: Choosing the best approach or method to overcome the obstacles and reach the
goal.
3.2 Components of Intelligence - 3. Problem Solving

Perception is the process of:
Acquiring information through the senses (like sight, hearing, etc.).
Interpreting that information to understand what’s happening around us.
Selecting important details and organizing them to form a clear picture.
Humans use sensory organs (like eyes, ears) to perceive the world.
AI systems use sensors (like cameras, microphones) to gather data and understand their environment.
3.2 Components of Intelligence - 4. Perception
It is used in in interpersonal communication and defines one’s ability to use, comprehend, speak and write
the verbal and written language
3.2 Components of Intelligence - 5. Linguistic Intelligence

3.3 Differences Between Human and Machine Intelligence
Aspect Human Intelligence Machine Intelligence
Perception Perceives through patterns Perceives by analyzing data with rules
Memory and Recall Recalls information by patterns
Uses search algorithms to find
information
Handling Missing Information
Can deduce missing or distorted
information accurately
Struggles with incomplete data, less
accurate

AI agents act in their environment, which may include other agents.
They perceive their environment using sensors.
They act upon the environment using effectors.
Types of Agents in an AI System:
1.Human Agent:
Sensors: Sensory organs like eyes, ears, nose, skin, etc.
Effectors: Hands, legs, mouth for taking action.
2.Robotic Agent:
Sensors: Cameras, infrared range finders.
Effectors: Motors, actuators to perform actions.
3.Software Agent:
Sensors: Uses bit strings as its programs.
Effectors: Executes programmed actions based on those bit strings.
3.4 Agent and Environment

Performance Measure of Agent:
It helps determine how successful an agent is based on its actions.
Behaviour of Agent:
The action performed by an agent after receiving a percept (input).
Percept:
Perceptual input received by an agent at a specific moment in time.
Percept Sequence:
A list of all percepts an agent has received up until now.
Agent Function:
A map that connects the percept sequence to an action performed by the agent.
3.4 Agent and Environment - 3.4.1 Key Terminology

Rationality is the ability to make responsible and sensible decisions.
A rational agent makes decisions that maximize its performance based on:
a.Performance measure (how successful the agent is).
b.Percept sequence (the inputs it has received).
c.Prior knowledge (what the agent already knows about the environment).
d.Possible actions (what the agent can do).
A rational agent always performs the right action to maximize its performance.
Problem Solved by Agent (PEAS):
Performance measure, Environment, Actuators, and Sensors are used to define a problem that an agent will solve.
3.4 Agent and Environment - 3.4.2 Rationality

1.Simple Reflex Agents
2.Model-Based Reflex Agents
3.Goal-Based Agents
4.Utility-Based Agents
5.Learning Agent
3.4 Agent and Environment - 3.4.4 Types of Agents

1. Simple Reflex Agents
Simple reflex agents choose actions based only on the current percept (the data they receive at a specific moment).
They are rational only if they make the correct decision based on the current percept.
Working:
They use a condition-action rule that maps a state (condition) to an action.
If the condition is true, the agent performs the action; otherwise, it does nothing.
Limitations:
They require the environment to be fully observable (the agent must have access to complete information).
If the environment is partially observable, the agent might get stuck in infinite loops.
In such cases, the agent can only escape the loop if it randomizes its actions.
Other Issues:
Simple reflex agents have very limited intelligence.
They don’t know anything about states other than the current one.
If the environment changes, the rules they follow might need to be updated.

2. Model-Based Reflex Agents
Model-based reflex agents use a model of the world to
choose their actions, which requires them to maintain an
internal state.
Internal State:
The internal state represents aspects of the current
situation that are not directly observable but can be
inferred based on the history of percepts.
Working
a.The agent updates its internal state by
understanding two things:
b.How the world evolves (what happens over time in
the environment).
c.How the agent’s actions affect the world (the
consequences of the agent's actions).

3. Goal-Based Agents
A description of desirable situations or
outcomes.
Function: Goal-based agents choose their
actions to achieve specific goals.
Flexibility: Provides more flexibility than reflex
agents as the decision-making knowledge is
explicitly modeled.
Modifications: Allows modifications to adapt
to changing conditions.

4. Utility-Based Agents
Purpose: Used when goals are conflicting or
difficult to achieve.
Function: These agents choose actions based
on a preference (utility) for each state.
Goal: They help prioritize goals by choosing
actions that lead to the most preferred
outcome.
Flexibility: Provides a way to handle multiple
goals by considering their relative importance.

5. Learning Agent
Purpose: A learning agent learns from past experiences
and adapts over time.
Starting Point: It begins with basic knowledge and
gradually improves by learning from its environment.
Four Main Components:
1.Learning Element:
Responsible for making improvements by learning from the
environment.
2.Critic:
Provides feedback to the learning element, evaluating how well
the agent is performing against a set performance standard.
3.Performance Element:
Selects the external action to be taken based on the current
situation.
4.Problem Generator:
Suggests actions that lead to new experiences for the agent,
promoting further learning.

Artificial Intelligence Systems:
Some AI programs are confined to limited environments
like keyboard input, databases, and file systems.
Others, like software robots (softbots), operate in
unlimited domains with complex environments.
Turing Test
Purpose: The Turing Test is used to determine if a
machine can exhibit intelligent behavior.
Test Setup:
a.Two humans and a software agent (machine)
participate.
b.One human is the tester, unaware of which participant
is the machine.
c.The tester sends typed questions to both humans and
the software agent.
3.4 Agent and Environment - 3.4.5 The Nature of Environments

1.Discrete/Continuous:
a.Discrete: Limited, well-defined states (e.g., chess).
b.Continuous: No limitations on percepts or actions (e.g., self-driving car).
2.Known vs Unknown:
a.Known: Agent knows results for all actions.
b.Unknown: Agent must learn how to act (e.g., reinforcement learning).
3.Observable/Partially Observable:
a.Observable: Agent can perceive the complete state (e.g., chess).
b.Partially Observable: Agent cannot perceive everything (e.g., Kriegspiel chess).
4.Static/Dynamic:
a.Static: Environment does not change while acting (e.g., crossword puzzle).
b.Dynamic: Environment changes during action (e.g., self-driving car).
c.Semi-dynamic: Environment doesn’t change, but agent’s performance can change.
3.4 Agent and Environment - 3.4.6 Types of Environments

5. Single Agent/Multiple Agents:
Single Agent: One agent in the environment (e.g., vacuum cleaner).
Multiple Agents: More than one agent, can be competitive or cooperative (e.g., chess, taxi driving).
6. Accessible/Inaccessible:
Accessible: Agent has full access to environment information (e.g., empty room).
Inaccessible: Agent cannot get complete information (e.g., global events).
7. Deterministic/Non-deterministic:
Deterministic: Next state can be determined from current state (e.g., chess).
Non-deterministic: Uncertainty about outcomes (e.g., ludo, dice roll).
8. Episodic/Non-episodic:
Episodic: Each episode is independent (e.g., simple tasks).
Non-episodic: Current actions affect future actions (e.g., long-term decision making).
3.4 Agent and Environment - 3.4.6 Types of Environments

AI agents use search algorithms to solve tasks and make decisions. For example, single-player games
like Sudoku and tile games use search algorithms to find optimal moves or positions.
Components of a Search Problem
1.State Space: The set of all possible states the agent can reach.
2.Start State: The initial state where the search begins.
3.Goal Test: A function that checks if the current state is the goal state.
4.Solution: A sequence of actions (plan) that transforms the start state to the goal state, achieved
using search algorithms.
3.5 Search

3.5.1 Types of Search Algorithms
3.5 Search

3.5.2 Properties of Search Algorithms
Completeness: A search algorithm is complete if it guarantees at least one solution for a given
input.
Optimality: A search algorithm is optimal if it provides the best solution with the lowest path
cost.
Time and Space Complexity:
Time Complexity: The amount of time an algorithm takes to complete a task.
Space Complexity: The amount of memory required for the search process.
A good search algorithm should use less time and less memory.
3.5 Search

Uninformed search (or blind search) algorithms have no extra information about the goal state
other than what is provided in the problem definition. The algorithm blindly explores the search
space without considering how close it is to the goal.
Key Concepts:
a.Problem Graph: Represents the problem, from the start node (S) to the goal node (G).
b.Strategy: The path taken in the search to reach the goal.
c.Fringe: A data structure that stores all possible states (nodes) that can be reached from the
current state.
d.Tree: The path representation that the algorithm follows while searching for the goal node.
e.Solution Plan: The sequence of nodes (states) from start node (S) to goal node (G).
f.Path/Step Cost: Integer values that represent the cost to move from one node to another.
3.6 Uninformed Search Algorithms

Uninformed search (or blind search) algorithms have no extra information about the goal state other than what is
provided in the problem definition. The algorithm blindly explores the search space without considering how close
it is to the goal.
Key Concepts:
a.Problem Graph: Represents the problem, from the start node (S) to the goal node (G).
b.Strategy: The path taken in the search to reach the goal.
c.Fringe: A data structure that stores all possible states (nodes) that can be reached from the current state.
d.Tree: The path representation that the algorithm follows while searching for the goal node.
e.Solution Plan: The sequence of nodes (states) from start node (S) to goal node (G).
f.Path/Step Cost: Integer values that represent the cost to move from one node to another.
3.6 Uninformed Search Algorithms
3.6.1 Depth First Search (DFS)
3.6.2 Depth-Limited Search Algorithm (DLS)
3.6.3 Breadth First Search (BFS)
3.6.4 Uniform Cost Search (UCS)
3.6.5 Iterative Deepening Depth-First Search (IDDFS)
3.6.6 Bidirectional Search

Depth First Search (DFS) is a simple search algorithm used to explore a tree or graph by
starting from the root node and exploring as far as possible along each branch before
backtracking.
Steps of DFS:
1.Start from the root node: Begin searching from the root node (node A).
2.Explore each branch: Move from node A to its child node (B), then to the next child (D),
and continue exploring until you reach the leaf node (the last node of that branch).
3.Backtrack: If the key you're looking for isn't found at the leaf node, backtrack to the last
node with unexplored branches and explore them.
4.Repeat the process: Continue exploring each branch by backtracking and then moving
to the next unexplored branch, until the entire tree is searched or the goal is found.
3.6 Uninformed Search Algorithms - 3.6.1 Depth First Search (DFS)

Start at node A.
Explore node B, then node D, then node H (leaf node).
Since H is a leaf and the key isn't found, backtrack to node B and
explore node E, then node I (leaf node).
Backtrack again to explore node J and other branches of node B.
Once all branches of node B are explored, move to node C, then
node F, node K, and finally node G.
Key Points:
Backtracking is a key feature of DFS, meaning it explores as far as possible along each branch
before going back to previous nodes to try other paths.
DFS uses a stack (Last-In-First-Out or LIFO structure) to keep track of nodes to visit next.

Advantages of Depth First Search (DFS)
1.Less Memory Usage: DFS stores only the nodes along the path from the root node to the current node,
requiring less memory.
2.Faster to Reach Goal: It often takes less time to find a goal compared to Breadth First Search (BFS),
especially when the solution is deep in the tree.
Disadvantages of Depth First Search (DFS)
1.Recurring States: Sometimes, many states repeat. In such cases, there’s no guarantee of finding the
solution.
2.Infinite Loops: DFS may get stuck in an infinite loop when it keeps going deeper. This can be avoided
by setting an appropriate cut-off depth, but:
Too small a cut-off may make the algorithm fail.
Too large a cut-off increases execution time.
3.Complexity: The algorithm's complexity depends on the number of paths it needs to explore.
4.Duplicate Nodes: DFS cannot check for duplicate nodes, potentially leading to inefficiency in the
search.

1.Start at node S, goal is node G.
2.How DFS Works:
a.DFS (Depth-First Search) explores the deepest node first before backtracking.
b.It goes down one branch completely until it reaches the goal or the end of that branch.
3.Step-by-Step Traversal:
a.Start at S →go to A
b.From A →go to B
c.From B →go to C
d.From C →reach G (goal found)
4.Path Found: S →A →B →C →G
5.The traversal is shown in blue arrows
DFS Example – Finding Path to Node G

Depth-Limited Search (DLS) is similar to Depth First Search
(DFS), but with a predetermined depth limit to avoid infinite
paths. When the search reaches the specified depth limit, nodes
at that depth are treated as leaf nodes (i.e., nodes with no
successors).
Termination Conditions of DLS:
1.No Solution: If the problem has no solution, it's called
standard error failure.
2.No Solution within Limit: If the solution is not found within
the given depth limit, it's called cut-off failure.
3.Solution Found: If the solution is found within the depth
limit, the algorithm stops.
3.6 Uninformed Search Algorithms - 3.6.2 Depth-Limited Search (DLS)
Example:
If the depth limit is set to 2, the
algorithm will not explore level 3 of
the tree, so nodes E, F, G, H won't
be traversed.

Search for node H using Depth-Limited Search (DLS) with a depth limit of 2.
How DLS Works:
Start at root node A (level 0).
Explore all nodes at level 1: B, C, D, E.
Move to level 2 and explore children of B →H not found →backtrack.
Explore children of C at level 2 →H found.
Traversal Path:
A →B →(children of B checked) →backtrack
A →C →H →goal found
Key Idea:
DLS explores nodes up to a fixed depth limit.
If the goal isn’t found at the current depth, it backtracks to explore other branches.
Useful when the goal depth is known or limited.
3.6 Uninformed Search Algorithms - 3.6.2 Depth-Limited Search (DLS)
DLS Example – Searching for Node H (Limit = 2)

Breadth First Search (BFS) is an algorithm that traverses the tree
breadthwise, meaning it explores all nodes at the current level before
moving to the next level.
How It Works:
1.Start at the root node: The search begins at the root node (node
A).
2.Traverse level by level: First, visit the immediate children of the
root (nodes B and C).
3.Move to the next level: After visiting all nodes at the current level,
the search moves to the next level, visiting nodes D, E, F, and G.
4.Continue level-wise traversal: The algorithm continues this
process, exploring all neighbor nodes (children) at each level
before moving deeper.
3.6 Uninformed Search Algorithms - 3.6.3 Breadth First Search (BFS)
Example
Start at node A.
Visit its children: nodes B and C.
After visiting nodes at level 1,
move to level 2: nodes D, E, F, and
G.
Then, visit level 3: nodes H to K.

Find the path from S to G using Breadth-First Search (BFS).
How BFS Works:
BFS explores nodes level by level, starting from the root (shallowest nodes first).
It explores all nodes at the current depth before moving to the next level.
Step-by-Step Traversal:
Start at S →explore children nodes at level 1.
Node D is explored first →then G is reached.
Path Found:
S →D →G
Key Idea:
BFS always finds the shallowest solution (fewest number of nodes in path).
Path length = level of the shallowest solution.
3.6 Uninformed Search Algorithms - 3.6.3 Breadth First Search (BFS)
Example: Find the BFS traversal from node S to node G

UCS finds the cheapest path from a start node to a goal node when
the step costs are different.
How It Works:
1.It calculates the total cost to reach each node from the start.
2.Always expands the node with the lowest cumulative cost next.
3.It does not follow depth-first or breadth-first order.
Cost of a Node:
cost(node) = sum of costs from start to this node
cost(start) = 0
Key Feature:
UCS always finds the optimal (cheapest) path.
Works like BFS if all step costs are the same.
3.6 Uninformed Search Algorithms - 3.6.4 Uniform Cost Search (UCS)
Example:
Start at S (start) and goal is G.
If A has lower cost than G, UCS will
explore A first.
Then it compares children (B and C),
chooses the one with lowest cost (say
C), and continues until reaching G.
Implementation:
Usually uses a priority queue, which
gives priority to nodes with lowest
cumulative cost.

Find the cheapest path from S (start) to G (goal) using Uniform Cost Search (UCS).
How UCS Works:
UCS expands nodes based on cumulative cost from the start node.
Always selects the node with the lowest total cost to explore next.
Start at S →check child nodes A and other options.
Choose A because it has the lowest cumulative cost.
From A, explore B →cost is still lowest.
From B, reach G →goal reached with least total cost.
Path Found:
S →A →B →G
Total Cost:
5
Key Idea: UCS always finds the optimal path based on actual costs, not depth or heuristic.
3.6 Uninformed Search Algorithms - 3.6.4 Uniform Cost Search (UCS)
Example: Find the path and cost to move from node S to node G in the graph given below

IDDFS is used to search for a goal node when the depth of the goal is unknown.
It combines the advantages of DFS (low memory use) and BFS (guaranteed shortest path).
How It Works:
1.Start with a depth limit of 1, perform DFS up to that depth.
2.If the goal isn’t found, increase the depth limit by 1 and repeat DFS.
3.Continue increasing the depth limit until the goal is found.
Key Feature:
Memory Efficient: Only stores nodes in the current DFS path (like DFS).
Fast Search: Gradually explores all levels (like BFS).
Does not generate nodes beyond the current depth limit until needed.
Best Used When:
The search space is large.
The depth of the goal node is unknown.
3.6 Uninformed Search Algorithms - 3.6.5 Iterative Deepening Depth-First
Search (IDDFS)

Search for a goal node H using Iterative Deepening Depth-First Search (IDDFS).
How IDDFS Works:
Performs DFS repeatedly with gradually increasing depth limits.
Each iteration explores all nodes up to the current depth limit.
Iteration 1 (Depth 0): Explore node A
Iteration 2 (Depth 1): Explore nodes B and C
Iteration 3 (Depth 2): Explore nodes D, E, F, G
Iteration 4 (Depth 3): Explore node H →goal found
Search (IDDFS)
Example: Traverse the given tree using the iterative deepening depth-first search
algorithm.

Key Features:
Memory Efficient: Only stores nodes along the current DFS path.
Complete: Guarantees to find the goal if it exists.
Combines DFS depth efficiency with BFS completeness.
Complexity:
Time Complexity: O(b^d)
Space Complexity: O(b·d)
Where b = branching factor, d = depth of the goal
Key Idea:
IDDFS gradually deepens the DFS limit until the goal is reached, balancing memory efficiency and completeness.
Search (IDDFS)
Example: Traverse the given tree using the iterative deepening depth-first search
algorithm.

To find the shortest path between a start node and a goal node more efficiently.
How It Works:
1.The search happens from both directions at the same time:
a.Forward Search: From the start node toward the goal.
b.Backward Search: From the goal node toward the start.
2.The search stops when the two paths meet at a common node.
Advantage:
Reduces the search space because each search only goes half the total distance.
Faster than searching in a single direction.
Key Idea:
Instead of one big search, two smaller searches meet in the middle, saving time and memory.
3.6 Uninformed Search Algorithms - 3.6.6 Bidirectional Search

Find the path from source node 0 to goal node 14 using Bidirectional Search.
3.6 Uninformed Search Algorithms - 3.6.6 Bidirectional Search
Example: Consider the graph given below and apply bidirectional search on it to
reach goal node 14 from source node 0.
How Bidirectional Search Works:
Two searches are run simultaneously:
Forward Search: From 0 →14
Backward Search: From 14 →0
Both searches continue until they meet at a
common node.
Forward search: 0 →… →7
Backward search: 14 →… →7
Intersection at node 7 →path found
Path Found:
Concatenate forward and backward
paths:
0 →… →7 →… →14

To find the goal node efficiently using extra information about the search space.
Key Feature:
Uses a heuristic function to estimate how close a node is to the goal.
How it Works:
The heuristic function h(n) calculates the estimated cost from the current node n to the goal.
The agent uses this information to choose the most promising path, reducing unnecessary
exploration.
Advantages:
Explores fewer nodes than uninformed search (DFS, BFS).
Reaches the goal faster in large search spaces.
Guarantee:
Heuristic may not always give the absolute best path, but it finds a good solution in reasonable time.
Key Idea:
Informed search = smarter search because it “knows” which directions are likely better.
3.7 Informed Search Algorithms

To solve problems with a large number of possible states efficiently by using problem-specific
knowledge.
Key Idea:
Expands nodes based on their heuristic value h(n) (an estimate of distance to the goal).
How it Works:
Maintains two lists:
OPEN list: Nodes yet to be expanded.
CLOSED list: Nodes already expanded.
In each step, the node with the lowest heuristic value is expanded first.
Process:
Apply heuristic to child nodes.
Add child nodes to OPEN list based on their heuristic value.
Keep shorter paths, discard longer ones.
Repeat until goal state is reached.
3.7 Informed Search Algorithms - 3.7.1 Pure Heuristic Search
Benefit:
Reduces unnecessary
exploration and
focuses on the most
promising paths.
Examples:
Greedy Best-First
Search
A Search Algorithm*

To find the goal node quickly by always choosing the most promising path.
Key Idea:
Uses a heuristic function h(n) to estimate which node is closest to the goal.
Combines advantages of DFS (deep exploration) and BFS (level-wise search).
How it Works:
a.Insert the start node in the OPEN list.
b.If OPEN list is empty, stop (goal not found).
c.Remove the node with lowest heuristic value h(n) from OPEN, add it to CLOSED list.
d.Expand this node and generate its successor nodes.
e.If any successor is the goal, stop and return success.
f.If a successor is not in OPEN or CLOSED, add it to OPEN list.
g.Repeat from Step b until goal is found.
3.7 Informed Search Algorithms - 3.7.2 Best-First Search Algorithm
(Greedy Search)

Implementation:
Usually uses a priority queue, giving priority to nodes with lowest heuristic value.
Benefit:
Finds the goal faster by focusing on promising paths rather than exploring all nodes.
Key Idea:
Greedy because it always picks the node that looks best now, but it may not always find the
shortest path.
(Greedy Search)

Traverse the tree from node
S to find the goal node
using the Greedy Best-First
Search Algorithm.
The algorithm uses the
heuristic value h(n) to
choose the most promising
node at each step.
(Greedy Search)
Example: Consider the tree given below and traverse it using greedy best first search
algorithm.

How Greedy Best-First Search Works:
At each step, the node with the lowest heuristic value (h(n)) is expanded.
The OPEN list contains nodes to be expanded, and the CLOSED list contains
nodes already expanded.
(Greedy Search)
algorithm.

Iteration 1:
Start at S.
Successors of S: A, B.
OPEN = [A, B], CLOSED = [S].
B has the lowest h(n) value, so expand B.
OPEN = [A], CLOSED = [S, B].
(Greedy Search)
algorithm.
Iteration 2:
Generate successors of B: E, F, A.
OPEN = [E, F, A], CLOSED = [S, B].
F has the lowest h(n) value, so
expand F.
OPEN = [E, A], CLOSED = [S, B, F].

Iteration 3:
Generate successors of F: I, G, E, A.
OPEN = [I, G, E, A], CLOSED = [S, B, F].
G is the goal, so the algorithm terminates
successfully.
OPEN = [I, E, A], CLOSED = [S, B, F, G].
(Greedy Search)
algorithm. Path Found:
The path is S →B →F →G.
Key Idea:
Greedy Search picks the node that
looks best now, but it may not
always find the shortest path.
It’s not optimal and can behave
incompletely if the heuristic is not
well-designed.

1. Introduction to Artificial Intelligence
1.Definition of Artificial Intelligence
2.How Does AI Work?
3.Advantages and Disadvantages of Artificial Intelligence
4.History of Artificial Intelligence
5.Types of Artificial Intelligence:
a.Weak AI vs. Strong AI
b.Reactive Machines
c.Limited Memory
d.Theory of Mind
e.Self-Awareness
6.Is Artificial Intelligence the Same as Augmented
Intelligence and Cognitive Computing?
7.Introduction to Machine Learning and Deep Learning
2. Machine Intelligence
1.Defining Intelligence
2.Components of Intelligence
3.Differences Between Human and Machine Intelligence
4.Agent and Environment in AI
5.Search Algorithms:
a.Uninformed Search Algorithms
b.Informed Search Algorithms:
i.Pure Heuristic Search
ii.Best-First Search Algorithm (Greedy Search)

T h a n k Y o u
M o d u l e 1 - C o m p l e t e d

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