This document provides information about the course Code 22MCA262. It includes details like CIE Marks (50), Teaching Hours (2:0:2), SEE Marks (50), Total Hours of Pedagogy (40), Total Marks (100), Credits (03), and Exam Hours (03). The document then discusses 5 key modules that will be covered in the course: 1) Introduction to AI and Production Systems, 2) Introduction to Artificial Intelligence, 3) History of AI, 4) Application of AI, and 5) Intelligent Robots. It provides overviews and examples for each module topic.

1. Basic information
Course Code 22MCA262
CIE Marks 50
Teaching Hours/Week (L:P:SDA) 2:0:2
SEE Marks 50
Total Hours of Pedagogy 40
Total Marks 100
Credits 03
Exam Hours 03

2. Module-1 INTRODUCTION TO Al AND PRODUCTION
SYSTEMS: Introduction to AI-Problem formulation, Problem
Definition -Production systems, Control strategies, Search strategies.
Problem characteristics, Production system characteristics -
Specialized productions system- Problem solving methods – Problem
graphs, Matching, Indexing and Heuristic functions -Hill Climbing-
Depth first and Breath first, Constraints satisfaction – Related
algorithms, Measure of performance and analysis of search
algorithms.

3. Introduction to Artificial Intelligence
Definition: Artificial Intelligence (AI) is a branch of Science which deals with helping machines to find solutions
to complex problems in a more human-like fashion.
This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a
computer friendly way.AI is the branch of computer science that attempts to approximate the results of human
reasoning by organizing and manipulating factual and heuristic knowledge.
AI is generally associated with Computer Science, but it has many important links with other fields such as
Math's, Psychology, Cognition, Biology and Philosophy, among many others
Areas of AI activity
 expert systems,
 natural language
 Understanding
 speech
 recognition, vision, and robotics.
Our ability to combine knowledge from all these fields will ultimately benefit our progress in the quest of
creating an intelligent artificial being.

4. The term was coined in 1956 by John McCarthy at the Massachusetts
Institute of Technology.
There are several programming languages that are known as AI
languages because they are used almost exclusively for AI applications.
The two most common are LISP (List Processing) and Prolog
(Programming Logic).
The greatest advances have occurred in the field of games playing. The
best computer chess programs are now capable of beating humans. In
May, 1997, an IBM super-computer called Deep Blue defeated world
chess champion Gary Kasparov in a chess match.

5. History of AI
Year Milestone / Innovation
1923
Karel Čapek plays named “Rossum’s Universal Robots, the first use of the word
“robot” in English.
1943 Foundations for neural networks laid.
1945 Isaac Asimov, a Columbia University alumni, use the term Robotics.
1956
John McCarthy first used the term Artificial Intelligence. Demonstration of the first
running AI program at Carnegie Mellon University.
1964
Danny Bobrow’s dissertation at MIT showed how computers could understand
natural language.
1969
Scientists at Stanford Research Institute Developed Shakey. A robot equipped with
locomotion and problem-solving.
1979
The world’s first computer-controlled autonomous vehicle, Stanford Cart, was
built.
1990 Significant demonstrations in machine learning
1997
The Deep Blue Chess Program beat the then world chess champion, Garry
Kasparov.

6. 2000
Interactive robot pets have become commercially available. MIT displays
Kismet, a robot with a face that expresses emotions.
2006
AI came into the Business world in the year 2006. Companies like
Facebook, Netflix, Twitter started using AI.
2012
Google has launched an Android app feature called “Google now”, which
provides the user with a prediction.
2018
The “Project Debater” from IBM debated complex topics with two master
debaters and performed exceptionally well.

7. Application of AI
1. Gaming
Non-player characters (NPCs): AI is often used to control the behavior of NPCs in games. These characters can
interact with players in a more realistic and dynamic way, adding to the immersion of the game.
Game design: AI is being used to design and balance game levels, as well as to generate new content such as
enemies and items. This helps developers create more diverse and interesting games with less effort.
Gameplay: AI can enhance gameplay by providing intelligent opponents for players to face off against. This
makes games more challenging and rewarding for players.
Virtual assistants: Some games include virtual assistants that can help players by providing information or
guidance during gameplay. These assistants use natural language processing (NLP) to understand and respond
to player requests.
Personalization: AI can personalize gameplay for individual players by adapting to their preferences and
playstyle. This helps keep players engaged and motivated to continue playing.
Predictive analytics: AI can be used to analyze player data and predict how they will behave in the future. This
can help developers design games that are more engaging and tailored to the preferences of specific player
segments.
Fraud detection: AI can be used to detect fraudulent activity in online games, such as cheating or hacking. This
helps maintain the integrity of the game and ensures that players have a fair and enjoyable experience.

8. Disadvantage
Cost: Developing AI technology can be expensive, which can be a barrier for
smaller studios or indie developers.
Complexity: Incorporating AI into a game can be complex and requires
specialized knowledge and expertise. This can make it difficult for developers
who are not familiar with AI to implement it in their games.
Limited intelligence: While AI can be very sophisticated, it is still limited by
its programming and the data it has been trained on. This means that AI may
not be able to respond appropriately to unexpected situations or player
actions.
Lack of creativity: AI can generate content and design levels, but it may not
be able to come up with truly creative or original ideas. This can limit the
potential of AI in the gaming industry.

9. 2. Speech recognition In the 1990s, computer speech recognition reached a practical
level for limited purposes. Thus United Airlines has replaced its keyboard tree for
flight information by a system using speech recognition of flight numbers and city
names. It is quite convenient
Transformer-based models: Transformer-based models, such as BERT and GPT, have been highly successful in natural
language processing tasks, and are now being applied to speech recognition AI.
End-to-end models: End-to-end models are designed to directly map speech signals to text, without the need for
intermediate steps. These models have shown promise in improving the accuracy and efficiency of speech recognition AI.
Multimodal models: Multimodal models combine speech recognition AI with other modalities, such as vision or touch, to
enable more natural and intuitive interactions between humans and machines.
Data augmentation: Data augmentation techniques, such as adding background noise or changing the speaking rate, can
be used to generate more training data for speech recognition AI models, improving their accuracy and robustness.

10. 3. Understanding natural language Just getting a sequence of words into a
computer is not enough. Parsing sentences is not enough either. The computer
has to be provided with an understanding of the domain the text is about, and
this is presently possible only for very limited domains.
How does natural language processing work?
• NLP enables computers to understand natural language as humans do.
Whether the language is spoken or written, natural language processing uses
artificial intelligence to take real-world input, process it, and make sense of it
in a way a computer can understand. Just as humans have different sensors --
such as ears to hear and eyes to see -- computers have programs to read and
microphones to collect audio. And just as humans have a brain to process that
input, computers have a program to process their respective inputs. At some
point in processing, the input is converted to code that the computer can
understand.

11. 4. Expert system
The expert systems are capable of −
 Advising
 Instructing and assisting human in decision making
 Demonstrating
 Deriving a solution
 Diagnosing
 Explaining
 Interpreting input
 Predicting results
 Justifying the conclusion
 Suggesting alternative options to a problem
Components of Expert Systems
The components of ES include −
 Knowledge Base
 Inference Engine
 User Interface

12. Applications of Expert System
The following table shows where ES can be applied.
Application Description
Design Domain Camera lens design, automobile design.
Medical Domain Diagnosis Systems to deduce cause of disease from observed data, conduction medical
operations on humans.
Monitoring Systems Comparing data continuously with observed system or with prescribed behavior such as
leakage monitoring in long petroleum pipeline.
Process Control Systems Controlling a physical process based on monitoring.
Knowledge Domain Finding out faults in vehicles, computers.
Finance/CommerceDetection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo
scheduling.

13. 5. 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
In the world of robotics, AI has proven to be a valuable asset in a variety of applications. From customer service to
manufacturing, AI has made its mark and continues to revolutionize the way we think about and interact with robots.
Let’s take a closer look at some of the key areas where AI is being used alongside robotics today.
Customer Service: AI-powered chatbots are becoming increasingly common in customer service applications.
Assembly: AI has proven to be an invaluable tool in this, especially in complex manufacturing industries such as
aerospace. With the help of advanced vision systems, AI can enable real-time course correction and can be used to
help a robot automatically learn the best paths for certain processes while in operation.
Packaging: to improve efficiency, accuracy and cost-effectiveness. By continuously refining and saving certain motions
made by robotic systems, AI helps make installing and moving robotic equipment easier for everyone.
Imaging: Across many industries — including assembly and logistics — accurate imaging is crucial. With the assistance
of AI,
Machine Learning: Machine learning is a powerful tool for robots. By exploring their surroundings, robots can learn
from their surrounding. find ways around obstacles and solve problems to complete tasks more efficiently. From home
robots like vacuum cleaners to manufacturing robots in factories, machine learning is helping robots become more
intelligent and adaptable in their work.

15. Subfields of Artificial Intelligence
Here, are some important subfields of Artificial Intelligence:
Machine Learning: Machine learning is the art of studying algorithms that learn from examples and experiences. Machine
learning is based on the idea that some patterns in the data were identified and used for future predictions.
Deep Learning: Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-
depth knowledge; it uses different layers to learn from the data. The depth of the model is represented by the number of
layers in the model. For instance, the Google LeNet model for image recognition counts 22 layers.
Natural Language Processing: A neural network is a group of connected I/O units where each connection has a weight
associated with its computer programs. It helps you to build predictive models from large databases. This model builds upon
the human nervous system. You can use this model to conduct image understanding, human learning, computer speech, etc.
Expert Systems: An expert system is an interactive and reliable computer-based decision-making system that uses facts and
heuristics to solve complex decision-making problems. It is also considered at the highest level of human intelligence. The
main goal of an expert system is to solve the most complex issues in a specific domain.
Fuzzy Logic: Fuzzy Logic is defined as a many-valued logic form that may have truth values of variables in any real number
between 0 and 1. It is the handle concept of partial truth. In real life, we may encounter a situation where we can’t decide
whether the statement is true or false

16. Neural networks
A neural network is a type of artificial intelligence model inspired by the structure and function of the human brain. It consists
of layers of interconnected nodes, or neurons, that can process input data and produce output signals. Each neuron receives
input signals from other neurons, processes them using an activation function, and sends the output to other neurons in the
next layer. Neural networks are trained using supervised learning techniques, where the weights of the connections between
the neurons are adjusted to minimize the error between the predicted and actual outputs. Neural networks are a powerful tool
in artificial intelligence and have been used in many applications, including image and speech recognition, natural language
processing, and autonomous systems.

17. Types of Artificial Intelligence
There are three main types of artificial intelligence: rule-based, decision tree, and neural
networks.
Narrow AI is a type of AI that helps you perform a dedicated task with intelligence.
General AI is a type of AI intelligence that can perform any intellectual task efficiently like
a human.
Rule-based AI is based on a set of pre-determined rules that are applied to an input data
set. The system then produces a corresponding output.
Decision tree AI is similar to rule-based AI in that it uses sets of pre-determined rules to
make decisions. However, the decision tree also allows for branching and looping to
consider different options.
Super AI is a type of AI that allows computers to understand human language and
respond in a natural way.
Robot intelligence is a type of AI that allows robots to have complex cognitive abilities,
including reasoning, planning, and learning.

18. Where is AI used? Examples
AI has broad applications-
• Artificial Intelligence is used to reduce or avoid repetitive
tasks. For instance, AI can repeat a task continuously,
without fatigue. AI never rests, and it is indifferent to the
task to carry out.
• Artificial intelligence improves an existing product. Before
the age of machine learning, core products were built
upon hard-code rules. Firms introduced artificial
intelligence to enhance the functionality of the product
rather than starting from scratch to design new products.
You can think of a Facebook image. A few years ago, you
had to tag your friends manually. Nowadays, with the
help of AI, Facebook gives you a friend’s
recommendation.
• AI is used in all industries, from marketing to supply
chain, finance, food-processing sector. According to a
McKinsey survey, financial services and high tech
communication are leading the AI fields.

19. Problem Solving
There are four things that are to be followed in order to solve a problem:
1. Define the problem. A precise definition that must include precise specifications of what
the initial situation(s) will be as well as what final situations constitute acceptable
solutions to the problem.
2. Problem must be analyzed. Some of the important features land up having an immense
impact on the appropriateness of various possible techniques for solving the problem.
• 3. Isolate and represent the task knowledge that is necessary to solve the problem.
• 4. Amongst the available ones choose the best problem-solving technique(s) and apply
the
• same to the particular problem

20. Search Algorithms in Artificial Intelligence
• Search algorithms are one of the most important areas of Artificial
Intelligence. This topic will explain all about the search algorithms in
AI.

21. Problem-solving agents:
In Artificial Intelligence, Search techniques are universal problem-
solving methods. Rational agents or Problem-solving agents in AI
mostly used these search strategies or algorithms to solve a specific
problem and provide the best result. Problem-solving agents are the
goal-based agents and use atomic representation. In this topic, we will
learn various problem-solving search algorithms.

22. Properties of Search Algorithms:
Following are the four essential properties of search algorithms to compare
the efficiency of these algorithms:
Completeness: A search algorithm is said to be complete if it guarantees to
return a solution if at least any solution exists for any random input.
Optimality: If a solution found for an algorithm is guaranteed to be the
best solution (lowest path cost) among all other solutions, then such a
solution for is said to be an optimal solution.
Time Complexity: Time complexity is a measure of time for an algorithm to
complete its task.
Space Complexity: It is the maximum storage space required at any point
during the search, as the complexity of the problem.

23. Types of search algorithms
• Based on the search problems we can classify the search algorithms
into uninformed (Blind search) search and informed search
(Heuristic search) algorithms.

25. Uninformed/Blind Search
• The uninformed search does not contain any domain knowledge such
as closeness, the location of the goal. It operates in a brute-force way
as it only includes information about how to traverse the tree and
how to identify leaf and goal nodes. Uninformed search applies a way
in which search tree is searched without any information about the
search space like initial state operators and test for the goal, so it is
also called blind search. It examines each node of the tree until it
achieves the goal node.

26. It can be divided into five main types:
 Breadth-first search
 Uniform cost search
 Depth-first search
 Iterative deepening depth-first search
 Bidirectional Search

27. Informed Search
• Informed search algorithms use domain knowledge. In an informed search,
problem information is available which can guide the search. Informed
search strategies can find a solution more efficiently than an uninformed
search strategy. Informed search is also called a Heuristic search
• Informed search can solve much complex problem which could not be
solved in another way.
An example of informed search algorithms is a traveling salesman problem.
Greedy Search
A* Search

28. What is Heuristic Search – Techniques & Hill
Climbing in AI

29. What is a Heuristic Search?
• A Heuristic is a technique to solve a problem faster than classic methods, or to find an
approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade
one of optimality, completeness, accuracy, or precision for speed.
• A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it
evaluates the available information and makes a decision on which branch to follow.
• It does so by ranking alternatives. The Heuristic is any device that is often effective but will not
guarantee work in every case.
• So why do we need heuristics? One reason is to produce, in a reasonable amount of time, a
solution that is good enough for the problem in question. It doesn’t have to be the best- an
approximate solution will do since this is fast enough.
• Most problems are exponential. Heuristic Search let us reduce this to a rather polynomial
number. We use this in AI because we can put it to use in situations where we can’t find known
algorithms.
• We can say Heuristic Techniques are weak methods because they are vulnerable to combinatorial
explosion.

31. Hill Climbing Algorithm in Artificial Intelligence
• Hill climbing algorithm is a local search algorithm which continuously moves in
the direction of increasing elevation/value to find the peak of the mountain or
best solution to the problem. It terminates when it reaches a peak value where
no neighbor has a higher value.
• Hill climbing algorithm is a technique which is used for optimizing the
mathematical problems. One of the widely discussed examples of Hill climbing
algorithm is Traveling-salesman Problem in which we need to minimize the
distance traveled by the salesman.
• It is also called greedy local search as it only looks to its good immediate neighbor
state and not beyond that.
• A node of hill climbing algorithm has two components which are state and value.
• Hill Climbing is mostly used when a good heuristic is available.
• In this algorithm, we don't need to maintain and handle the search tree or graph
as it only keeps a single current st

32. Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
• Generate and Test variant: Hill Climbing is the variant of Generate
and Test method. The Generate and Test method produce feedback
which helps to decide which direction to move in the search space.
• Greedy approach: Hill-climbing algorithm search moves in the
direction which optimizes the cost.
• No backtracking: It does not backtrack the search space, as it does
not remember the previous states.

33. State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the
hill-climbing algorithm which is showing a graph between
various states of algorithm and Objective function/Cost.
.
Local Maximum: Local maximum is a state which is better than
its neighbor states, but there is also another state which is higher
than it.
Global Maximum: Global maximum is the best possible state of
state space landscape. It has the highest value of objective
function.
Current state: It is a state in a landscape diagram where an agent
is currently present.
Flat local maximum: It is a flat space in the landscape where all
the neighbor states of current states have the same value.
Shoulder: It is a plateau region which has an uphill edge.

34. Types of Hill Climbing Algorithm:
• Simple hill Climbing:
• Steepest-Ascent hill-climbing:
• Stochastic hill Climbing:

35. Simple Hill Climbing:
Simple hill climbing is the simplest way to implement a hill climbing
algorithm. It only evaluates the neighbor node state at a time and
selects the first one which optimizes current cost and set it as a
current state. It only checks it's one successor state, and if it finds
better than the current state, then move else be in the same state.
This algorithm has the following features:
Less time consuming
Less optimal solution and the solution is not guaranteed

36. Algorithm for Simple Hill Climbing:
Step 1: Evaluate the initial state, if it is goal state then return success and
Stop.
Step 2: Loop Until a solution is found or there is no new operator left to
apply.
Step 3: Select and apply an operator to the current state.
Step 4: Check new state:
If it is goal state, then return success and quit.
Else if it is better than the current state then assign new state as a current state.
Else if not better than the current state, then return to step2.
Step 5: Exit.

37. 2. Steepest-Ascent hill climbing:
The steepest-Ascent algorithm is a variation of simple hill climbing algorithm. This algorithm examines all the neighboring
nodes of the current state and selects one neighbor node which is closest to the goal state. This algorithm consumes
more time as it searches for multiple neighbors
Algorithm for Steepest-Ascent hill climbing:
Step 1: Evaluate the initial state, if it is goal state then return success and stop, else make current state as initial state.
Step 2: Loop until a solution is found or the current state does not change.
Let SUCC be a state such that any successor of the current state will be better than it.
For each operator that applies to the current state:
Apply the new operator and generate a new state.
Evaluate the new state.
If it is goal state, then return it and quit, else compare it to the SUCC.
If it is better than SUCC, then set new state as SUCC.
If the SUCC is better than the current state, then set current state to SUCC.
Step 5: Exit.

38. 3. Stochastic hill climbing:
• Stochastic hill climbing does not examine for all its neighbor before
moving. Rather, this search algorithm selects one neighbor node at
random and decides whether to choose it as a current state or
examine another state.

39. Hill Climbing in Artifical Intelligence

40. • Let’s discuss some of the features of this algorithm (Hill Climbing):
• It is a variant of the generate-and-test algorithm
• It makes use of the greedy approach
• This means it keeps generating possible solutions until it finds the
expected solution, and moves only in the direction which optimizes the
cost function for it.

41. Types of Hill Climbing in AI

42. Simple Hill Climbing- This examines one neighboring
node at a time and selects the first one that optimizes
the current cost to be the next node.
Steepest Ascent Hill Climbing- This examines all
neighboring nodes and selects the one closest to the
solution state.
This selects a neighboring node at random and
decides whether to move to it or examine another.