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Department of Computer Science & Engineering
School of Engineering & Technology
Manav Rachna International Institute of Research and Studies
Faridabad (Haryana)
Subject: Artificial Intelligence for Engineers
Course Code: BCS-100A
Periods Assigned: Lecture: 2, Tutorial: 0 Credits: 2.0
Marks Assigned Theory: Internal: 100, External: 100, Total: 200
Academic Session: Jan –June 2025
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Course Outcomes:
The Students will be able to-
BCS-100A.1 understand evolution of Artificial Intelligence.
BCS-100A.2 Familiarize with artificial intelligence problems and their
formulations.
BCS-100A.3 understand Intelligent system, Agents & its environment.
BCS-100A.4 understand applications of artificial intelligence.
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Unit-1: AI Introduction, Background and History
1.1 Introduction to AI
1.2 Foundations of AI
1.3 AI Evolution
1.4 Introduction to AI programming languages
Unit-2: AI Problem Formulation
2.1 AI problem formulation
2.2 Problem characteristics
2.3 Production System
2.4 Production System characteristics
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Unit 3: Intelligent System & Agents
3.1 Introduction to intelligence system
3.2 Types of Intelligence
3.3 Difference between Human and Machine learning
3.4 Introduction to Agent & environment
3.5 Structure of Intelligent Agent
3.6 Nature and Properties of Environment.
Unit-4: AI Applications
4.1 Robotics
4.2 Natural Language Processing
4.3Computer Vision
4.4 Health Care
4.5 Education
4.6 Expert System
5. Artificial Intelligence (1991)
Elaine Rich & Kevin Knight, Second Ed, Tata McGraw Hill
Artificial Intelligence : A modern Approach
By Russel & Norvig, Second Ed, Prentice Hall.
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6. What is AI?
The foundations of AI
A brief history of AI
The state of the art
Introductory problems
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7. To automate the task.
Automatons are abstract models of machines that perform
computations on an input by moving through a series of states or
configurations. At each state of the computation a transition
function determines the next configuration on the basis of finite
portion of the present configuration. As a result once the
computation reaches an accepting configuration it accepts that
input. The most general and powerful automata is the Turing
machine.
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10. AI is concerned with designing Intelligence in
Artificial System/artefacts ( coined By John
McCarthy in 1956)
AI is the study of how to make computers make
things which at the moment people do better.
Examples: Speech recognition, Smell, Face,
Object, Intuition, Inference, Learning new skills,
Decision making, Abstract thinking
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11. Weak artificial intelligence is a form of AI
specifically designed to be focused on a narrow
task and to seem very intelligent at it.
Strong AI, in which an AI is capable of all and any
cognitive functions that a human may have, and
is in essence no different than a real human
mind.
Weak AI is never taken as a general intelligence
but rather a construct designed to be intelligent
in the narrow task that it is assigned to.
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12. Weak AI simply acts upon and is bound by the
rules imposed on it and it could not go beyond
those rules. A good example of weak AI are
characters in a computer game that act
believably within the context of their game
character, but are unable to do anything
beyond that.
Weak artificial intelligence is also known as
narrow artificial intelligence.
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13. A very good example of a weak AI is Apple's Siri, which has the
Internet behind it serving as a powerful database. Siri seems
very intelligent, as it is able to hold a conversation with actual
people, even giving snide remarks and a few jokes, but actually
operates in a very narrow, predefined manner. However, the
"narrowness" of its function can be evidenced by its inaccurate
results when it is engaged in conversations that it is not
programmed to respond to.
Robots used in the manufacturing process can also seem very
intelligent because of the accuracy and the fact that they are
doing very complicated actions that could seem
incomprehensible to a normal human mind. But that is the
extent of their intelligence.
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14. Artificial Intelligence can be divided in various types, there are mainly two types of main
categorization which are based on capabilities and based on functionally of AI. Following is
flow diagram which explain the types of AI.
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15. 1. Weak AI or Narrow AI:
Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. The most
common and currently available AI is Narrow AI in the world of Artificial Intelligence.
Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task.
Hence it is also termed as weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits.
Apple Siriis a good example of Narrow AI, but it operates with a limited predefined range of
functions.
IBM's Watson supercomputer also comes under Narrow AI, as it uses an Expert system approach
combined with Machine learning and natural language processing.
Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce site, self-
driving cars, speech recognition, and image recognition.
2. General AI:
General AI is a type of intelligence which could perform any intellectual task with efficiency like a
human.
The idea behind the general AI to make such a system which could be smarter and think like a human
by its own.
Currently, there is no such system exist which could come under general AI and can perform any task
as perfect as a human.
The worldwide researchers are now focused on developing machines with General AI.
As systems with general AI are still under research, and it will take lots of efforts and time to develop
such systems.
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16. 3. Super AI:
Super AI is a level of Intelligence of Systems at which machines could surpass human
intelligence, and can perform any task better than human with cognitive properties. It is
an outcome of general AI.
Some key characteristics of strong AI include capability include the ability to think, to
reason, solve the puzzle, make judgments, plan, learn, and communicate by its own.
Super AI is still a hypothetical concept of Artificial Intelligence. Development of such
systems in real is still world changing task.
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17. 1. Reactive Machines
Purely reactive machines are the most basic types of Artificial Intelligence.
Such AI systems do not store memories or past experiences for future actions.
These machines only focus on current scenarios and react on it as per possible best action.
IBM's Deep Blue system is an example of reactive machines ( chess playing system defeated
human in 1997.
Google's AlphaGo is also an example of reactive machines ( computer program that uses machine
learning to play the board game Go).
2. Limited Memory
Limited memory machines can store past experiences or some data for a short period of time.
These machines can use stored data for a limited time period only.
Self-driving cars are one of the best examples of Limited Memory systems. These cars can
store recent speed of nearby cars, the distance of other cars, speed limit, and other information
to navigate the road.
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18. 3. Theory of Mind
Theory of Mind AI should understand the human emotions, people, beliefs, and
be able to interact socially like humans.
This type of AI machines are still not developed, but researchers are making lots
of efforts and improvement for developing such AI machines.
4. Self-Awareness
Self-awareness AI is the future of Artificial Intelligence. These machines will be
super intelligent, and will have their own consciousness, sentiments, and self-
awareness.
These machines will be smarter than human mind.
Self-Awareness AI does not exist in reality still and it is a hypothetical concept.
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21. Not content to have a program correctly
solving a problem.
More concerned with comparing its reasoning
steps to traces of human solving the same
problem.
Requires testable theories of the workings of
the human mind: cognitive science.
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22. Formal logic provides a precise notation and
rules for representing and reasoning with all
kinds of things in the world.
Obstacles:
Informal knowledge representation.
Computational complexity and resources.
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23. Acting so as to achieve one’s goals, given one’s
beliefs.
Does not necessarily involve thinking.
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24. 1943-1955: Gestation
1956: Birth
1952-1969: Great Expectations
1966-1973: Reality
1969-1979: Knowledge is Power
1980-present: AI and Industry
1986-present: The Return of Neural Networks 1987-
present: AI Becomes a Science
1995-present: Intelligent Agents
https://www.youtube.com/watch?v=056v4OxKwlI
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25. The gestation of AI (1943 1956):
1943: McCulloch & Pitts: Boolean circuit model of brain.
1950: Turing’s “Computing Machinery and
Intelligence”.
1956: McCarthy’s name “Artificial Intelligence” adopted.
Early enthusiasm, great expectations (1952
1969):
Early successful AI programs: Samuel’s checkers,
Newell & Simon’s Logic Theorist, Gelernter’s Geometry
Theorem Prover.
Robinson’s complete algorithm for logical reasoning.
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26. A dose of reality (1966 1974):
AI discovered computational complexity.
Neural network research almost disappeared after
Minsky & Papert’s book in 1969.
Knowledge-based systems (1969 1979):
1969: DENDRAL by Buchanan et al..
1976: MYCIN by Shortliffle.
1979: PROSPECTOR by Duda et al..
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27. AI becomes an industry (1980 1988):
Expert systems industry booms.
1981: Japan’s 10-year Fifth Generation project.
The return of NNs and novel AI (1986 present):
Mid 80’s: Back-propagation learning algorithm
reinvented.
Expert systems industry busts.
1988: Resurgence of probability.
1988: Novel AI (ALife, GAs, Soft Computing, …).
1995: Agents everywhere.
2003: Human-level AI back on the agenda.
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28. Mundane Tasks:
◦ Perception
Vision
Speech
◦ Natural Languages
Understanding
Generation
Translation
◦ Common sense reasoning
◦ Robot Control
Formal Tasks
◦ Games : chess, checkers etc
◦ Mathematics: Geometry, logic, Proving properties of programs
Expert Tasks:
◦ Engineering ( Design, Fault finding, Manufacturing planning)
◦ Scientific Analysis
◦ Medical Diagnosis
◦ Financial Analysis
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29. Intelligence requires Knowledge
Knowledge posesses less desirable properties such as:
◦ Voluminous
◦ Hard to characterize accurately
◦ Constantly changing
◦ Differs from data that can be used
AI technique is a method that exploits knowledge that should
be represented in such a way that:
◦ It can be understood by people who must provide it
◦ It can be easily modified to correct errors.
◦ It can be used in variety of situations
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30. Computer beats human in a chess game.
Computer-human conversation using speech
recognition.
Expert system controls a spacecraft.
Robot can walk on stairs and hold a cup of water.
Language translation for web pages.
Home appliances use fuzzy logic.
......
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31. Autonomous planning and scheduling (NASA)
Game playing
Autonomous control (minivan steering)
Diagnosis (medicine)
Logistics planning (Gulf war)
Robotics (surgery)
Language understanding and generation
(translation, dialogue)
Problem solving (crossword puzzles)
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32. Introduction to LISP
John McCarthy invented LISP in 1958, shortly after the development of FORTRAN. It was first
implemented by Steve Russell on an IBM 704 computer.
It is particularly suitable for Artificial Intelligence programs, as it processes symbolic information
effectively.
Common Lisp originated, during the 1980s and 1990s, in an attempt to unify the work of several
implementation groups that were successors to Maclisp, like ZetaLisp and NIL (New Implementation of
Lisp) etc.
It serves as a common language, which can be easily extended for specific implementation.
Programs written in Common LISP do not depend on machine-specific characteristics, such as word length
etc.
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33. Introduction to Prolog
Prolog as the name itself suggests, is the short form of LOGical PROgramming. It is a logical and
declarative programming language. Before diving deep into the concepts of Prolog, let us first
understand what exactly logical programming is.
Logic Programming is one of the Computer Programming Paradigm, in which the program
statements express the facts and rules about different problems within a system of formal logic.
Here, the rules are written in the form of logical clauses, where head and body are present. For
example, H is head and B1, B2, B3 are the elements of the body. Now if we state that “H is true,
when B1, B2, B3 all are true”, this is a rule. On the other hand, facts are like the rules, but without
any body. So, an example of fact is “H is true”.
Some logic programming languages like Datalog or ASP (Answer Set Programming) are known as
purely declarative languages. These languages allow statements about what the program should
accomplish. There is no such step-by-step instruction on how to perform the task. However, other
languages like Prolog, have declarative and also imperative properties. This may also include
procedural statements like “To solve the problem H, perform B1, B2 and B3”.
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34. • Python works on different platforms (Windows, Mac,
Linux, Raspberry Pi, etc).
• Python has a simple syntax similar to the English
language.
• Python has syntax that allows developers to write
programs with fewer lines than some other
programming languages.
• Python runs on an interpreter system, meaning that
code can be executed as soon as it is written. This
means that prototyping can be very quick.
• Python can be treated in a procedural way, an
object-oriented way or a functional way.
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35. Python is widely considered the preferred language for AI development due to its extensive library ecosystem, particularly for
machine learning, its easy-to-read syntax, strong community support, platform independence, flexibility, and a low barrier to
entry, making it accessible to both beginners and experienced developers alike.
Key advantages of Python for AI:
• Rich Libraries:
• Python boasts a vast collection of specialized libraries like NumPy, Pandas, Scikit-learn, and TensorFlow, which significantly
simplify complex AI tasks like data manipulation, model building, and visualization.
• Readability:
• Python's simple and clear syntax makes code easy to understand, which is crucial for collaboration and maintaining complex AI
projects.
• Visualization Capabilities:
• Libraries like Matplotlib and Seaborn provide powerful data visualization tools, which are essential for analyzing and
interpreting AI results.
• Low Entry Barrier:
• Python's beginner-friendly syntax makes it accessible to individuals with less programming experience, allowing them to enter
the field of AI development.
• Open Source:
• Python is an open-source language, meaning it is freely available and can be customized by the community.
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36. • Community Support:
• A large and active community of Python developers provides extensive documentation,
tutorials, and support forums, making it easier to find solutions to problems.
• Platform Independence:
• Python code can run seamlessly across different operating systems like Windows,
macOS, and Linux without major modifications.
• Rapid Prototyping:
• The ease of use allows developers to quickly experiment with different AI algorithms
and models, facilitating faster development cycles.
• Flexibility:
• Python supports multiple programming paradigms, allowing developers to choose the
most suitable approach for their AI project.
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37. The primary advantage of using R for Artificial Intelligence (AI) is its extensive collection of specialized
packages and libraries specifically designed for robust statistical analysis and data manipulation,
making it highly effective for developing complex machine learning models and extracting
meaningful insights from data, especially when dealing with intricate statistical problems.
Key benefits of using R for AI:
• Powerful Statistical Capabilities:
• R was originally built as a statistical computing language, providing a wide range of statistical tests,
models, and techniques that are crucial for data analysis in AI applications.
• Rich Ecosystem of Packages:
• A vast array of packages like "caret", "glmnet", "randomForest", and "dplyr" are readily available in R,
allowing users to easily implement various machine learning algorithms and data processing tasks.
• Data Visualization Strengths:
• R offers exceptional data visualization capabilities through packages like "ggplot2", enabling users to
create high-quality plots and graphs for better understanding of patterns in data.
• Community Support:
• A large and active community of R users contributes to the development of packages and provides
support for troubleshooting and learning new techniques.
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38. • Community Support:
• A large and active community of R users contributes to the development of packages and
provides support for troubleshooting and learning new techniques.
• Reproducible Research:
• R promotes reproducibility in data analysis by allowing users to document their code and
analysis steps comprehensively.
When to use R for AI:
• Complex Statistical Modeling:
• When your AI project requires advanced statistical techniques like time series analysis,
survival analysis, or multilevel modeling, R's statistical power shines.
• Exploratory Data Analysis (EDA):
• R's data manipulation capabilities are excellent for cleaning, transforming, and exploring large
datasets before building machine learning model.
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