Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for the use of students of BIT
Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair use Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair use’ of any
such copyrighted material
• The material contained in this website is distributed without
profit for research and educational purposes only

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Course Information

Course Title & Basics of Intelligent Computing IT201

Course ID
Pre-Requisite Basic Computer Programming Concepts and
Mathematics
Co-requisite: MATLAB, PYTHON
Credits L:3 T: 0 P: 0
Number of Modules Five
Number of Lectures Eight Lectures/Module
Teacher In charge Dr. Subrajeet Mohapatra, Assistant Professor,
Department of Computer Science & Engineering,
BIT Mesra, Ranchi, Jharkhand

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Suggested Books
Text Books • Rich Elaine, Knight Kevin, Nair S. B. Artificial Intelligence, 3 rd
Edition, Tata Mc. Graw Hill.
• Padhy N. P., Simon S. P. Soft Computing: With MATLAB
Programming, Oxford University Press, 2015.
• Buyya Raj Kumar, Vecchiola Christian & Selvi S. Thamarai,
Mastering Cloud Computing, McGraw Hill Publication, New Delhi,
2013
• Madisetti Vijay and Bahga Arshdeep, Internet of Things (A Hands-
on-Approach), 1st Edition, VPT, 2014.
Reference • Konar Amit, Computational Intelligence: Principles, Techniques and
Books Applications, Springer.
• Shivanandam and Deepa, Principles of Soft Computing, 2nd Edition,
John Wiley and Sons, 2011.
• Raj Pethuru and Raman Anupama C., The Internet of Things:
Enabling Technologies, Platforms, and Use Cases, CRC Press.

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Traditional Computing
Computation is any type of calculation that includes both
arithmetical and non-arithmetical steps which follows a
well defined model. e.g. Algorithm of a well defined
problem.
Input
Output
Characteristics of an algorithm Finiteness
Definiteness
Unambiguous

Traditional computing can be of three types : Serial

Computing, Parallel Computing and Distributed
Computing.
Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Traditional Computing

Data

Computation Result

Algorithm

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Characteristics of Traditional Computing
• Needs a exactly stated analytic (mathematical) model
• Relies on binary logic and crisp system
• Works on exact input data and produces precise output
• Performs sequential computations
• Results are reliable and consistent

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Intelligent Computing
• Intelligent Computing refers to the ability of a computer/machines
to learn specific task from data or experimental observations.

Data

Computation Outcome

Knowledge

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Characteristics of Intelligent Computing
• Does not needs a exactly stated mathematical model
• Liberal of inexactness, uncertainty, partial truth and
approximation
• Based on multivalued logic and probabilistic reasoning
• Produces approximate results
• Stochastic in nature
• Emerges its own programs

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Traditional vs. Intelligent Computing
Dimension Traditional Computing Intelligent Computing

Algorithmic (Instructions are Knowledge base (Includes

Processing stored in memory and conceptualization & Symbolic
executed in sequential way) representation)
Nature of Must be complete Can be incomplete (Partial input
Input acceptable)
Nature of Solution is guaranteed Solution is not guaranteed
Solution
Type of Reliable & consistent May not be reliable & consistent
Results

Problem Programmer gives specific Programmer does not gives specific

Solving instructions for problem instructions for problem solving
Instruction solving (Learns itself from examples)
Reasoning None Limited, but improving
Capability

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Trends in Intelligent Computing
• Artificial Intelligence
• Soft Computing
• Machine Learning
• Deep Learning
• Data Science
• Cloud Computing

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Trends in Intelligent Computing
• Artificial Intelligence (AI) :-
• The study of the modeling of human mental functions by
computer programs.
• Any code, technique or algorithm that enable machines to
mimic, develop or demonstrate the human cognition or
behavior is AI.
• Machine Learning (ML) :-
• Machine learning is the science of getting computers to
act without being explicitly programmed.
• ML is a subset of AI which uses statistical methods to
enable machines to improve with experience.
• Algorithms based on ML are designed in such a way that
they can learn and improve over time when exposed to
new data.
Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Trends in Intelligent Computing
• Deep Learning (DL) :-
• DL is a subfield of machine learning concerned with
algorithms inspired by the structure and function of the
brain called artificial neural networks.
• Data Science (DS) :-
• Data science is a concept used to tackle big data and
includes data cleansing, preparation, and analysis.
• A data scientist gathers data from multiple sources and
applies machine learning, predictive analytics, and
sentiment analysis to extract critical information from the
collected data sets.
• They understand data from a business point of view and
can provide accurate predictions and insights that can be
used to power critical business decisions.

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Trends in Intelligent Computing
• Cloud Computing :-
• Cloud computing is a new paradigm for the dynamic
provisioning of computing services supported by state-of-the-
art data centers employing virtualization technologies for
consolidation and effective utilization of resources.

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Cousins of Artificial Intelligence

Image Credit: Artem Opermann, Medium.com [1]

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Artem Opermann (2019, October). Learn the Difference between the most popular
Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning.
https://towardsdatascience.com/artificial-intelligence-vs-machine-learning-vs-
deep-learning-2210ba8cc4ac
2. Kashyap Raval (2017, April). Q/A System—Deep learning(1/2).
https://medium.com/@kashyapraval/qna-system-deep-learning-1-2-
4aa20c017042
3. Buyya Raj Kumar, Vecchiola Christian & Selvi S. Thamarai, Mastering Cloud
Computing, McGraw Hill Publication, New Delhi, 2013
4. Pratihar D. K., Soft Computing : Fundamentals and Applications (2nd Ed.)
(Narosa, 2013)
5. Buyya Raj Kumar, Vecchiola Christian & Selvi S. Thamarai, Mastering Cloud
Computing, McGraw Hill Publication, New Delhi, 2013
6. Aurelien Geron, Hands–On Machine Learning with Scikit–Learn and TensorFlow,
O Reilly, 2017
7. Saransh Mehta, Hands-On Deep Learning Architectures with Python, Packt, 2019

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Thank You

Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Copyright Information
• The course materials is intended only for the use of students of BIT
Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• Students violating the above policies may be subject to student
conduct proceedings

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Disclaimer & Fair use Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair use’ of any
such copyrighted material
• The material contained in this website is distributed without
profit for research and educational purposes only

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this website is distributed without
profit for research and educational purposes only

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Course Information

Course Title & Basics of Intelligent Computing IT201

Course ID
Pre-Requisite Basic Computer Programming Concepts and
Mathematics
Co-requisite: MATLAB, PYTHON
Credits L:3 T: 0 P: 0
Number of Modules Five
Number of Lectures Eight Lectures/Module
Teacher In charge Dr. Subrajeet Mohapatra, Assistant Professor,
Department of Computer Science & Engineering,
BIT Mesra, Ranchi, Jharkhand

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Suggested Books
Text Books • Rich Elaine, Knight Kevin, Nair S. B. Artificial Intelligence, 3rd
Edition, Tata Mc. Graw Hill.
• Padhy N. P., Simon S. P. Soft Computing: With MATLAB
Programming, Oxford University Press, 2015.
• Buyya Raj Kumar, Vecchiola Christian & Selvi S. Thamarai,
Mastering Cloud Computing, McGraw Hill Publication, New Delhi,
2013
• Madisetti Vijay and Bahga Arshdeep, Internet of Things (A Hands-
on-Approach), 1st Edition, VPT, 2014.
Reference • Konar Amit, Computational Intelligence: Principles, Techniques and
Books Applications, Springer.
• Shivanandam and Deepa, Principles of Soft Computing, 2nd Edition,
John Wiley and Sons, 2011.
• Raj Pethuru and Raman Anupama C., The Internet of Things:
Enabling Technologies, Platforms, and Use Cases, CRC Press.

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Traditional Computing
Computation is any type of calculation that includes both
arithmetical and non-arithmetical steps which follows a
well defined model. e.g. Algorithm of a well defined
problem.
Input
Output
Characteristics of an algorithm Finiteness
Definiteness
Unambiguous

Traditional computing can be of three types : Serial

Computing, Parallel Computing and Distributed
Computing.

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Traditional Computing

Data

Computation Result

Algorithm

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Characteristics of Traditional Computing
• Needs a exactly stated analytic (mathematical) model
• Relies on binary logic and crisp system
• Works on exact input data and produces precise output
• Performs sequential computations
• Results are reliable and consistent

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Intelligent Computing
• Intelligent Computing refers to the ability of a computer/machines
to learn specific task from data or experimental observations.

Data

Computation Outcome

Knowledge

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Characteristics of Intelligent Computing
• Does not needs a exactly stated mathematical model
• Liberal of inexactness, uncertainty, partial truth and
approximation
• Based on multivalued logic and probabilistic reasoning
• Produces approximate results
• Stochastic in nature
• Emerges its own programs

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Traditional vs. Intelligent Computing
Dimension Traditional Computing Intelligent Computing

Algorithmic (Instructions are Knowledge base (Includes

Processing stored in memory and conceptualization & Symbolic
executed in sequential way) representation)
Nature of Must be complete Can be incomplete (Partial input
Input acceptable)
Nature of Solution is guaranteed Solution is not guaranteed
Solution
Type of Reliable & consistent May not be reliable & consistent
Results

Problem Programmer gives specific Programmer does not gives specific

Solving instructions for problem instructions for problem solving
Instruction solving (Learns itself from examples)
Reasoning None Limited, but improving
Capability

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Trends in Intelligent Computing
• Artificial Intelligence
• Soft Computing
• Machine Learning
• Deep Learning
• Data Science
• Cloud Computing

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Trends in Intelligent Computing
• Artificial Intelligence (AI) :-
• The study of the modeling of human mental functions by
computer programs.
• Any code, technique or algorithm that enable machines to
mimic, develop or demonstrate the human cognition or
behavior is AI.
• Machine Learning (ML) :-
• Machine learning is the science of getting computers to
act without being explicitly programmed.
• ML is a subset of AI which uses statistical methods to
enable machines to improve with experience.
• Algorithms based on ML are designed in such a way that
they can learn and improve over time when exposed to
new data.
Module -1, Lecture-1 Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Trends in Intelligent Computing
• Deep Learning (DL) :-
• DL is a subfield of machine learning concerned with
algorithms inspired by the structure and function of the
brain called artificial neural networks.
• Data Science (DS) :-
• Data science is a concept used to tackle big data and
includes data cleansing, preparation, and analysis.
• A data scientist gathers data from multiple sources and
applies machine learning, predictive analytics, and
sentiment analysis to extract critical information from the
collected data sets.
• They understand data from a business point of view and
can provide accurate predictions and insights that can be
used to power critical business decisions.

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Trends in Intelligent Computing
• Cloud Computing :-
• Cloud computing is a new paradigm for the dynamic
provisioning of computing services supported by state-of-the-
art data centers employing virtualization technologies for
consolidation and effective utilization of resources.

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Cousins of Artificial Intelligence

Image Credit: Artem Opermann, Medium.com [1]

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Artem Opermann (2019, October). Learn the Difference between the most popular
Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning.
https://towardsdatascience.com/artificial-intelligence-vs-machine-learning-vs-
deep-learning-2210ba8cc4ac
2. Kashyap Raval (2017, April). Q/A System—Deep learning(1/2).
https://medium.com/@kashyapraval/qna-system-deep-learning-1-2-
4aa20c017042
3. Buyya Raj Kumar, Vecchiola Christian & Selvi S. Thamarai, Mastering Cloud
Computing, McGraw Hill Publication, New Delhi, 2013
4. Pratihar D. K., Soft Computing : Fundamentals and Applications (2nd Ed.)
(Narosa, 2013)
5. Buyya Raj Kumar, Vecchiola Christian & Selvi S. Thamarai, Mastering Cloud
Computing, McGraw Hill Publication, New Delhi, 2013
6. Aurelien Geron, Hands–On Machine Learning with Scikit–Learn and TensorFlow,
O Reilly, 2017
7. Saransh Mehta, Hands-On Deep Learning Architectures with Python, Packt, 2019

Module -1, Lecture-1 Subrajeet Mohapatra

Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Module -1, Lecture-1 Subrajeet Mohapatra
Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-2

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this website is distributed without
profit for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Intelligent Computing
• Intelligent Computing refers to the ability of a computer/machines
to learn specific task from data or experimental observations.

Data

Computation Outcome

Knowledge

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Necessity of Intelligent Computing
• Some real time problems cannot be a exactly stated using a
mathematical model
• Many problems cannot be solved using algorithmic processes
• Exhaustive search based problems are subjected to delayed
convergence
• Constraint based optimization problems can be better solved
using heuristic approach
• Decision making under uncertainty is difficult using crisp
reasoning approach of traditional computing

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
What is Intelligence
Intelligence
• The capacity to learn and solve problems (Webster's
Dictionary)
• The ability to use memory, knowledge, experience,
understanding, reasoning, imagination and judgment in order
to solve problems and adapt to new situations (All Words
Dictionary)
• The ability to learn, understand and make judgments or have
opinions that are based on reason (Cambridge Advance
Learner's Dictionary)
• A very general mental capability that, among other things,
involves the ability to reason, plan, solve problems, think
abstractly, comprehend complex ideas, learn quickly and
learn from experience ( Gottfredson)
Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
What’s involved in Intelligence
• Ability to interact with the real world
• To perceive and act
• Learning and Adaptation
• Continuously learn from environment and adapt to changes
• Biologically our brain (neuron models) always get updated
• Reasoning and Planning
• Reasoning is the process of deriving logical conclusion
and making predictions from available knowledge, facts,
and beliefs.
• Planning is about decision making tasks performed by the
robots or computer programs to achieve a specific goal.

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
What’s involved in Intelligence

Image Credit: verywellmind.com

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
What is a Artificial Intelligence (AI)?
• Intelligence demonstrated by machines, unlike the natural
intelligence displayed by humans and animals (Wikipedia)
• Machines (or computers) that mimic "cognitive" functions that
humans associate with the human mind, such as "learning"
and "problem solving (Russel & Norvig)
• The study of how to make computers thinks at which, at the
movement, people are better (Rich & Knight)
• Art of creating machines that perform functions that require
intelligence when performed by people (Kurzweil)
• Branch of computer science that is concerned with the
automation of intelligent behaviour (Luger and Stubblefield).
Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Areas of AI and their Inter-Dependencies
Knowledge
Search Logic Representation

Machine
Planning
Learning

Expert
NLP Vision Robotics Systems
Image Credit: Dr. Pushpak Bhattacharya, IIT Bombay

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Relationship to other Disciplines
• AI is a young discipline
• Diverse disciplines i.e. philosophy, evolutionary biology,
neurobiology, economics, political science, psychology,
sociology, anthropology, control engineering, and others have
been studying intelligence much longer

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Relationship to other Disciplines
Discipline Conceptualization
Mathematics Logic, Search, Analysis of Search Algorithms
Economics Decision Theory, Principles of Rational Behavior
Philosophy Foundation of AI (Is AI possible?), Knowledge
Representation, Logic

Brain Science Learning Models, Artificial Neural Networks

Psychology Understand the Psychology of Human needs,
Develop behavioristic insights into AI Programs
Physics Learning, Information Theory , Entropy, Robotics
Computer Science Creating Intelligent Machines

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Different Types of Artificial Intelligence
• Definitions of AI can be organized into four categories
• Systems that think like humans
• Systems that act like humans
• Systems that think rationally
• Systems that act rationally

Acting Humanly: Turing Machine Approach

Thinking Humanly: The Cognitive Modeling Approach
Thinking Rationally: The Laws of Thought Approach
Acting Rationally: The Rational Agent Approach

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Different Types of Artificial Intelligence

Modern AI focuses on developing systems that act rationally

(Rational Agents)
Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Different Flavour's of Artificial Intelligence?
Systems that think like humans Systems that think rationally
``The exciting new effort to make computers ``The study of mental faculties through the
think ... machines with minds, in the full and use of computational models'' (Charniak and
literal sense'' (Haugeland, 1985) McDermott, 1985)
``The automation of activities that we associate ``The study of the computations that make it
with human thinking, activities such as possible to perceive, reason, and act''
decision-making, problem solving, learning ...'' (Winston, 1992)
(Bellman, 1978)

Systems that act like humans Systems that act rationally

``The art of creating machines that perform ``A field of study that seeks to explain and
functions that require intelligence when emulate intelligent behavior in terms of
performed by people'' (Kurzweil, 1990) computational processes'' (Schalkoff, 1990)
``The study of how to make computers do things ``The branch of computer science that is
at which, at the moment, people are better'' concerned with the automation of intelligent
(Rich and Knight, 1991) behavior'' (Luger and Stubblefield, 1993)

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Subrajeet Mohapatra
Module-1, Lecture-2 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-3

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Acting humanly: The Turing Test Approach
• Human beings are intelligent
• To be called intelligent, a machine must produce responses
that are indistinguishable from those of a human being
• Turing test was designed to provide a satisfactory
operational definition of intelligence

Alan Turing

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Acting humanly: The Turing Test Approach
Turing Test

Intelligent behavior in machines is the ability to achieve human-level

performance in all cognitive tasks to fool an evaluator.

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Acting humanly: The Turing Test Approach
• Turing Test was designed to provide a satisfactory
operational definition of intelligence
• Implementation of Turing Test need to possess the
following capabilities:

1. Natural language processing to enable it to communicate successfully

in English (or some other human language).
2. Knowledge representation to store information provided before or
during the interrogation.
3. Automated reasoning to use the stored information to answer
questions and to draw new conclusions.
4. Machine learning to adapt to new circumstances and to detect and
extrapolate patterns.

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Acting humanly: The Turing Test Approach
Limitations of Turing Test
• Deception: The machine is forced to construct a false
identity, which is not part of intelligence.
• Conversation: A lot of interaction may qualify as "legitimate
conversation"—jokes, clever asides, points of order—without
requiring intelligent reasoning.
• Evaluation: Humans make mistakes and judges often would
disagree on the results.
Alternatives to Turing Test
• Winograd Schema Challenge
• The Marcus Test
• The Lovelace Test 2.0
• The Visual Turing Test
• The Reverse Turing Test

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
The Turing Test Approach : Some Facts

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Thinking Humanly: The Cognitive Modeling
Approach
• AI expects that a computer program should think like a
human being.
• It is essential to understand that how human beings (brain)
think to develop similar artificial systems.
• Cognitive modeling provides methods to construct precise
and testable theories for understanding the working of the
human mind.
• Once we have a sufficiently precise theory of the human
mind, it becomes possible to express the theory as a
computer program.

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Thinking Humanly: The Cognitive Modeling
Approach
• Cognitive modeling combines computer models from AI and
experimental techniques from cognitive psychology to develop
methods for understanding the working of the human mind.

• Different approaches of Cognitive modeling

• Cognitive Science (Top-Down) : Scientific study of the human
reasoning, emotions, language, perception, attention, and memory,
which draws on aspects of psychology, linguistics, philosophy, and
computer modeling.

• Cognitive Neuroscience (Bottom-Up) :

Anatomical and
physiological study of the biological processes and aspects that
underlie cognition, with a specific focus on the neural connections
in the brain which are involved in mental processes.

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Thinking Rationally: The Laws of Thought
Approach
• “Right thinking” is the inferential character of every reasoning
process.
• Aristotle in his syllogisms provided patterns of argument
structures that always give correct conclusions from given correct
premises.
• There are three “Laws of Thought” recognized by the logicians
• Law of Identity : Emphasis is on correct syllogistic inferences
• Law of Contradiction,
• Law of Excluded–Middle

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Thinking Rationally: The Laws of Thought
Approach
• In the ‘Laws of Thought’ approach to AI, the whole
emphasis is on correct syllogistic inferences.

• Example-
“Socrates is a man; [Premises]
All men are mortal; [Premises]
Therefore, Socrates is mortal.” [Conclusion]

• Conclusion is based on the premises according to the rules

of inference.

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Thinking Rationally: The Laws of Thought
Approach
Limitations
• Difficult to represent informal knowledge in the formal
terms required by logical notation
• There exists differences between being able to solve a
problem in principle and implementing so in practice

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Acting Rationally: The Rational Agent
Approach
• Agent : Something that can perceive and act
• Agents are not merely “Computer Program”
• Rational : Doing the right thing
• Acting rationally means acting so as to achieve one's goals, given
one's beliefs.
• Expected to maximize goal achievement, given the available
information.
• Doesn't necessarily involve thinking – e.g., blinking reflex
• Thinking should be in the service of rational action
• Advantages :
• More general than “thinking rationally”,
• Better than human standards
• AI is viewed as the study and construction of rational agents
Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Branches of AI
• Logical AI
• Search
• Pattern recognition
• Inference
• Artificial reasoning
• Learning from experience
• Planning
• Heuristics
• Genetic programming
• Robotics
• And many others…
Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
State of the Art
• Machine Learning
• Robotics: DARPA Grand/Urban Challenges
• Speech recognition: banking agent, travel agent
• Autonomous planning and scheduling: NASA’s autonomous
planning programs ¨
• Game playing: IBM’s Deep Blue, Google’s AlphaGo
• Logistic planning: During the Persian Gulf crisis U.S. forces
deployed a tool to do automated logistics planning and scheduling
for transportation, hours vs. weeks of efforts
• Robotics: Roomba that helps cleanning, PackBots that handle
hazardous materials, clear explosives and identify location of
snipers
• Machine translation
• Many more….
Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Subrajeet Mohapatra
Module-1, Lecture-3 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-4

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Rational (Intelligent) Agents
What is an agent?
• An agent is anything that perceiving its environment through
sensors and acting upon that environment through actuators or
effectors.
• Objective of AI is to design an automated agent program (transfer
function) that maps from percepts to action.
• A suitable computing hardware is essential for running the agent
program

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Rational (Intelligent) Agents
Examples of Agent
• Human agent consists of sensory organs i.e. ears, eyes, tongue,
nose and skin for feeling the environment, and organs i.e.
hands, legs, mouth, act as actuators.
• Robotic agents uses cameras, infrared range finders and other
transducers for sensing the environment and various motors and
actuators as effectors.
• Software agent is an autonomous computer program in which
keystrokes, file contents, received network packages act as
sensors and displays on the screen, files, sent network packets
act as actuators.

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Agent and Environment

Agent = Architecture + Program

• The agent function maps from percept histories to actions:

[f: P*  A]
• The agent program runs on the physical architecture to produce f

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Intelligent Agents (Terminologies)
• Sensors :
• An agent receives stimuli (observes) from its environment
through sensors.
• Sensing device detects the change (stimuli) in the
environment and sends the information (percepts) to other
electronic devices in the agent.
• Stimuli can be light, sound, words typed on a keyboard,
information obtained from a web page, mouse movements,
and physical bumps.
• Actuators :
• The component of agents converts energy into motion.
• Responsible for moving and controlling a system.
• E.g. : Electric motor, Gears, Rails, etc.

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Intelligent Agents (Terminologies)
• Effectors :
•Devices which affect the environment.
• Can be legs, wheels, arms, fingers, wings, fins, and display
screen
• Percept : Agent’s perceptual inputs at any given instant
• Percept Sequence : Complete history of everything that the
agent has ever perceived
• More Definitions
• An agent is a persistent software entity dedicated to a
specific purpose.
• Intelligent agents continuously perform three functions:
• Perception of dynamic conditions in the environment
• Action to affect conditions in the environment
• Reasoning to interpret perceptions, solve problems, draw
inferences, and determine actions
Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Agent Function & Agent Program
Agent Function
• The agent function is a mathematical function that maps a
sequence of perceptions into action.
• The function is implemented as the agent program.
• Environment -> Sensors -> Agent Function -> Actuators -> Environment
Agent Program
• Real implementation of an agent function.
• Maps percepts to actions

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Rules for an AI Agent

Rule 1: An AI agent must have the ability to perceive the environment.

Rule 2: The observation must be used to make decisions.
Rule 3: Decision should result in an action.
Rule 4: The action taken by an AI agent must be a rational action.

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Intelligent AI Agent
Example : A Vacuum Cleaning Agent
• Environment: Square A and B
• Perception : Clean or Dirty?, Where it is in?
• Action : Move left, Move right, suck, do nothing
• Percept Sequence : History of percepts the vacuum cleaner
senses

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Intelligent AI Agent
Example : Partial tabulation of a simple agent function for a vacuum
cleaning agent

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Intelligent AI Agent
Example : Program implementation of a simple agent function for a
vacuum cleaning agent

Function Reflex-Vacuum-Agent([location, status])

return an action
If status = Dirty then return Suck
else if location = A then return Right
else if location = B then return left

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Intelligent AI Agent
Example : Problem formulation of a simple agent function for a
vacuum cleaning agent

• World state space: 2 positions, dirt or

no dirt 8 world states
• Actions: Left (L), Right (R), or Suck (S)  
• Goal : No dirt in the rooms
• Path costs : One unit per action

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Subrajeet Mohapatra
Module-1, Lecture-4 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-5

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent
• AI agents can be grouped into different categories based on
the degree of perceived intelligence and capability
• Simple Reflex Agents
• Model-Based Reflex Agents
• Goal-Based Agents
• Utility-Based Agents
• Learning Agent

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Simple Reflex Agents)
• Characteristics
• Ignores the percept history and act only on the basis of
the current percept
• Agent function is based on the condition-action rule
(mapping from condition to an action)
• Applicable only for fully observable environment
• Limitations
• Limited intelligence.
• Non-perceptual parts of state is not considered for decision
making
• Difficult to generate and store.
• Collection of rules need to be updated for changes in the
environment

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Simple Reflex Agents)

Image Credit: Sahil Bansall, GeeksforGeeks

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Model-based Reflex Agents)
Characteristics
• Can work in a partially observable environment, and track
the situation
• Consists of two components
Model: Knowledge representation about "how things happen in
the environment," hence called a Model-based agent.
Internal State: It is a representation of the current state based
on percept history.
• Performs action based on model (knowledge about how
things happen in the environment)
• Updation of the agent is based on how
• The way the environment evolves
• The way agent's action affects the world

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Model-based Reflex Agents)

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Goal-based Agents)

• Does not makes decisions based solely on the current

environment
• Capable of thinking beyond the present moment to decide
the best actions to take in order to achieve its goal.
• Operates based on the principle of search and planning
(targets the goal ahead and finds the right action)
• Decisions are taken based on how best to reach that goal
• Decision making process is proactive rather than simply
reactive

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Goal-based Agents)

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Utility-based Agents)
• Similar to the goal-based agent with an extra component of
utility measurement (provides a measure of success at a
given state)
• Acts based not only on what the goal is, but the best way
to achieve that goal
• Suitable for scenarios when there are multiple possible
solutions, and an agent has to choose the best one for
optimum performance
• Utility function maps each state to a real number to check
how efficiently each action achieves the goals

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Utility-based Agents)

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Learning Agents)
• An agent with learning capabilities ( can learn from its past
percept sequences) and improves performance with time
• Can adapt to environment and chooses action based on
percept history
Learning Agents Components
• Performance element :
• Chooses what action to take
• Shifts to a new action based on feedback and suggestions for improvement
• Critic element determines the outcome of the action and gives feedback
• Learning element takes the feedback from the critic element and updates itself
to take better decisions further
• Problem generator is responsible for developing new experiences for the
learning agent and assist the agent to continue learning

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Agent (Learning Agents)

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Subrajeet Mohapatra
Module-1, Lecture-5 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-6

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
What is an Intelligent Agent?

• Agent : Anything that can be seen as

• Perceiving its environment through sensors
• Acting upon it through actuators
• Iteratively run in cycles of perceiving, thinking and acting
• Intelligent Agent : An autonomous entity which act upon
an environment using sensors and actuators for achieving
its goals (Maximize Performance Measure).
• Intelligent agents are also known as rational agent

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Rational Behavior & Rationality
• Rationality is about being reasonable, sensible, and having
good sense of judgment.
• Rational behavior refers to a decision-making process
that is based on making choices that result in maximizing
performance measure for an agent. (In terms of AI)
• The performance measure defines the criterion of success
for an agent
• Status of rationality for an agent can be measured by its
• Performance Measure
• Prior knowledge
• Environment it can perceive
• Actions it can Perform
• Prperties of an agent can be represented as a single term i.e.
PEASLecture-6
Module-1, Basics of Intelligent Computing (IT201)
Subrajeet Mohapatra
Department of CSE, BIT Mesra
Rational Behavior & Rationality
• Rationality is about being reasonable, sensible, and having
good sense of judgment.
• Rational behavior refers to a decision-making process
that is based on making choices that result in maximizing
performance measure for an agent. (In terms of AI)
• The performance measure defines the objective criterion
for measuring success of an agent

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Rationality

• Rationality is relative to a performance measure

• Status of rationality for an agent can be judged by its
• Performance Measure that defines the criteria for
success
• Prior knowledge
• Environment it can perceive
• Actions it can Perform
• Properties of an agent can be represented as a single
term i.e. PEAS

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
PEAS
• The properties of an intelligent agents are often grouped
in the term PEAS, which stands for Performance,
Environment, Actuators and Sensors.
• Specifying the task environment is always the first step in
designing an intelligent agent
• The rational agent we want to design for this task
environment is the solution

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
PEAS
What is PEAS for a self driving car?
• Performance : Safety, time, legal drive, comfort.
• Environment : Roads, other cars, pedestrians, road signs.
• Actuators : Steering, accelerator, brake, signal, horn.
• Sensors : Camera, sonar, GPS, Speedometer, odometer,
accelerometer, engine sensors, keyboard

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
PEAS
What is PEAS for a automated vacuum cleaner?
• Performance : Cleanness, Efficiency: Distance traveled to
clean, Battery life, Security
• Environment : Room, table, wood floor, carpet, different
obstacles.
• Actuators : Wheels, different brushes, vacuum extractor
• Sensors : Camera, Dirt detection sensor, Cliff sensor, Bump
sensors, Infrared wall sensors.

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Task Environment

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Subrajeet Mohapatra
Module-1, Lecture-6 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-7

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Designing Intelligent Agent

• An agent operates in a task environment: –

• Task : The goal(s) the agent is trying to achieve
• Environment: That part of the real world or a computational
system ‘inhabited’ by the agent
• An environment can be described as a situation in which
an agent is present
• An agent obtains information about the environment in the
form of percepts
• Agent changes the environment by performing actions to
achieve its goals

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Designing Intelligent Agent
• Task environments are the problems
• Rational agents are the solutions
• Specifying the task environment
• PEAS description are used as reference
• Performance
• Environment
• Actuators
• Sensors
Initial step in designing an agent is to specify the task
environment as fully as possible.
Example: Automated Taxi driver

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Designing Intelligent Agent: (Unmanned Taxi)
• Performance Measure
• Reaching to the correct destination
• Minimizing fuel consumption
• Minimizing the trip time and/or cost
• Minimizing the violations of traffic laws
• Maximizing the safety and comfort, etc.
• Environment
• Needs to deal with a variety of roads
• Traffic lights, other vehicles, pedestrians, stray
animals, road works, police cars, etc.
• Interact with the customer

Subrajeet Mohapatra
• Actuators
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Designing Intelligent Agent: (Unmanned Taxi)
• Actuators (for outputs)
• Control over the accelerator, steering, gear shifting and
braking
• A display to communicate with the customers

• Sensors (for inputs)

• Camera, sonar, GPS, Speedometer, odometer,
accelerometer, engine sensors, keyboard

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
The Task Environment : Problem Definition

• The task and/or environment can

be manipulated to make things
easier
• Usually the task environment is
given

Picture Credit: Brian Logan 2013

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Specifying Task Environment

• Task specifies the goals the agent must achieve (and any
results required)
• Agent and environment jointly determine:
• The information the agent can obtain (percepts)
• The actions the agent can perform
• Decomposition into task and environment is not always
obvious

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Specifying Task Environment

Picture Credit: Brian Logan 2013

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Specifying the Task

• Tasks may come from the agent

itself (autonomy)
• Agent may generate its own
(top-level) goals
• e.g., may generate a goal to
keep its battery charged
• or it may make use of its own
results, e.g., in learning

Picture Credit: Brian Logan 2013

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Specifying the Environment

• An agent may be part of its own

environment
• Example:
• Agent having sensors
monitoring its battery level
• Agent may also be a part of the
environment of other agents
• e.g., in a multi-agent system

Picture Credit: Brian Logan 2013

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Task Environment Classification

• Agent’s task environment can be classified based on:

• Task: Properties of the goals that must be achieved
• Environment: Properties of the percepts and actions
possible in the environment

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Characterizing Task Environment

• Fully observable / Partially observable

• If an agent’s sensors give it access to the complete state of
the environment needed to choose an action, the
environment is fully observable. (e.g. chess – what about
Kriegspiel?)
• Deterministic / Stochastic
• Deterministic : If the next state of the environment is
completely determined by the current state of the
environment and the action of the agent
• Stochastic : If there are multiple, unpredictable outcomes.
• Strategic : (If the environment is deterministic except for
the actions of other agents

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Characterizing Task Environment

• Episodic / Sequential
• Episodic : It is a series of one-shot actions, and only the
current percept is required for the action
• Sequential : An agent requires memory of past actions
to determine the next best actions
• Single-Agent / Multi-Agent
• Single-Agent : Only one agent involved
• Multi-Agent : Multiple agent involved
• Static/ Dynamic
• Environment which can change itself while an agent is
deliberating then such environment is called a dynamic
environment else it is called a static environment

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Characterizing Task Environment

• Discrete / Continuous
• If there are a finite number of percepts and actions that can
be performed within it, then such an environment is called a
discrete environment else it is called continuous
environment.

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Subrajeet Mohapatra
Module-1, Lecture-7 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-8
Problem Solving by Search

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Problem Solving

• Objective :
• To automatically solve a given problem
• Requirement:
• Suitable representation of the problem
• Develop suitable algorithms which uses some strategy to
find a suitable solution

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Problem Representation

• General :
• State Space : A problem is divided into a sequence of
steps from the initial state to goal state
• Reduction to Sub-programs : Problem is arranged into a
hierarchy of sub-problems
• Specific:
• Constraints satisfaction

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Problem Solving using Search

• A search problem can be formulated by

• Providing a description of the current problem state
• Providing a description of its own actions that can
transform one problem state into another one
• Providing a description of the goal state where a desired
goal holds
• The solution of such a search based problem consists of
finding an optimum path from the current state to the goal
state.

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Problem Solving using Search

• A problem is defined by its elements and their relations

• In each instant of the resolution of the problem the
elements of the problem has a particular representation
• A state is a representation of those elements in a
particular moment
• A state space is a set of all possible states where you
can be.
• A start state is the state from where the search begins.
• Goal test is a function which observe the current state and
returns whether the goal state is achieved or not.

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Problem Solving using Search
• Solution to a search problem is a sequence of actions,
called the plan
• Plan transforms the start state to the goal state and is
achieved through search algorithms.
• A search algorithm takes a problem as input and returns a
solution in the form of action sequence
• Once a solution is found the actions it recommends can be
carried out (execution phase)

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Properties of Search Algorithms

• Essential for the comparison of different algorithms

• Four properties of search algorithms :
• Completeness: If the algorithm 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: 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.

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Search Algorithms
• Search algorithms can be classified into uninformed (Blind
search) search and informed search (Heuristic search)
• Uninformed Search
• Such algorithms have no additional information on the goal
node other than the one provided in the problem definition.
• The plans to reach the goal state from the start state differ
only by the order and/or length of actions.
• Also known as blind search.
• Informed search (Heuristic search)
• Algorithms have previous information on the goal state,
which assists in efficient searching.
• Previous information about goal state is called a heuristic.
Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Types of Search Algorithms

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Example : 8 Puzzle

1 2 3 1 2 3
7 8 4 8 4
6 5 7 6 5
Initial State Goal State

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Example : 8 Puzzle

12 3 GOAL 123
8 4 784
76 5 6 5

right

1 23 123 123
7 84 784 7 4
65 65 6 85

Which move is best?

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Example :8 Puzzle Heuristics

• Blind search techniques used an arbitrary ordering

(priority) of operations.
• Heuristic search techniques make use of domain specific
information - a heuristic.
• What heuristics can we use to decide which 8-puzzle move
is “best” (worth considering first).

Module-1, Lecture-8 Basics of Intelligent Computing (IT201) 15 Subrajeet Mohapatra

Department of CSE, BIT Mesra
Example :A Simple 8-puzzle heuristic

First Approach
• Number of tiles in the correct position.
• The higher the number the better.
• Easy to compute (fast and takes little memory).
• Probably the simplest possible heuristic.

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Example :A Simple 8-puzzle heuristic

Second Approach
• Number of tiles in the incorrect position.
• This can also be considered a lower bound on the number of
moves from a solution!
• The “best” move is the one with the lowest number returned
by the heuristic.
• Is this heuristic more than a heuristic (is it always correct?).
• Given any 2 states, does it always order them properly
with respect to the minimum number of moves away from
a solution?

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Example :A Simple 8-puzzle heuristic

GOAL
1 2 3 1 23
8 4 7 84
7 6 5 6 5

right

1 23 1 23 123
7 84 7 84 7 4
6 5 6 5 6 85
h=2 h=4 h=3

Module-1, Lecture-8 Basics of Intelligent Computing (IT201) 18 Subrajeet Mohapatra

Department of CSE, BIT Mesra
Another 8-puzzle heuristic

• Count how far away (how many tile movements) each tile is
from it’s correct position.
• Sum up this count over all the tiles.
• This is another estimate on the number of moves away
from a solution.

Module-1, Lecture-8 Basics of Intelligent Computing (IT201) 19 Subrajeet Mohapatra

Department of CSE, BIT Mesra
Another 8-puzzle heuristic

1 2 3 GOAL 1 23
8 4 7 84
7 6 5 6 5

right

1 23 1 23 123
7 84 7 84 7 4
6 5 6 5 6 85
h=2 h=4 h=4

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Subrajeet Mohapatra
Module-1, Lecture-8 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-9
Knowledge Representation in AI

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Definition of Knowledge

• Definition 1: The fact or condition of knowing something with

familiarity gained through experience or association (Merriam-
Webster)
• Definition 2: Acquaintance with or understanding of a science, art,
or technique (Merriam- Webster)
• Definition 3: Is a familiarity, awareness, or understanding of
someone or something, such as facts, skills, or objects (Merriam-
Webster)
• Definition 4: Acquaintance with facts, truths, or principles
(Wikipedia)
• Definition 5: Knowledge is a general term and is a progression that
starts with data which is of limited utility

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Progression

• Data can be understandable by organizing and analyzing it

(information).
• The interpretation of information yields knowledge.
• Understanding the principles embodied within the
knowledge is wisdom.

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Progression

• Data :
• A raw and unorganized fact that required to be processed
to make it meaningful
• Comprises of facts, observations, perceptions numbers,
characters, symbols, image, etc.
• Viewed as a collection of disconnected facts
• Always require interpretation by human or machine to
derive meaning
• Information :
• Set of data which is processed in a meaningful way
according to the desired requirement
• Processed data, structured and presented in a meaningful
manner
• Relationship among facts are established and understood
Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Progression
• Knowledge :
• General awareness or possession of information, facts, ideas,
truths, or principles
• Combination of information, experience, and insight that helps
the individual or the organization to identify and understand
relationship among patterns
• Possessed by each individual and is an outcome of his or her
experience
• Includes norms to evaluate new inputs from his surroundings
• Wisdom :
• Knowledge and experience needed to make sensible decisions
and judgments
• Accumulated knowledge of life or in a particular sphere of
activity that has been gained through experience

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Progression : DIKW Pyramid

Image Credit : https://favpng.com

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Progression

Image Credit : https://favpng.com

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Progression: Example

Image Credit : https:// https://www.guru99.com/

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Progression : Example

Image Credit : https://media.cheggcdn.com

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Model
• Knowledge model depicts that the degree of “connectedness”
and “understanding” increases, as we progress from data
through information and knowledge to wisdom.

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Categorization of Knowledge
• Knowledge can be categorized into :
• Tacit : informal or implicit
• Explicit : formal

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Categorization of Knowledge
• Cognitive psychologist further classify knowledge into

Image Credit : https://www.javatpoint.com

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Framework of Knowledge Representation
• Human beings perform various actions in the real world
based on their understanding, reasoning, and interpreting
knowledge capability
• Study of machines having understanding, reasoning, and
interpreting knowledge capability is called knowledge
representation (KR) and reasoning
• KR addresses the following objectives :
• Introducing thinking ability in AI agents thinking
• Study how thinking contributes to intelligent behavior of
agents
• Representing information about the real world for computer
understanding

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Importance of Knowledge Representation
• Computer requires a well-defined problem description to
process and provide a well-defined acceptable solution
• Intelligent machine aided problem solving requires
• Formal knowledge representation
• Conversion from informal to formal knowledge
representation (implicit to explicit knowledge)

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
What to Represent?
• Different kinds/components of knowledge which might be
required to be represented in AI based systems
• Objects : All the facts about objects in our world
domain.
• Events : Actions which occur in our world.
• Performance : Describe behavior which involves
knowledge about how to do things.
• Facts : Truths about the real world and what we want
to represent
• Meta-Knowledge : Knowledge about knowledge (what
we know)
• Knowledge-base : Database used for knowledge sharing
and management.

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Cycle
• AI Systems consists of different components to display their
intelligent behavior

Image Credit : https://www.edureka.co

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Relation between Knowledge & Intelligence

Image Credit : https://www.edureka.co

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Methods

Image Credit : https://www.edureka.co

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Image Credit : https://favpng.com

Subrajeet Mohapatra
Module-1, Lecture-9 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Basics of Intelligent Computing

Dr. Subrajeet Mohapatra

Assistant Professor
Department of Computer Science & Engineering,
BIT Mesra, Ranchi
Jharkhand

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Copyright Information
• The course materials is intended only for instructional purpose of
students of BIT Mesra
• Students can take notes and make copies of course materials only
for individual use
• Enrolled students should not reproduce, distribute or display
(post/upload) lecture notes or recordings or course materials in
any other way without my express written consent
• The teacher in-charge for this course material will not be
responsible for violation of above policies by the students
• Students violating the above policies may be subject to student
conduct proceedings

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Disclaimer & Fair Dealing Statement
• The author of this course material will not be held personally
responsible, nor liable for any damages, actual or consequential,
for any posts by third parties which may violate any law.
• This course material may contain copyrighted material the use of
which has not always been specifically authorized by the
copyright owner
• Use of materials in the presentation constitutes a ‘fair dealing of
any such copyrighted material
• The material contained in this presentation is used without profit
for research and educational purposes only

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Lecture-10
Knowledge Representation Methods &
Classification of AI

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge in AI based Systems

Algorithms Knowledge
+ Data Structures + Inference
= Programs = Expert System

Conventional Programming Intelligent System

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques

Image Credit : https://www.edureka.co

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Logical Representation
• A language with some definite rules which deal with
propositions and has no ambiguity in representation.
• Represents a conclusion based on various conditions and lays
down some important communication rules.
• Consists of precisely defined syntax and semantics which
supports the sound inference.
Syntax :
• Decides how we can construct legal sentences in logic.
• It determines which symbol we can use in knowledge
representation.
Semantics
• Rules by which we can interpret the sentence in the logic.
• It assigns a meaning to each sentence.

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Logical Representation
• Logical representation can be categorized into propositional
logic and predicate logic
• Propositional Logic :
• Simplest form of logic where all the statements are made by
propositions and are represented in logical and mathematical
form
• Proposition is a declarative statement which is either true or false
• Example :
a. It is Sunday.
b. The Sun rises from West (False proposition)
c. 3+3= 7(False proposition)
d. 5 is a prime number.

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Logical Representation
• Predicate Logic
• A formal language in which propositions are expressed in terms of
predicates, variables and quantifiers
• It is different from propositional logic which lacks quantifiers
• Can be viewed as an extension to propositional logic
• Superior to propositional logic in the sense that it is able to
capture the structure of several arguments

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Semantic Network Representation
• An alternative to predicate logic form of KR
• Knowledge is represented in the form of a directed graph
• Nodes in the graph represents objects in the world, and arcs
represents relationships between the objects
• Can categorize the object in different forms and can also link
those objects
• Easy to understand and can be extended
• Semantic network representation consists of mainly two types of
relations:
• IS-A relation (Inheritance)
• Kind-of-relation

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques

Semantic Network Representation

Statements
• Jerry is a cat
• Jerry is a mammal
• Jerry is owned by Priya
• Jerry is brown colored
• All Mammals are animal.

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Production Rules
• Consists of condition and action pairs (i.e. "If condition then
action“)
• Comprises of mainly three parts:
• The set of production rules
• Working Memory
• The recognize-act-cycle
• The complete process of recognize-act-cycle consists of
• Agent checks for the condition and if the condition exists then
production rule fires and corresponding action is carried out
• Condition part of the rule determines which rule may be applied to
a problem
• Action part carries out the associated problem-solving steps

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Production Rules
• Working memory contains the description of the current state of
problems-solving and rule can write knowledge to the working
memory
• Examples :
i. IF (at bus stop AND bus arrives) THEN action (get into the bus)
ii. IF (on the bus AND paid AND empty seat) THEN action (sit down).
iii. IF (on bus AND unpaid) THEN action (pay charges).
iv. IF (bus arrives at destination) THEN action (get down from the bus).

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Frame Representation

• A record like structure which consists of a collection of

attributes and its values to describe an entity in the world
• Frames are the AI data structure which divides knowledge
into substructures by representing stereotypes situations
• Consists of a collection of slots and slot values
• Slots may be of any type and sizes
• Slots have names and values which are called facets

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Frame Representation

• Frames are derived from semantic networks and later evolved

into our modern-day classes and objects
• In the frame, knowledge about an object or event can be stored
together in the knowledge base
• Widely used in various applications including Natural
language processing and machine visions

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Frame Representation Example (Frame of a Book)

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Knowledge Representation Techniques
Frame Representation Example (Frame of a Bird)

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Classification of AI

AI can be broadly classified into two categories

• Category 1
• Weak AI or Narrow AI
• Strong AI
• Super AI
• Category 2 (Based on Functionalities)
• Reactive Machines:
• Limited Memory
• Theory of Mind:
• Self-awareness

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Classification of AI

• AI can be broadly classified into two categories

Category 1
• Weak AI or Narrow AI:
• Refers to any AI tool that focuses on doing one task really well
(has a narrow scope in terms of what it can do).
• Weak AI based agents cannot mimic or replicate human
intelligence (Rather, it’s to simulate human behavior)
• It’s nowhere near matching human intelligence, neither it isn’t
trying to
• Agents are capable of completing the specific tasks it’s
programmed to do.
• Weak AI based agents are barely intelligent at all
• Example: Amazon’s Alexa and iPhone’s Siri

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Classification of AI

Category 1
• Strong AI :
• Machines that can actually understand, think and perform tasks
on its own just like a human being in any given situation
• Theoretically, anything a human can do, a strong AI should be
able to do
• To be honest we don’t have a strong AI in the world yet
• Super AI :
• AI that surpasses human intelligence and ability (Artificial Super
Intelligence (ASI) or super intelligence)
• It’s difficult to come close to the abilities of super AI (Super AI is
best at everything)
• Its about AI overthrowing or enslaving humans
• Super AI is purely speculative

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
Classification of AI
There are different ways AI can be achieved

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 References
1. Russell, Stuart J. and Norvig, Peter, Artificial Intelligence: A Modern Approach
2. Rich, Elaine, Artificial Intelligence, McGraw-Hill, Inc., Singapore, 1984
3. Charniak, Eugene & McDermott, Drew, Introduction to Artificial Intelligence,
Addission Wesley Publishing Company, Canada 1985
4. Winston, Patrick Henry, Artificial Intelligence, Addison-Wesley Publishing Company,
London, July 1984
5. Luger, G. F. and Stubblefield, W. A., Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Benjamin/Cummings, California, 1993
6. Turing, Alan, “Computing Machinery and Intelligence,” in Minds and Machines,
A.R. Anderson (ed), Prentice Hall.,Inc., Englewood Cliffs, New Jersy, 1964

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra
 Image Credit : https://favpng.com

Subrajeet Mohapatra
Module-1, Lecture-10 Basics of Intelligent Computing (IT201)
Department of CSE, BIT Mesra