LECTURE NOTES
ON
ARTIFICIAL INTELLIGENCE
PREPARED BY
DR. PRASHANTA KUMAR PATRA
COLLEGE OF ENGINEERING AND TECHNOLOGY,
BHUBANESWAR
1
,ARTIFICIAL INTELLIGENCE SYLLABUS
Module 1 12Hrs
What is Artificial Intelligence? AI Technique, Level of the Model,Problem Spaces, and Search: Defining
the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System
Characteristics, Issues in the Design of Search Programs. Heuristic Search Techniques: Generate-and-
Test, Hill Climbing, Best-first Search, Problem Reduction, Constraint Satisfaction, Means-ends
Analysis, Knowledge Representation: Representations and Mappings, Approaches to Knowledge
Representation, Using Predicate Logic: Representing Simple Facts in Logic, Representing Instance and
ISA Relationships, Computable Functions and Predicates, Resolution, Natural Deduction.Using Rules:
Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning,
Matching, Control Knowledge.Symbolic Reasoning Under Uncertainty: Introduction to Nonmonotonic
Reasoning, Logics for Nonmonotonic Reasoning, Implementation Issues, Augmenting a Problem-solver,
Depth-first Search, Breadthfirst Search.Weak and Strong Slot-and-Filler Structures: Semantic Nets,
Frames, Conceptual Dependency Scripts, CYC.
Module 2 10Hrs
Game Playing: The Minimax Search Procedure, Adding Alpha-beta Cutoffs, Iterative Deepening.Planning:
The Blocks World, Components of a Planning System, Goal Stack Planning, Nonlinear Planning Using
Constraint Posting, Hierarchical PlanningOther Planning Techniques.Understanding: What is
Understanding, What Makes Understanding Hard?, Understanding as Constraint Satisfaction.Natural
Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Discourse and Pragmatic
Processing, Statistical Natural Language Processing, Spell Checking.
Module 3 8Hrs
Learning: Rote Learning, learning by Taking Advice, Learning in Problem-solving, Learning from
Examples: Induction, Explanation-based Learning, Discovery, Analogy, Formal Learning Theory, Neural
Net Learning and Genetic Learning. Expert Systems: Representing and Using Domain Knowledge, Expert
System Shells, Explanation, Knowledge Acquisition.
Text Book:
1. Elaine Rich, Kevin Knight, & Shivashankar B Nair, Artificial Intelligence, McGraw Hill, 3rd ed.,2009
References:
1) Introduction to Artificial Intelligence & Expert Systems, Dan W Patterson, PHI.,2010
2) S Kaushik, Artificial Intelligence, Cengage Learning, 1st ed.2011
2
,Module 1
ARTIFICIAL INTELLIGENCE
What is Artificial Intelligence?
It is a branch of Computer Science that pursues creating the computers or machines as intelligent
as human beings.
It is the science and engineering of making intelligent machines, especially intelligent computer
programs.
It is related to the similar task of using computers to understand human intelligence, but AI does
not have to confine itself to methods that are biologically observable
Definition: Artificial Intelligence is the study of how to make computers do things, which, at the
moment, people do better.
According to the father of Artificial Intelligence, John McCarthy, it is “The science and
engineering of making intelligent machines, especially intelligent computer programs”.
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a
software think intelligently, in the similar manner the intelligent humans think.
AI is accomplished by studying how human brain thinks and how humans learn, decide, and
work while trying to solve a problem, and then using the outcomes of this study as a basis of
developing intelligent software and systems.
It has gained prominence recently due, in part, to big data, or the increase in speed, size and
variety of data businesses are now collecting. AI can perform tasks such as identifying patterns
in the data more efficiently than humans, enabling businesses to gain more insight out of
their data.
From a business perspective AI is a set of very powerful tools, and methodologies for using
those tools to solve business problems.
From a programming perspective, AI includes the study of symbolic programming, problem
solving, and search.
AI Vocabulary
Intelligence relates to tasks involving higher mental processes, e.g. creativity, solving problems,
pattern recognition, classification, learning, induction, deduction, building analogies,
optimization, language processing, knowledge and many more. Intelligence is the computational
part of the ability to achieve goals.
Intelligent behaviour is depicted by perceiving one’s environment, acting in complex
environments, learning and understanding from experience, reasoning to solve problems and
discover hidden knowledge, applying knowledge successfully in new situations, thinking
abstractly, using analogies, communicating with others and more.
3
, Science based goals of AI pertain to developing concepts, mechanisms and understanding
biological intelligent behaviour. The emphasis is on understanding intelligent behaviour.
Engineering based goals of AI relate to developing concepts, theory and practice of building
intelligent machines. The emphasis is on system building.
AI Techniques depict how we represent, manipulate and reason with knowledge in order to
solve problems. Knowledge is a collection of ‘facts’. To manipulate these facts by a program, a
suitable representation is required. A good representation facilitates problem solving.
Learning means that programs learn from what facts or behaviour can represent. Learning
denotes changes in the systems that are adaptive in other words, it enables the system to do the
same task(s) more efficiently next time.
Applications of AI refers to problem solving, search and control strategies, speech recognition,
natural language understanding, computer vision, expert systems, etc.
Problems of AI:
Intelligence does not imply perfect understanding; every intelligent being has limited perception,
memory and computation. Many points on the spectrum of intelligence versus cost are viable,
from insects to humans. AI seeks to understand the computations required from intelligent
behaviour and to produce computer systems that exhibit intelligence. Aspects of intelligence
studied by AI include perception, communicational using human languages, reasoning, planning,
learning and memory.
The following questions are to be considered before we can step forward:
1. What are the underlying assumptions about intelligence?
2. What kinds of techniques will be useful for solving AI problems?
3. At what level human intelligence can be modelled?
4. When will it be realized when an intelligent program has been built?
Branches of AI:
A list of branches of AI is given below. However some branches are surely missing, because no
one has identified them yet. Some of these may be regarded as concepts or topics rather than full
branches.
Logical AI — In general the facts of the specific situation in which it must act, and its goals are
all represented by sentences of some mathematical logical language. The program decides what
to do by inferring that certain actions are appropriate for achieving its goals.
4
ON
ARTIFICIAL INTELLIGENCE
PREPARED BY
DR. PRASHANTA KUMAR PATRA
COLLEGE OF ENGINEERING AND TECHNOLOGY,
BHUBANESWAR
1
,ARTIFICIAL INTELLIGENCE SYLLABUS
Module 1 12Hrs
What is Artificial Intelligence? AI Technique, Level of the Model,Problem Spaces, and Search: Defining
the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System
Characteristics, Issues in the Design of Search Programs. Heuristic Search Techniques: Generate-and-
Test, Hill Climbing, Best-first Search, Problem Reduction, Constraint Satisfaction, Means-ends
Analysis, Knowledge Representation: Representations and Mappings, Approaches to Knowledge
Representation, Using Predicate Logic: Representing Simple Facts in Logic, Representing Instance and
ISA Relationships, Computable Functions and Predicates, Resolution, Natural Deduction.Using Rules:
Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning,
Matching, Control Knowledge.Symbolic Reasoning Under Uncertainty: Introduction to Nonmonotonic
Reasoning, Logics for Nonmonotonic Reasoning, Implementation Issues, Augmenting a Problem-solver,
Depth-first Search, Breadthfirst Search.Weak and Strong Slot-and-Filler Structures: Semantic Nets,
Frames, Conceptual Dependency Scripts, CYC.
Module 2 10Hrs
Game Playing: The Minimax Search Procedure, Adding Alpha-beta Cutoffs, Iterative Deepening.Planning:
The Blocks World, Components of a Planning System, Goal Stack Planning, Nonlinear Planning Using
Constraint Posting, Hierarchical PlanningOther Planning Techniques.Understanding: What is
Understanding, What Makes Understanding Hard?, Understanding as Constraint Satisfaction.Natural
Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Discourse and Pragmatic
Processing, Statistical Natural Language Processing, Spell Checking.
Module 3 8Hrs
Learning: Rote Learning, learning by Taking Advice, Learning in Problem-solving, Learning from
Examples: Induction, Explanation-based Learning, Discovery, Analogy, Formal Learning Theory, Neural
Net Learning and Genetic Learning. Expert Systems: Representing and Using Domain Knowledge, Expert
System Shells, Explanation, Knowledge Acquisition.
Text Book:
1. Elaine Rich, Kevin Knight, & Shivashankar B Nair, Artificial Intelligence, McGraw Hill, 3rd ed.,2009
References:
1) Introduction to Artificial Intelligence & Expert Systems, Dan W Patterson, PHI.,2010
2) S Kaushik, Artificial Intelligence, Cengage Learning, 1st ed.2011
2
,Module 1
ARTIFICIAL INTELLIGENCE
What is Artificial Intelligence?
It is a branch of Computer Science that pursues creating the computers or machines as intelligent
as human beings.
It is the science and engineering of making intelligent machines, especially intelligent computer
programs.
It is related to the similar task of using computers to understand human intelligence, but AI does
not have to confine itself to methods that are biologically observable
Definition: Artificial Intelligence is the study of how to make computers do things, which, at the
moment, people do better.
According to the father of Artificial Intelligence, John McCarthy, it is “The science and
engineering of making intelligent machines, especially intelligent computer programs”.
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a
software think intelligently, in the similar manner the intelligent humans think.
AI is accomplished by studying how human brain thinks and how humans learn, decide, and
work while trying to solve a problem, and then using the outcomes of this study as a basis of
developing intelligent software and systems.
It has gained prominence recently due, in part, to big data, or the increase in speed, size and
variety of data businesses are now collecting. AI can perform tasks such as identifying patterns
in the data more efficiently than humans, enabling businesses to gain more insight out of
their data.
From a business perspective AI is a set of very powerful tools, and methodologies for using
those tools to solve business problems.
From a programming perspective, AI includes the study of symbolic programming, problem
solving, and search.
AI Vocabulary
Intelligence relates to tasks involving higher mental processes, e.g. creativity, solving problems,
pattern recognition, classification, learning, induction, deduction, building analogies,
optimization, language processing, knowledge and many more. Intelligence is the computational
part of the ability to achieve goals.
Intelligent behaviour is depicted by perceiving one’s environment, acting in complex
environments, learning and understanding from experience, reasoning to solve problems and
discover hidden knowledge, applying knowledge successfully in new situations, thinking
abstractly, using analogies, communicating with others and more.
3
, Science based goals of AI pertain to developing concepts, mechanisms and understanding
biological intelligent behaviour. The emphasis is on understanding intelligent behaviour.
Engineering based goals of AI relate to developing concepts, theory and practice of building
intelligent machines. The emphasis is on system building.
AI Techniques depict how we represent, manipulate and reason with knowledge in order to
solve problems. Knowledge is a collection of ‘facts’. To manipulate these facts by a program, a
suitable representation is required. A good representation facilitates problem solving.
Learning means that programs learn from what facts or behaviour can represent. Learning
denotes changes in the systems that are adaptive in other words, it enables the system to do the
same task(s) more efficiently next time.
Applications of AI refers to problem solving, search and control strategies, speech recognition,
natural language understanding, computer vision, expert systems, etc.
Problems of AI:
Intelligence does not imply perfect understanding; every intelligent being has limited perception,
memory and computation. Many points on the spectrum of intelligence versus cost are viable,
from insects to humans. AI seeks to understand the computations required from intelligent
behaviour and to produce computer systems that exhibit intelligence. Aspects of intelligence
studied by AI include perception, communicational using human languages, reasoning, planning,
learning and memory.
The following questions are to be considered before we can step forward:
1. What are the underlying assumptions about intelligence?
2. What kinds of techniques will be useful for solving AI problems?
3. At what level human intelligence can be modelled?
4. When will it be realized when an intelligent program has been built?
Branches of AI:
A list of branches of AI is given below. However some branches are surely missing, because no
one has identified them yet. Some of these may be regarded as concepts or topics rather than full
branches.
Logical AI — In general the facts of the specific situation in which it must act, and its goals are
all represented by sentences of some mathematical logical language. The program decides what
to do by inferring that certain actions are appropriate for achieving its goals.
4
