ARTIFICIAL INTELLIGENCE LECTURE
Computer Science and Engineering &
Information Technology
BCS-404 ARTIFICIAL INTELLIGENCE (3-1-0) Cr.-04
Module - I
Formalized symbolic logic: Propositional logic-first order predicate logic, wff conversion
to clausal form, inference rules, the resolution principle, Dealing with inconsistencies and
uncertainties, fuzzy logic.
Module - II
Probabilistic Reasoning Structured knowledge, graphs, frames and related structures,
Knowledge organization and manipulation.
Module – III
Matching Techniques, Knowledge organizations, Management.
Module - IV
Natural Language processing, Pattern recognition, expert systems.
Text Book:
1. Artificial Intelligence, Dan W Patterson, Prentice Hall of India (1999)
Chapter-4, 5,7,9,10,11,12,13,15.
Reference Books:
1. Artificial Intelligence, Nils J.Nilsson, ELSEVIER.
2. E.Rich and K.Knight, Artificial Intelligence, - TMH
Overview of Artificial Intelligence
What is AI ?
∙ Artificial Intelligence (AI) is a branch of Science which deals with helping machines
find solutions to complex problems in a more human-like fashion.
∙ This generally involves borrowing characteristics from human intelligence, and applying
them as algorithms in a computer friendly way.
∙ A more or less flexible or efficient approach can be taken depending on the
, requirements established, which influences how artificial the intelligent behavior
appears
∙ Artificial intelligence can be viewed from a variety of perspectives. ✔ From the
perspective of intelligence, artificial intelligence is making machines "intelligent"
-- acting as we would expect people to act.
o The inability to distinguish computer responses from human responses
is called the Turing test.
o Intelligence requires knowledge
o Expert problem solving - restricting domain to allow including
significant relevant knowledge
✔ 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.
o Typically AI programs focus on symbols rather than numeric
processing.
o Problem solving - achieve goals.
o Search - seldom access a solution directly. Search may include a variety
of techniques.
o AI programming languages include:
– LISP, developed in the 1950s, is the early programming language strongly
associated with AI. LISP is a functional programming language with
procedural extensions. LISP (LISt Processor) was specifically designed for
processing heterogeneous lists -- typically a list of symbols. Features of LISP
are run- time type checking, higher order functions (functions that have other
functions as parameters), automatic memory management (garbage collection)
and an interactive environment.
– The second language strongly associated with AI is PROLOG. PROLOG
was developed in the 1970s. PROLOG is based on first order logic. PROLOG
is declarative in nature and has facilities for explicitly limiting the search
space.
– Object-oriented languages are a class of languages more recently used for AI
programming. Important features of object-oriented languages include:
concepts of objects and messages, objects bundle data and methods for
manipulating the data, sender specifies what is to be done, receiver decides
how to do it, inheritance (object hierarchy where objects inherit the attributes
of the more general class of objects). Examples of object-oriented languages
, are Smalltalk, Objective C, C++. Object oriented extensions to LISP (CLOS -
Common LISP Object System) and PROLOG (L&O - Logic & Objects) are
also used.
∙ Artificial Intelligence is a new electronic machine that stores large amount of
information and process it at very high speed
∙ The computer is interrogated by a human via a teletype It passes if the human cannot tell
if there is a computer or human at the other end
∙ The ability to solve problems
∙ 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
Importance of AI
∙ Game Playing
You can buy machines that can play master level chess for a few hundred dollars.
There is some AI in them, but they play well against people mainly through brute
force computation--looking at hundreds of thousands of positions. To beat a world
champion by brute force and known reliable heuristics requires being able to look at
200 million positions per second.
∙ Speech Recognition
In the 1990s, computer speech recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its keyboard tree for flight information
by a system using speech recognition of flight numbers and city names. It is quite
convenient. On the other hand, while it is possible to instruct some computers using
speech, most users have gone back to the keyboard and the mouse as it is still more
convenient.
∙ Understanding Natural Language
Just getting a sequence of words into a computer is not enough. Parsing sentences is
not enough either. The computer has to be provided with an understanding of the
domain the text is about, and this is presently possible only for very limited domains.
∙ Computer Vision
The world is composed of three-dimensional objects, but the inputs to the human eye
and computers' TV cameras are two dimensional. Some useful programs can work
solely in two dimensions, but full computer vision requires partial three-dimensional
information that is not just a set of two-dimensional views. At present there are only
limited ways of representing three-dimensional information directly, and they are not
, as good as what humans evidently use.
∙ Expert Systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their
knowledge in a computer program for carrying out some task. How well this works
depends on whether the intellectual mechanisms required for the task are within the
present state of AI. When this turned out not to be so, there were many disappointing
results. One of the first expert systems was MYCIN in 1974, which diagnosed
bacterial infections of the blood and suggested treatments. It did better than medical
students or practicing doctors, provided its limitations were observed. Namely, its
ontology included bacteria, symptoms, and treatments and did not include patients,
doctors, hospitals, death, recovery, and events occurring in time. Its interactions
depended on a single patient being considered. Since the experts consulted by the
knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that
the knowledge engineers forced what the experts told them into a predetermined
framework. The usefulness of current expert systems depends on their users having
common sense.
∙ Heuristic Classification
One of the most feasible kinds of expert systems given the present knowledge of AI is
to put some information in one of a fixed set of categories using several sources of
information. An example is advising whether to accept a proposed credit card
purchase. Information is available about the owner of the credit card, his record of
payment and also about the item he is buying and about the establishment from which
he is buying it (e.g., about whether there have been previous credit card frauds at this
establishment).
∙ The applications of AI are shown in Fig 1.1:
✔ Consumer Marketing
o Have you ever used any kind of credit/ATM/store card while shopping?
o if so, you have very likely been “input” to an AI algorithm
o All of this information is recorded digitally
o Companies like Nielsen gather this information weekly and search for
patterns
– general changes in consumer behavior
– tracking responses to new products
– identifying customer segments: targeted marketing, e.g., they find out
that consumers with sports cars who buy textbooks respond well to
offers of new credit cards.
Computer Science and Engineering &
Information Technology
BCS-404 ARTIFICIAL INTELLIGENCE (3-1-0) Cr.-04
Module - I
Formalized symbolic logic: Propositional logic-first order predicate logic, wff conversion
to clausal form, inference rules, the resolution principle, Dealing with inconsistencies and
uncertainties, fuzzy logic.
Module - II
Probabilistic Reasoning Structured knowledge, graphs, frames and related structures,
Knowledge organization and manipulation.
Module – III
Matching Techniques, Knowledge organizations, Management.
Module - IV
Natural Language processing, Pattern recognition, expert systems.
Text Book:
1. Artificial Intelligence, Dan W Patterson, Prentice Hall of India (1999)
Chapter-4, 5,7,9,10,11,12,13,15.
Reference Books:
1. Artificial Intelligence, Nils J.Nilsson, ELSEVIER.
2. E.Rich and K.Knight, Artificial Intelligence, - TMH
Overview of Artificial Intelligence
What is AI ?
∙ Artificial Intelligence (AI) is a branch of Science which deals with helping machines
find solutions to complex problems in a more human-like fashion.
∙ This generally involves borrowing characteristics from human intelligence, and applying
them as algorithms in a computer friendly way.
∙ A more or less flexible or efficient approach can be taken depending on the
, requirements established, which influences how artificial the intelligent behavior
appears
∙ Artificial intelligence can be viewed from a variety of perspectives. ✔ From the
perspective of intelligence, artificial intelligence is making machines "intelligent"
-- acting as we would expect people to act.
o The inability to distinguish computer responses from human responses
is called the Turing test.
o Intelligence requires knowledge
o Expert problem solving - restricting domain to allow including
significant relevant knowledge
✔ 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.
o Typically AI programs focus on symbols rather than numeric
processing.
o Problem solving - achieve goals.
o Search - seldom access a solution directly. Search may include a variety
of techniques.
o AI programming languages include:
– LISP, developed in the 1950s, is the early programming language strongly
associated with AI. LISP is a functional programming language with
procedural extensions. LISP (LISt Processor) was specifically designed for
processing heterogeneous lists -- typically a list of symbols. Features of LISP
are run- time type checking, higher order functions (functions that have other
functions as parameters), automatic memory management (garbage collection)
and an interactive environment.
– The second language strongly associated with AI is PROLOG. PROLOG
was developed in the 1970s. PROLOG is based on first order logic. PROLOG
is declarative in nature and has facilities for explicitly limiting the search
space.
– Object-oriented languages are a class of languages more recently used for AI
programming. Important features of object-oriented languages include:
concepts of objects and messages, objects bundle data and methods for
manipulating the data, sender specifies what is to be done, receiver decides
how to do it, inheritance (object hierarchy where objects inherit the attributes
of the more general class of objects). Examples of object-oriented languages
, are Smalltalk, Objective C, C++. Object oriented extensions to LISP (CLOS -
Common LISP Object System) and PROLOG (L&O - Logic & Objects) are
also used.
∙ Artificial Intelligence is a new electronic machine that stores large amount of
information and process it at very high speed
∙ The computer is interrogated by a human via a teletype It passes if the human cannot tell
if there is a computer or human at the other end
∙ The ability to solve problems
∙ 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
Importance of AI
∙ Game Playing
You can buy machines that can play master level chess for a few hundred dollars.
There is some AI in them, but they play well against people mainly through brute
force computation--looking at hundreds of thousands of positions. To beat a world
champion by brute force and known reliable heuristics requires being able to look at
200 million positions per second.
∙ Speech Recognition
In the 1990s, computer speech recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its keyboard tree for flight information
by a system using speech recognition of flight numbers and city names. It is quite
convenient. On the other hand, while it is possible to instruct some computers using
speech, most users have gone back to the keyboard and the mouse as it is still more
convenient.
∙ Understanding Natural Language
Just getting a sequence of words into a computer is not enough. Parsing sentences is
not enough either. The computer has to be provided with an understanding of the
domain the text is about, and this is presently possible only for very limited domains.
∙ Computer Vision
The world is composed of three-dimensional objects, but the inputs to the human eye
and computers' TV cameras are two dimensional. Some useful programs can work
solely in two dimensions, but full computer vision requires partial three-dimensional
information that is not just a set of two-dimensional views. At present there are only
limited ways of representing three-dimensional information directly, and they are not
, as good as what humans evidently use.
∙ Expert Systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their
knowledge in a computer program for carrying out some task. How well this works
depends on whether the intellectual mechanisms required for the task are within the
present state of AI. When this turned out not to be so, there were many disappointing
results. One of the first expert systems was MYCIN in 1974, which diagnosed
bacterial infections of the blood and suggested treatments. It did better than medical
students or practicing doctors, provided its limitations were observed. Namely, its
ontology included bacteria, symptoms, and treatments and did not include patients,
doctors, hospitals, death, recovery, and events occurring in time. Its interactions
depended on a single patient being considered. Since the experts consulted by the
knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that
the knowledge engineers forced what the experts told them into a predetermined
framework. The usefulness of current expert systems depends on their users having
common sense.
∙ Heuristic Classification
One of the most feasible kinds of expert systems given the present knowledge of AI is
to put some information in one of a fixed set of categories using several sources of
information. An example is advising whether to accept a proposed credit card
purchase. Information is available about the owner of the credit card, his record of
payment and also about the item he is buying and about the establishment from which
he is buying it (e.g., about whether there have been previous credit card frauds at this
establishment).
∙ The applications of AI are shown in Fig 1.1:
✔ Consumer Marketing
o Have you ever used any kind of credit/ATM/store card while shopping?
o if so, you have very likely been “input” to an AI algorithm
o All of this information is recorded digitally
o Companies like Nielsen gather this information weekly and search for
patterns
– general changes in consumer behavior
– tracking responses to new products
– identifying customer segments: targeted marketing, e.g., they find out
that consumers with sports cars who buy textbooks respond well to
offers of new credit cards.
