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Презентация на тему Artificial intelligence

Artificial Intelligence began in earnest with the
emergence of the modern computer during 1940s and
1950s. It was the ability of these new electronic machines
to store large amounts of information
It’s a branch of computer science concerned with the
study and creation of computer systems that can show
some form of intelligence
AI requires an understanding of related terms such as
intelligence, knowledge, reasoning, thought, learning and
a number of computer-related terms.
Intelligence is the ability to acquire, understand and
apply knowledge or the ability to exercise thought and
Food for this intelligence is knowledge.
Study and creation of conventional computer
Study of the mind.
Study of the body.
Study of Languages.
AI in business
Banks use artificial intelligence systems to organize
operations, invest in stocks, and manage properties
 AI in fiction
In science fiction AI — almost always strong AI — is
commonly portrayed as an upcoming power trying to
overthrow human authority
AI in Philosophy
The strong AI vs. weak AI debate is still a hot topic
amongst AI philosophers
Turing in 1950s, published an article in the Mind
Magazine, which triggered a controversial topic “Can a
Machine Think?” In that article he proposed a game
named imitation game which was later called
Other researchers said that the test determines
the intelligence of a programmer, who programs
it as compared to machine.
The machine
AI can replace human beings in some more specific jobs
for some business store and household day to day
activities and will help to sort out the manpower
AI machines can help in hospitals, providing food and
medicines where human being feared to be attacked by
such disease. Robotics is good example
It is estimated that AI will help human being in
aeronautics to know the universe.
The AI problems can be broadly divided into :
 Ordinary
 Formal Tasks
 Expert Tasks
Commonsense Reasoning: Commonsense reasoning
i.e. developing computer systems which has some
commonsense like if we let fall any thing on the floor it
may break
Perception: Perception includes two basic properties
what humans generally posses the i.e. Vision and Speech
Natural Language understanding: Communicating
various ideas is perhaps the most important thing that
differentiates humans from animals
Game Playing: Making computers playing games seems
to be very interesting that is why many researchers have
extensively contributed for computer game playing
Mathematics: Finding a proof for a theorem in
mathematics is certainly is an intelligent task. The study
of theorem proving play a significant part in development
of Artificial Intelligence Methods.
Expert Systems:
This area of AI deals in creation of computer
systems which can perform those tasks which now a
days is performed by experts. Expert systems are
the expert programs that manipulate encoded
knowledge to solve problem in a particular domain
e.g. Medical, Military
The AI programs manipulate symbols where as
conventional programs deal with numeric
The basis of AI program is that it must be able to
manipulate the knowledge and it has to be
represented in a way that in which it can be easily
The problems of AI deal with have a
combinational explosion of various solution paths.
E.g. Chess problem or in general game playing.
This assumption is made because this is the only way by
which knowledge can be manipulated to arrive at new
results. This symbol system has necessary and sufficient
means for general intelligent action
The assumption is only an assumption there is no way to
prove or disapprove it on some logical grounds
The knowledge should be general i.e. When ever we talk
about the solution to a given problem we must reach a
general solution which can be applicable to other
problems as well
It can be used in many situations even if it is not perfect
or complete. e.g. Chess playing
It should be easy to modify
It should be able to overcome its own volume by reducing
the range of overall possibilities
Three AI techniques:-
Use of Knowledge
There are two ways in which the AI
problem can be represented:
 State
Space Representation
 Problem Reduction
State: AI problem can be represented as a well formed
set of possible states. State can be Initial State i.e.
starting point, Goal State i.e. destination point and
various other possible states between them which are
formed by applying certain set of rules
Space: In an AI problem the exhaustive set of all
possible states is called space
Search: In simple words search is a technique which
takes the initial state to goal state by applying certain set
of valid rules while moving through space of all possible
Production systems provide such structures, which helps
the search procedure to perform efficiently. The process
of solving the problem can be modeled as a production
A production system consists of the following:
A set of Rules
Control Strategy
Knowledge Data Base
Rule Applier
Let us consider that the Initial state of a problem to be
BADCCB and Goal State is ABBCCD. The set of rules are as
follows (notice they are written as Left Hand side (IF) and
Right Hand Side (Then Condition)
Starting form Initial State BADCCB, we can see that the rules
1,2,3 can be applied at this state to move on to next one. As BA
can be changed in to AB, DC can be changed in to CD and CB
can be changed in to BC. This conflict will be resolved by
choosing a control strategy. Here “Apply First applicable rule”
strategy has been chosen. Control Strategy also take care of the
fact that whether the rule pointer will stay on the position after
applying the rule or will it move to first rule again.
Table representing working of production rules
Properties of Control Strategy:
Control strategy should cause movement: The
control strategy should be chosen in such a
way that it should cause movement other wise
same stated will be repeated again and the
search will be able to move ahead in space of a
given problem
Control strategy should be systematic
Factors influencing the direction of the
 Nature
of states
 Branching factor
Types of Search Techniques:
The searching process in AI can be divided
in two parts based on the amount of the
knowledge it is carrying
 Un-Informed
Search Techniques
 Informed Search Techniques
 The
only information these kinds of search
procedures have is about Initial State, Goal
State and Set of rules that means they don’t
have domain specific knowledge
There are two types of search blind
 Depth-First
 Breadth-First
It can be noted that both above said search methods are
systematic and force mobility, which are primary
conditions of any good search process. In the perspective of
AI, many times one may not get the best solution. In such
cases it is required to obtain a very good solution
The heuristics which are required for solving problems are
generally represented as heuristic functions. Heuristic
functions convert the problems states in to quantitative
Following are the search algorithms which
use the heuristic functions:
 Hill
 Best First Search
 A* Algorithm
 AO* Algorithm
 Beam Search
 Constraint Satisfaction