Introduction To Artificial Intelligence and applications

ARTIFICIAL
INTELLIGENCE
(Unit I)
VI Sem B.E. Computer Sc. &
Engg
Department of Computer Science & Engineering
Priyadarshini Bhagwati College of Engineering,
Nagpur
(Even 2021)
Kapil N Hande
Head of the Department

Introduction
A branch of Computer Science named Artificial
Intelligence pursues creating the computers or
machines as intelligent as human beings.
One thing it could be is Making
computational models of human behavior.
One way, which would be a kind of cognitive
One way, which would be a kind of cognitive
science, is to do experiments on humans, see
how they behave in certain situations and see if
you could make computers behave in that same
way.
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What is AI?
Artificial Intelligence is the development of
computer systems that are able to perform
tasks that would require human
intelligence.
Examples of these tasks are visual perception,
Examples of these tasks are visual perception,
speech recognition, decision-making, and
translation between languages.
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Definitions
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.
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Definitions
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.
The exciting new effort to make computers
think … machines with minds, in the full literal
sense.
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Definitions
The study of mental faculties through the use
of computational models.
A field of study that seeks to explain and
emulate intelligent behavior in terms of
computational processes.
computational processes.
The study of how to make computers do
things at which, at the moment, people are
better. (Rich Knight, 1991 )
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Definitions
Computational models of human behavior?
• Programs that behave (externally) like
humans
Computational models of human “thought”
processes?
processes?
Programs that operate (internally) the way
humans do
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What is Intelligence?
The ability of a system to calculate, reason,
perceive relationships and analogies, learn from
experience, store and retrieve information from
memory, solve problems, comprehend complex
ideas, use natural language fluently, classify,
generalize, and adapt new situations.
• “the capacity to learn and solve problems”
• “the capacity to learn and solve problems”
In particular,
the ability to solve novel problems
the ability to act rationally
the ability to act like humans
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What’s involved in Intelligence?
Ability to interact with the real world
• to perceive, understand, and act
• e.g., speech recognition and
understanding and synthesis
• e.g., image understanding
• e.g., image understanding
• e.g., ability to take actions, have an effect
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What’s involved in Intelligence?
Reasoning and Planning
• modeling the external world, given input
• solving new problems, planning, and making
decisions
• ability to deal with unexpected problems,
uncertainties
Learning and Adaptation
• we are continuously learning and adapting
• our internal models are always being “updated”
• e.g., a baby learning to categorize and recognize
animals
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Goals of AI
• To Create Expert Systems: The systems
which exhibit intelligent behavior, learn,
demonstrate, explain, and advice its users.
To Implement Human Intelligence in
• To Implement Human Intelligence in
Machines: Creating systems that understand,
think, learn, and behave like humans.
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What Contributes to AI?
Artificial intelligence is a science and technology
based on disciplines such as Computer Science,
Biology, Psychology, Linguistics, Mathematics, and
Engineering. A major thrust of AI is in the
development of computer functions associated with
human intelligence, such as reasoning, learning, and
human intelligence, such as reasoning, learning, and
problem solving.
Out of the following areas, one or multiple areas can
contribute to build an intelligent system.
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History of AI
1931 The Austrian Kurt Gödel shows that in first-order predicate logic all
true statements are derivable. In higher-order logics, on the other hand,
there are true statements that are unprovable
1937 Alan Turing points out the limits of intelligent machines with the
halting problem
1943 McCulloch and Pitts model neural networks and make the connection to
propositional logic.
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propositional logic.
1950 Alan Turing defines machine intelligence with the Turing test and
writes about learning machines and genetic algorithms
1951 Marvin Minsky develops a neural network machine. With 3000 vacuum
tubes he simulates 40 neurons.
1955 Arthur Samuel (IBM) builds a learning checkers program that plays
better than its developer

History of AI
1956 McCarthy organizes a conference in Dartmouth College. Here the name
Artificial Intelligence was first introduced. Newell and Simon of
Carnegie Mellon University (CMU) present the Logic Theorist, the first
symbol-processing computer program
1958 McCarthy invents at MIT (Massachusetts Institute of Technology) the
high-level language LISP. He writes programs that are capable of
modifying themselves.
1959 Gelernter (IBM) builds the Geometry Theorem Prover.
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1959 Gelernter (IBM) builds the Geometry Theorem Prover.
1961 The General Problem Solver (GPS) by Newell and Simon imitates human
thought
1963 McCarthy founds the AI Lab at Stanford University.
1965 Robinson invents the resolution calculus for predicate logic

History of AI
1966 Weizenbaum’s program Eliza carries out dialog with people in
natural language
1969 Minsky and Papert show in their book Perceptrons that the
perceptron, a very simple neural network, can only represent
linear functions
1972 French scientist Alain Colmerauer invents the logic
programming language PROLOG
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1976 Shortliffe and Buchanan develop MYCIN, an expert system for
diagnosis of infectious diseases, which is capable of dealing with
uncertainty
1981 Japan begins, at great expense, the “Fifth Generation Project”
with the goal of building a powerful PROLOG machine.
1982 R1, the expert system for configuring computers, saves Digital
Equipment Corporation 40 million dollars per year

History of AI
1986 Renaissance of neural networks through, among others, Rumelhart,
Hinton and Sejnowski. The system Nettalk learns to read texts aloud
1990 Pearl , Cheeseman , Whittaker, Spiegelhalter bring probability theory
into AI with Bayesian networks . Multi-agent systems become popular.
1992 Tesauros TD-gammon program demonstrates the advantages of
reinforcement learning.
1993 Worldwide RoboCup initiative to build soccer-playing autonomous
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1993 Worldwide RoboCup initiative to build soccer-playing autonomous
robots
1995 From statistical learning theory, Vapnik develops support vector
machines, which are very important today.
1997 IBM’s chess computer Deep Blue defeats the chess world champion
Gary Kasparov.
2003 The robots in RoboCup demonstrate impressively what AI and robotics
are capable of achieving.

History of AI
2006 Service robotics becomes a major AI research area.
2009 First Google self-driving car drives on the California freeway.
2010 Autonomous robots begin to improve their behavior through learning.
2011 IBM’s “Watson” beats two human champions on the television game
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2011 IBM’s “Watson” beats two human champions on the television game
show “Jeopardy!”. Watson understands natural language and can
answer difficult questions very quickly
2015 Daimler premiers the first autonomous truck on the Autobahn.
Google self-driving cars have driven over one million miles and operate
within cities.
Deep learning enables very good image classification.
Paintings in the style of the Old Masters can be automatically generated
with deep learning. AI becomes creative!

History of AI
2016 The Go program AlphaGo by Google
DeepMind beats the European champion 5:0
in January and Korean Lee Sedol, one of the
world’s best Go players, 4:1 in March. Deep
learning techniques applied to pattern
recognition, as well as reinforcement learning
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recognition, as well as reinforcement learning
and Monte Carlo tree search lead to this
success.

Applications of AI
1. Artificial Intelligence in Healthcare:
-AI is a study realized to emulate human intelligence
into computer technology that could assist both, the
doctor and the patients in the following ways:
By providing a laboratory for the examination,
representation and cataloguing medical information
By devising novel tool to support decision making and
By devising novel tool to support decision making and
research
By integrating activities in medical, software and
cognitive sciences
By offering a content rich discipline for the future
scientific medical communities.
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Applications of AI
2. Artificial Intelligence in business:
Robotic process automation is being applied to
highly repetitive tasks normally performed by
humans.
Chatbots have already been incorporated into
websites and e companies to provide immediate
websites and e companies to provide immediate
service to customers.
3. AI in education: It automates grading, giving
educators more time. It can also assess students and
adapt to their needs, helping them work at their
own pace.
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Applications of AI
4. AI in Autonomous vehicles: Just like humans, self-
driving cars need to have sensors to understand the
world around them and a brain to collect, processes
and choose specific actions based on information
gathered.
AI has several applications for these vehicles and among
them the more immediate ones are as follows:
Directing the car to gas station or recharge station when
Directing the car to gas station or recharge station when
it is running low on fuel.
Adjust the trips directions based on known traffic
conditions to find the quickest route.
Incorporate speech recognition for advanced
communication with passengers.
Natural language interfaces and virtual assistance
technologies.
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Applications of AI
5. AI for robotics will allow us to address the
challenges in taking care of an aging population
and allow much longer independence. It will
drastically reduce, may be even bring down traffic
accidents and deaths, as well as enable disaster
response for dangerous situations
response for dangerous situations
6. Software systems: Diagnosis of software, technical
components
7. Adaptive software systems: Intelligent interfaces,
Intelligent helper applications, Web applications,
(softbots, shopbots)
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8. AI applications: information retrieval.
Web search engines
– Improve the quality of search
– Rely on methods developed in AI
Semantic web:
– From information to knowledge sharing
– From information to knowledge sharing
– OWL (The W3C Web Ontology Language (OWL) is a
Semantic Web language designed to represent rich
and complex knowledge about things, groups of
things, and relations between things.)
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9. AI applications: Speech recognition
Speech recognition systems:
– Hidden Markov models
Adaptive speech systems
– Adapt to the user (training)
– continuous speech
– continuous speech
Multi-user speech recognition systems
– Restricted (no training)
– Customer support:
• Airline schedules, baggage tracking
• Credit card companies.
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10. Applications: Space exploration
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AI applications: Medicine
Medical diagnosis:
Medical imaging: Image guided surgery
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AI applications: Transportation
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AI applications: Game playing
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AI APPLICATIONS
Robotic toys
– Sony’s Aibo
Humanoid robot
Humanoid robot
– Honda’s ASIMO
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Everyday Examples of AI
1. Smartphones:From the obvious AI features such as
the built-in smart assistants to not so obvious ones
such as the portrait mode in the camera, AI is
impacting our lives every day.
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2. Smart Cars and Drones: Tesla cars are a prime example of
how the AI is impacting our daily life. Did you know that
all the Tesla cars are connected and the things that your
car learns is shared across all the cars?
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3. Social Media Feeds: From the feeds that you see in your
timeline to the notifications that you receive from these
apps, everything is curated by AI. AI takes all your past
behavior, web searches, interactions, and everything else
that you do when you are on these websites and tailors the
experience just for you.
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4. Another great example of how AI impacts our lives
are the music and media streaming services that
we are using on a daily basis. Whether you are
using Spotify, Netflix, or YouTube, AI is making the
decisions for you.
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5. Video Games: The video game industry is probably
one of the earliest adopters of AI. The integration
started very small with the use of AI to generate
random levels that people can play.
When you are playing a game such as PUBG or
Fortnite, you essentially start against a couple of AI-
Fortnite, you essentially start against a couple of AI-
powered bots and then move to play against real
players.
Just know that if you play any game, you are using
AI.
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6. Online Ads Network: One of the biggest users of
artificial intelligence is the online ad industry
which uses AI to not only track user statistics but also
serve us ads based on those statistics.
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PROBLEM SPACES AND SEARCH
Building a system to solve a problem requires the
following steps
• Define the problem precisely including detailed
specifications and what constitutes an acceptable
solution
• Analyze the problem thoroughly for some
features may have a dominant affect on the
features may have a dominant affect on the
chosen method of solution
• Isolate and represent the background
knowledge needed in the solution of the
problem
• Choose the best problem solving techniques in
the solution.
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Defining the Problem as state space Search
Problems dealt with in artificial intelligence
generally use a common term called 'state'.
A state represents a status of the solution at
a given step of the problem solving
procedure.
The problem solving procedure applies an
The problem solving procedure applies an
operator to a state to get the next state
The process of applying an operator to a
state and its subsequent transition to the
next state, thus, is continued until the goal
(desired) state is derived.
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The opening position can be defined as the
initial state and a winning position as a goal
state, there can be more than one.
The representation of games in this way
leads to a state space representation and it
leads to a state space representation and it
is natural for well organized games with
some structure
It means that the solution involves using
known techniques and a systematic search.
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A problem space can also be considered to be
a search space because in order to solve the
problem, we will search the space for a goal
state.
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Problem of Playing a Chess
Suppose we start with the problem
statement “Play Chess”
To build a program that would play chess, we
would first have to specify the starting
position of the chess board, the rules that
position of the chess board, the rules that
define the legal moves, and the board
position that represent the win position for
one side or the other.
It is fairly easy to provide the complete and
formal problem description.
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The starting position can be described as an
8x8 array where each position contains the
symbol standing for the appropriate piece
in the official chess opening position.
We can define our goal as any board position
We can define our goal as any board position
in which the opponent does not have a legal
move and his or her King is under attack.
The legal moves can be described as a set of
rules consisting of two parts.
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A left side that defines the pattern to be
matched against the current board position
and right side that describes the change to
be made to the board position to reflect the
move.
However if we write the rules like above, we
have to write very large number of them
since there has to be separate rule for
roughly 10120 possible board positions.
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Write the rules describing the legal moves in
as general way as possible.
Introduce some convenient notation for
describing patterns and substitutions.
describing patterns and substitutions.
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Rule for Playing Chess
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State Space Representation
It allows for some formal definition of the
problem as the need to convert some given
situation into some desired situation using a
set of permissible operations.
It permits us to define the process of solving
It permits us to define the process of solving
a particular problem as a combination of
known technique and search, the general
technique of exploring the space to try to
find some path from the current state to goal
state.
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A Water Jug Problem In Artificial Intelligence
Problem: We are given two jugs, a 4-gallon one and 3-gallon
one. Neither has any measuring marked on it. There is a
pump, which can be used to fill the jugs with water. How
can we get exactly 2 gallons of water into 4-gallon jug?
The state space for this problem can be described
as the set of ordered pairs of integers (X, Y) such
that X = 0, 1, 2, 3 or 4 and Y = 0, 1, 2 or 3
that X = 0, 1, 2, 3 or 4 and Y = 0, 1, 2 or 3
X is the number of gallons of water in the 4-gallon
jug
Y the quantity of water in the 3-gallon jug.
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A Water Jug Problem In Artificial Intelligence
The start state is (0, 0) and the goal state is
(2, n) for any value of n, as the problem does
not specify how many gallons need to be
filled in the 3-gallon jug (0, 1, 2, 3).
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The operators to be used to solve the problem
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Assumptions, not mentioned, in the problem state.
1. We can fill a jug from the pump.
2. We can pour water out a jug, onto the ground.
3. We can pour water out of one jug into the
other
4. No other measuring devices are available
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Water Jug Problem
To solve the water jug problem, all we need, in
addition to the problem description given
above, is a control structure which loops
through a simple cycle in which some rule
whose left side matches the current state is
chosen, the appropriate change to the state is
chosen, the appropriate change to the state is
made as described in the corresponding right
side and the resulting state is checked to see if
it corresponds to a goal state.
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Issues in Water Jug Problem
It has been shown above how an informal
problem state (stated in English) has been
converted into a formal one (Fig. 2.4) with
the help of a water jug problem.
In doing so some issues which affect the
In doing so some issues which affect the
approach towards the solution are:
The rules should be stated explicitly and not
written because they are allowable
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Issues in Water Jug Problem
Now consider the rules 3 and 4 should or
should not these rules be included in the list of
available operators?
The rules 11 and 12 are special purpose rules,
written to capture special purpose knowledge
to solve this problem.
to solve this problem.
It is thus clear that to create a program for
solving a problem simply the formal description
of the problem has to be generated using the
knowledge about the given problem. This
process is called operationalization
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To formalize a problem the following steps are needed
(i)Define the problem precisely, giving the
specification of what the initial situation (s) and
the final situation (s) will be.
(ii) Analyze the problem because a few important
features can have immense impact on the
suitability of different techniques available for
suitability of different techniques available for
solving the problem.
(iii) Represent the knowledge completely, which is
necessary to solve problem in a given domain.
(iv) Choose the best technique(s) and apply it
(them) to the particular problem.
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Summarizingly any problem can be solved by the
following series of step
1. Define a state space which contains all the possible
configurations of the relevant objects and even some
impossible ones.
2. Specify one or more states within that space which
would describe possible situations from which the
problem solving process may start. These states are
problem solving process may start. These states are
called the initial states.
3. Specify one or more states which would be
acceptable as solutions to the problem. These states
are called goal states.
4. Specify a set of rules which describe the actions
(operators) available and a control strategy to
decide the order of application of these rules.
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Approach
The problem can be solved by collecting all
knowledge of the problem, (in the present
case, water jug problem) with the help of
production rules and using an appropriate
production rules and using an appropriate
control strategy; move through the problem
space until a path from an initial state to
goal state is found.
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PRODUCTION SYSTEM
A production is a rule consisting of a
situation recognition part and an action part.
A production is a situation-action pair in
which the left side is a list of things to watch
for and the right side is a list of things to do
for and the right side is a list of things to do
so
The production system is a model of
computation that can be applied to
implement search algorithms and model
human problem solving
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Components of Production System
1) Set of production rules, which are of the form
A®B.
2) A database, which contains all the appropriate
information for the particular task.
3) A control strategy that specifies order in which
the rules will be compared to the database of
the rules will be compared to the database of
rules and a way of resolving the conflicts that
arise when several rules match simultaneously.
4) A rule applier, which checks the capability of
rule by matching the current state with the left
hand side of the rule and finds the appropriate
rule from database of rules.
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Features of Production System
Expressiveness and intuitiveness
Simplicity: The structure of each sentence in
a production system is unique and uniform
Modularity
Modifiability
Knowledge intensive
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Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us
about which rule has to be applied next
while searching for the solution of a problem
while searching for the solution of a problem
within problem space.
Control Strategy should cause Motion
Control strategy should be Systematic
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One systematic Control Strategy for the Water Jug problem
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Advantages of Depth First Search
Requires less memory since only the nodes
on current path are stored.
May find the solution without examining
much of the search space at all.
much of the search space at all.
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Advantages of Breadth First Search
Will not get trapped in exploring a blind
alley
If there is a solution then Breadth First
Search guaranteed to find it. Longer paths
Search guaranteed to find it. Longer paths
are never explored until all shorter paths
have been examined.
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The Traveling Salesman Problem
Problem: A traveler needs to visit all the cities from a list,
where distances between all the cities are known and
each city should be visited just once. What is the shortest
possible route that he visits each city exactly once and
returns to the origin city?
The salesman‘s goal is to keep both the travel
costs and the distance traveled as low as
costs and the distance traveled as low as
possible.
Focused on optimization, TSP is often used in
computer science to find the most efficient
route for data to travel between
various nodes.
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TSP has been studied for decades and several
solutions have been theorized.
Rather than focus on finding the most
effective route, TSP is often concerned with
finding the cheapest solution.
finding the cheapest solution.
Travelling salesman problem is the most
notorious computational problem
We can use brute-force approach to
evaluate every possible tour
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For n number of vertices in a graph, there
are (n - 1)! Number of possibilities.
This phenomenon is called combinatorial
explosion.
Technique called as branch and bound can be
Technique called as branch and bound can be
used to find the solution to the TPS problem.
Begin generating complete paths, keeping track of
shortest paths found so far. Give up exploring any
path as soon as its partial length becomes greater
than the shortest path found so far.
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Heuristics Search
A Heuristic is a technique to solve a problem
faster than classic methods, or to find an
approximate solution when classic methods
cannot.
This is a kind of a shortcut as we often trade
one of optimality, completeness, accuracy,
one of optimality, completeness, accuracy,
or precision for speed.
A heuristic is a method that
• might not always find the best solution
• but is guaranteed to find a good solution in
reasonable time.
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Heuristics Search
In mathematical optimization and computer
science, heuristic (from Greek I find, discover) is a
technique designed for solving a problem more quickly
when classic methods are too slow, or for finding an
approximate solution when classic methods fail to find
approximate solution when classic methods fail to find
any exact solution.
In a way, it can be considered a shortcut.
The objective of a heuristic is to produce a solution in a
reasonable time frame that is good enough for solving
the problem at hand.
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Heuristics Search
One example of one general purpose heuristics that
is useful in variety of combinatorial problems is the
nearest neighbor heuristics, which work by
selecting the locally superior alternative at each
step.
Applying it to TSP we arrive at following
procedure.
procedure.
– Arbitrarily select the starting city
– To select the next city, look at all the cities not
yet visited, and select the one closest to current
city. Go to it next.
– Repeat step 2 until all cities have been visited.
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Heuristics Search
There are two major ways in which domain-
specific, heuristic knowledge can be
incorporated in rule based search procedure.
• In the rules themselves. For example, rule for
chess playing system might describe not
simply the set of legal moves but rather a set
simply the set of legal moves but rather a set
of “sensible moves” as determined by the
writer.
• As a heuristic function that evaluates the
individual problems states and determines
how desirable they are.
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A Heuristic Function
A heuristic function is a function that maps
from problem state descriptions to
measures of desirability, usually
represented as numbers.
Value of the heuristic function at given node
in the search process will give as good an
in the search process will give as good an
estimate as possible of whether that node is
on desired path to solution.
Well designed heuristic functions can play an
important part in efficiently guiding a
search process towards a solution.
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The purpose of the heuristic function is to guide
the search process in most profitable direction
by suggesting which path to follow first when
more than one path is available.
more than one path is available.
In general there is a trade-off between the cost
of evaluating a heuristic function and savings in
search time that the function provides.
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With respect to the heuristic function we can
define Artificial Intelligence as the study of
techniques for solving exponentially hard
problems in a polynomial time by
problems in a polynomial time by
exploiting knowledge about the problem
domain.
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PROBLEM CHARACTERISTICS
In order to choose the most appropriate method for
a particular problem, it is necessary to analyze the
problem along several key dimensions.
Is the problem decomposable into set of sub
problems?
Can the solution step be ignored or undone?
Is the problem universally predictable?
Is a good solution to the problem obvious
without comparison to all the possible
solutions?
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PROBLEM CHARACTERISTICS
Is the desire solution a state of world or a
path to a state?
Is a large amount of knowledge absolutely
required to solve the problem?
required to solve the problem?
Will the solution of the problem required
interaction between the computer and the
person?
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1. Is the Problem Decomposable?
Suppose we want to solve the problem of
computing the expression.
We can solve this problem by breaking it
down into three smaller problems, each of
which we can then solve by using a small
collection of specific rules.
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Is the Problem Decomposable?
Problem tree will be generated by the
process of problem decomposition as it can
be exploited by simple recursive
integration program.
integration program.
Using this technique of problem
decomposition we can easily solve the very
large problems.
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Consider the following problem of Blocks
World
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Assume following operators are available
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The idea of the solution is to reducing the
problem of getting B on C, and A on B to two
separate problems

In this problem, the two sub problems are
not independent.
They interact and those interaction must be
considered to arrive at the solution for the
entire problem.
entire problem.
These two examples, symbolic integration
and blocks world, illustrate the difference
between decomposable and non-
decomposable problems.
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2. Can the solution step be ignored or undone?
Consider the problem of proving a
mathematical problem.
We proceed by proving a lemma, but we
realize that lemma is no help at all.
Are we in trouble? No……
Are we in trouble? No……
Consider the different problem
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In attempting to solve the 8-puzzle we might
make a stupid move.
An additional step must be performed to
undo each incorrect step, whereas no action
was required to “undo” a useless lemma
was required to “undo” a useless lemma
The control structure for 8-puzzle solver
must keep track of a order in which the
operations are performed.
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Can The Solution Step Be Ignored Or Undone?
The recoverability of the problem plays an
important role in determining the
complexity of the control structure
necessary for the problem solution.
necessary for the problem solution.
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3. Is the problem universally predictable?
In case of 8-puzzle problem, it is possible to
plan entire sequence of moves and be
confident that we know what the resulting
state will be.
We can use planning to avoid having to undo
We can use planning to avoid having to undo
actual moves.
Thus a control structure that allows
backtracking is necessary.
However in other games this planning
process may not be possible.
K N Hande 101

Suppose we want play Bridge.
But it is not possible to do such planning with
certainty, since we cannot know where exactly
all the cards are.
all the cards are.
So here we can investigate several plans and use
probabilities of the various outcomes to choose
a plan that has highest estimated probability of
leading to good score
K N Hande 102

So 8-puzzle is certain-outcome problem and
Bridge is uncertain-outcome problem.
Planning can be defined here is problem-
solving without a feedback from
environment.
environment.
For solving certain-outcome problem,
planning will generate the sequence of
operators which will guarantee to lead the
solution of the problem.
K N Hande 103

For uncertain-outcome problems planning
can at best generate the operators that has a
good probability of leading a solution.
These problem characteristics, ignorable
versus recoverable versus irrecoverable
versus recoverable versus irrecoverable
and certain-outcome versus uncertain-
outcome, interact in an interesting way.
Thus one of the hardest type of problem to
solve is the irrecoverable, uncertain-
outcome
K N Hande 104

A few examples of such problems are:
1. Playing bridge
2. Controlling a robot arm
3. Helping a lawyer decide how to defend his
client against a murder charge.
K N Hande 105

4. Is A Good Solution Absolute Or Relative?
Consider the problem of answering questions
based on database of simple facts:
K N Hande 106

Suppose we ask the question “Is Marcus
Alive?”
By using predicate logic and using formal
inference methods we can easily answer
this question.
this question.
In fact either of the two reasoning paths will
lead to the answer as shown in following
figure.
K N Hande 107

But now consider again the traveling salesman
problem. Our goal is to find the shortest route that
will visit the each city exactly once.
K N Hande 109

These two examples illustrate the difference
between any-path problems and best-path
problems.
Best-path problems are computationally
Best-path problems are computationally
harder than any-path problems.
K N Hande 111

Introduction To Artificial Intelligence and applications

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