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1. Artificial Intelligence
BY:
HANAMANT JAKARADDI
DEPARTMENT OF COMPUTER APPLICATIONS
ACHARYA INSTITUTE OF GRADUATE STUDIES

2. Table of Content
Introduction to Artificial
Intelligence: Definitions
AI Applications
AI representation
Properties of internal
Representation
Heuristic search techniques
Best first search
Depth first Search
mean and end analysis
A* and AO* Algorithm
Game Playing
Minimize search procedure
Alpha beta cutoffs

3. Introduction to Artificial Intelligence
Some Definitions
 AI is a technique that facilitates a machine to perform all cognitive
functions such as perceiving, learning and reasoning that are
otherwise performed by humans.
 “The Science and Engineering of making intelligent machines,
especially intelligent Computer programs is Artificial intelligence” –
JOHN MC CARTHY [Father of AI]
 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.

4. Continued…
The exciting new effort to make computers think … machines with
minds, in the full literal sense.
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.
The study of how to make computers do things at which, at the
moment, people are better.
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.

5. Continued…
Artificial Intelligence (AI) is the machine-displayed intelligence that
simulates human behavior or thinking and can be trained to solve
specific problems. AI is a combination of Machine Learning techniques
and Deep Learning. AI models that are trained using vast volumes of
data have the ability to make intelligent decisions.
Deep learning is an artificial intelligence (AI) function that imitates the
workings of the human brain in processing data and creating patterns
for use in decision making. Deep learning is a subset of machine
learning.
Modern AI focuses on the definition: “engineering” approach success
is judged by how well the agent performs.

6. AI Facts and Figures
 According to Tractica, a market research firm, the Global AI Market
is expected to reach a revenue of 118 billion by dollars by 2025.
 As per Gartner, 37% of organizations have implemented AI in some
form. The percentage of enterprises employing AI grew 270% over
the past four years.
 According to Servion Global Solutions, by 2025, 95% of customer
interactions will be powered by AI.
 A recent 2020 report from Statista reveals that the global AI software
market is expected to grow approximately 54% year-on-year and is
expected to reach a forecast size of 22.6 billion U.S. dollars.
 Let’s now take a look at how AI is used in different domains.

7. Different Types of AI
 Modeling exactly how humans actually think
 Modeling exactly how humans actually act
 Modeling how ideal agents “should think”
 Modeling how ideal agents “should act”

8. Artificial Intelligence Applications
 Gaming − AI plays crucial role in strategic games such as chess,
poker, tic-tac-toe, etc., where machine can think of large number of
possible positions based on heuristic knowledge.
 Natural Language Processing − It is possible to interact with the
computer that understands natural language spoken by humans.
 Expert Systems − There are some applications which integrate
machine, software, and special information to impart reasoning and
advising. They provide explanation and advice to the users.
 Vision Systems − These systems understand, interpret, and
comprehend visual input on the computer.
 A spying airplane takes photographs, which are used to figure out spatial
information or map of the areas.
 Doctors use clinical expert system to diagnose the patient.
 Police use computer software that can recognize the face of criminal with the
stored portrait made by forensic artist.

9. Continued…
 Speech Recognition − Some intelligent systems are capable of hearing
and comprehending the language in terms of sentences and their
meanings while a human talks to it. It can handle different accents, slang
words, noise in the background, change in human’s noise due to cold,
etc.
 Handwriting Recognition − The handwriting recognition software
reads the text written on paper by a pen or on screen by a stylus. It can
recognize the shapes of the letters and convert it into editable text.
 Intelligent Robots − Robots are able to perform the tasks given by a
human. They have sensors to detect physical data from the real world
such as light, heat, temperature, movement, sound, bump, and pressure.
They have efficient processors, multiple sensors and huge memory, to
exhibit intelligence. In addition, they are capable of learning from their
mistakes and they can adapt to the new environment.

10. Artificial Intelligence Applications
AI in E-Commerce
AI in Finance
AI in Business
AI in Banking
AI in Marketing
AI in Social Media
AI in Healthcare
AI in Education
AI in Law
AI in Transportation
AI in Automobiles
AI in Manufacturing
AI in Navigation
AI in Robotics
AI in Gaming

11. AI in E-commerce
 Personalized Shopping: Artificial Intelligence technology is used to create
recommendation engines through which you can engage better with your
customers. These recommendations are made in accordance with their browsing
history, preference, and interests. It helps in improving your relationship with your
customers and their loyalty towards your brand.
 AI-powered Assistants: Virtual shopping assistants and chatbots help improve the
user experience while shopping online. Natural Language Processing is used to
make the conversation sound as human and personal as possible. Moreover, these
assistants can have real-time engagement with your customers. Did you know that
on amazon.com, soon, customer service could be handled by chatbots?
 Fraud Prevention: Credit card frauds and fake reviews are two of the most
significant issues that E-Commerce companies deal with. By considering the
usage patterns, AI can help reduce the possibility of credit card frauds taking
place. Many customers prefer to buy a product or service based on customer
reviews. AI can help identify and handle fake reviews.

12. AI in Finance
 AI in personal finance applications, such as Intuit Mint or TurboTax,
is disrupting financial institutions. Applications such as these collect
personal data and provide financial advice. Other programs, such as
IBM Watson, have been applied to the process of buying a home.
Today, artificial intelligence software performs much of the trading
on Wall Street.

13.  Machine learning algorithms are being integrated into analytics and
customer relationship management (CRM) platforms to uncover
information on how to better serve customers.
 Chatbots have been incorporated into websites to provide immediate
service to customers. Automation of job positions has also become a
talking point among academics and IT analysts.
AI in Business

14. AI in Banking
 AI in banking. Banks are successfully employing chatbots to make
their customers aware of services and offerings and to handle
transactions that don't require human intervention. AI virtual
assistants are being used to improve and cut the costs of compliance
with banking regulations. Banking organizations are also using AI to
improve their decision-making for loans, and to set credit limits and
identify investment opportunities.

15.  Artificial intelligence applications are popular in the marketing
domain as well.
 Using AI, marketers can deliver highly targeted and personalized ads
with the help of behavioral analysis, pattern recognition, etc. It also
helps with retargeting audiences at the right time to ensure better
results and reduced feelings of distrust and annoyance.
 AI can help with content marketing in a way that matches the brand's
style and voice. It can be used to handle routine tasks like
performance, campaign reports, and much more.
 Chatbots powered by AI, Natural Language Processing, Natural
Language Generation, and Natural Language Understanding can
analyze the user's language and respond in the ways humans do.
AI in Marketing

16.  Instagram: On Instagram, AI considers your likes and the accounts you
follow to determine what posts you are shown on your explore tab.
 Facebook: Artificial Intelligence is also used along with a tool called
DeepText. With this tool, Facebook can understand conversations
better. It can be used to translate posts from different languages
automatically.
 Twitter: AI is used by Twitter for fraud detection, removing
propaganda, and hateful content. Twitter also uses AI to recommend
tweets that users might enjoy, based on what type of tweets they
engage with.
AI in Social Media

17.  AI is used in healthcare to build sophisticated machines that can detect diseases
and identify cancer cells.
 AI can help analyze chronic conditions with lab and other medical data to ensure
early diagnosis.
 AI uses the combination of historical data and medical intelligence for the
discovery of new drugs.
 The biggest bets are on improving patient outcomes and reducing costs.
Companies are applying machine learning to make better and faster diagnoses than
humans. One of the best-known healthcare technologies is IBM Watson. It
understands natural language and can respond to questions asked of it. The system
mines patient data and other available data sources to form a hypothesis, which it
then presents with a confidence scoring schema. Other AI applications include
using online virtual health assistants and chatbots to help patients and healthcare
customers find medical information, schedule appointments, understand the billing
process and complete other administrative processes. An array of AI technologies
is also being used to predict, fight and understand pandemics such as COVID-19.
AI in Healthcare

18. AI in Education
 AI can automate grading, giving educators more time. It can assess
students and adapt to their needs, helping them work at their own pace.
AI tutors can provide additional support to students, ensuring they stay
on track. And it could change where and how students learn, perhaps
even replacing some teachers.

19. AI in Law
 AI in law. The discovery process -- sifting through documents -- in
law is often overwhelming for humans. Using AI to help automate the
legal industry's labor-intensive processes is saving time and
improving client service. Law firms are using machine learning to
describe data and predict outcomes, computer vision to classify and
extract information from documents and natural language processing
to interpret requests for information.

20. AI in Transportation
 AI in transportation. In addition to AI's fundamental role in
operating autonomous vehicles, AI technologies are used in
transportation to manage traffic, predict flight delays, and make ocean
shipping safer and more efficient.

21.  Artificial Intelligence is used to build self-driving vehicles.
 AI can be used along with the vehicle’s camera, radar, cloud services,
GPS, and control signals to operate the vehicle.
 AI can improve the in-vehicle experience and provide additional
systems like emergency braking, blind-spot monitoring, and driver-
assist steering.
AI in Automobiles

22. AI in Manufacturing
 AI in manufacturing. Manufacturing has been at the forefront of
incorporating robots into the workflow. For example, the industrial
robots that were at one time programmed to perform single tasks and
separated from human workers, increasingly function as cobots:
Smaller, multitasking robots that collaborate with humans and take on
responsibility for more parts of the job in warehouses, factory floors
and other workspaces.

23.  Based on research from MIT, GPS technology can provide users with
accurate, timely, and detailed information to improve safety.
 The technology uses a combination of Convolutional Neural Network
and Graph Neural Network, which makes lives easier for users by
automatically detecting the number of lanes and road types behind
obstructions on the roads.
 AI is heavily used by Uber and many logistics companies to improve
operational efficiency, analyze road traffic, and optimize routes.
AI in Navigation

24.  Robotics is another field where artificial intelligence applications are
commonly used. Robots powered by AI use real-time updates to sense
obstacles in its path and pre-plan its journey instantly.
 It can be used for –
 Carrying goods in hospitals, factories, and warehouses
 Cleaning offices and large equipment
 Inventory management
AI in Robotics

25.  Another sector where Artificial Intelligence applications have found
prominence is the gaming sector.
 AI can be used to create smart, human-like NPCs to interact with the
players.
 It can also be used to predict human behavior using which game
design and testing can be improved.
 The Alien Isolation games released in 2014 uses AI to stalk the player
throughout the game.
 The game uses two Artificial Intelligence systems - ‘Director AI’ that
frequently knows your location and the ‘Alien AI,’ driven by sensors
and behaviors that continuously hunt the player.
AI in Gaming

26.  Knowledge Representation in AI describes the representation of
knowledge. Basically, it is a study of how the beliefs, intentions, and
judgments of an intelligent agent can be expressed suitably for
automated reasoning.
 Logical representation is a language with some concrete rules which
deals with propositions and has no ambiguity in representation.
Logical representation means drawing a conclusion based on various
conditions.
 Importance of representation: It is also a way which describes how we
can represent knowledge in artificial intelligence. Knowledge
representation is not just storing data into some database, but it also
enables an intelligent machine to learn from that knowledge and
experiences so that it can behave intelligently like a human.
AI Representation

27.  Primarily, we see five types of knowledge in any knowledge
representation block in AI systems. The knowledge types are as
follows:
 Declarative: It is the type of knowledge that deals with facts,
instances, objects, declared as a statement.
 Structural: It deals with the type of knowledge that describes the
relationship between instances and description.
 Procedural: It deals with the procedures and rules required for a
particular system to work efficiently.
 Meta: It is the knowledge consisting of the higher-level data of
other types of knowledge data.
 Heuristic: It represents the data that helps in governing decisions.
Types of Knowledge

31.  As search engines are made up of codes, natural language processing
technology helps these applications to understand humans. In fact,
they are also able to predict what a human wants to ask by compiling
top-ranked searches and predicting their queries when they start to
type.
 New features such as voice search and image search are also
constantly being programmed into machines. If you want to find a
song that is playing at a mall, you can simply hold your phone up to it,
and a music-identifying app will tell you what it is within seconds.
Searching Algorithms

33.  Based on the search problems we can classify the search algorithm
into:
 Uninformed search (Blind search)
 informed search (Heuristic search)
Uninformed / Blind Search Informed search
• Breadth first search
• Uniform cost search
• Depth first search
• Depth limited search
• Iterative Deeping
depth first search
• Best first search
• A* search
• AO* algorithm
• Problem reduction
• Hill climbing
Types of Search Algorithm
Search Algorithm

34.  A problem determines the graph and the goal but not which path to
select from the frontier.
 A search strategy specifies which paths are selected from the frontier.
 Different strategies are obtained by modifying how the selection of
paths in the frontier is implemented.
 Breadth first
 Depth first
 Iterative deepening
Uninformed Search Strategies

35.  Breadth-first search completes one level before moving on to next
level
 Has to keep in memory all the competing paths that aspire to be
extended to a goal node
 A possible representation of candidate paths: list of lists
 Easiest to store paths in reverse order; then, to extend a path, add a
node as new head (easier than adding a node at end of list)
Breadth first Search algorithm

36. Breadth first Search algorithm

37. Breadth first Search algorithm

38. Breadth first Search algorithm

39. Breadth first Search algorithm

40.  Is not guaranteed to find shortest solution first
 Susceptible to infinite loops (should check for cycles)
 Has low space complexity: only proportional to depth of search
 Only requires memory to store the current path from start to the current
node
 When moving to alternative path, previously searched paths can be
forgotten
Properties of Breadth first Search
algorithm

41. Depth first Search algorithm

42. Depth first Search algorithm

43. Depth first Search algorithm

44. Depth first Search algorithm

45. Depth first Search algorithm

46. Depth first Search algorithm

47. Depth first Search algorithm

48. Depth first Search algorithm

49.  Iterative deepening combines the benefits of depth-first and breadth-
first search.
 In general, iterative deepening is the preferred uninformed search
method when the search space is large and the depth of the solution is
not known.
 The iterative deepening search algorithm, which repeatedly applies
depth- limited search with increasing limits. It terminates when a
solution is found or if the depth- limited search returns failure, meaning
that no solution exists.
Iterative Deepening

51.  For simpler analysis consider state-space as a tree
 Uniform branching b
 Solution at depth d
n
Number of nodes at level n : 𝑏𝑛
Complexity of basic seach methods

52.  Completeness: is the algorithm guaranteed to find a solution when
there is one?
 Optimality: Does the strategy find the optimal solution?
 Time: How long does it take to find a solution?
 Space: How much memory is needed to perform the search?
Complexity Analysis

53. Shortest
solution
Time Space guaranteed
Breadth-first b
d
b
d
yes
Depth-first b
dmax
dmax no
Iterative deepening b
d
d yes
Time and Space Complexity orders

54.  Time: number of nodes generated
 Space: maximum number of nodes stored in memory
 Branching factor: b
Maximum number of successors of any node
 Depth: d
Depth of shallowest goal node
 Path length: m
Maximum length of any path in the state space
Cost Variables

56. Uninformed /Blind search(Brute force
way):
• The uninformed search does not contain any domain
knowledge such as closeness ,the location of the goal.
• It operates in a brute force way, as it only includes information
about how to traverse the tree and how to identify leaf and
goal nodes .
• Uninformed search applies a way in which search tree is
searched without any information about the search space like
initial state operatores & test for goal ,so it is called blind
search it examines each node until it reach the goal node.

57. Informed search :
• It uses domain knowledge the problem informed is available which
can guide the search
• Informed search strategies can find a solution more efficient than
an uninformed informed search is also called as Heuristic search
• A heuristic is a way which might not always be guarantees for the
best soln but guaranteed to find a good soln in reasonable time .
• It cab solve much complex problem which could not be solved in
another way.

58. Heuristic Search Techniques and Function
Heuristic Search (Informed):
The purpose of heuristic function is to guide the search process in the
most profitable path among all that are available .
Heuristic search is defined as a procedure of search that endeavors to upgrade an issue by iteratively
improving the arrangement dependent on a given heuristic capacity or a cost measure.
*This technique doesn’t generally ensure to locate an ideal or the best arrangement, however, it may
rather locate a decent or worthy arrangement inside a sensible measure of time and memory space.
*This is a sort of an alternate route as we regularly exchange one of optimality, culmination, exactness,
or accuracy for speed.
*It may be a function which is employed in Informed Search, and it finds the foremost promising path. It
takes the present state of the agent as its input and produces the estimation of how close agent is from
the goal.

61. BEST FIRST SEARCH(informed search):
Best first search Algorithm(Greedy Search)
It always selects the path which appears best at that
moment.
It is combination of DFS & BFS
It uses the heuristitc function h(n)<=h*(n) and search
h(n)=heuristic cost
h*(n)=estimated cost
The greedy best first alg is implemented by priority
queue

62. Best Search Algorithm:
Step1: Place the starting node into the open list .
Step2: If the open list is empty ,stop and return failure.
Step3: Remove the node n, from the open list which has lowest value of h(n)
and place it in the closed list
Step4: Expand the node n, and generate the successors of node n.
Step5:Check each successor of node n and find whether any node is a goal
node , then return success and terminate the search .
Step6: for each successor node ,algorithm checks for evaluation function F(n) &
then check if the node has been in either OPEN or CLOSED list .if the node has
not been in both list , then add it to OPEN list .
Step5: Return to step 2.

63. Best first search algorithm example :

68. Advantages and Disadvantages of Best
First Search:
Advantage:
• Best first search can switch between BFS & DFS by gaining the
advantages of both the algorithm.
• This algorithm is more efficient than BFS & DFS algorithm
Disadvantage:
• It can behave as an unguided depth first search in the worst
case .
• It can get stuck in a loop as DFS.
• This algorithm is not optimal.

69. Means-Ends Analysis
• Means –Ends Analysis is problem –solving techniques
used in Artificial intelligence for limiting search in AI
programs
• It is a mixture of Backward and Forward search
technique.
• The MEA analysis process centred on the evaluation of
the difference between the current state and goal state.

70. How means-ends analysis Works
o The means –end analysis process can be applied recursively for a
problem.
o It is a strategy to control search in problem –solving.
o Following are the main steps which describes the working of MEA
technique.
1. Frist evalute the difference between intial state and final state .
2. Select the various operators which can be applied for each difference
3. Apply the operator at each difference ,which reduces the difference
between the current state and goal state .

71. Operator Sub goaling:
In the MEA process , we detect the differences between the
current state and goal state .
Once this occur ,then we can apply an operator to reduce the
differences
But sometimes it is possible that an operator cannot be applied
to the current state.
So we create the subproblem of the current state , in which
operator can be applied , such type of backward chaining in
which operators are selected , and then sub goals are set up to
establish the preconditions of the operator is called operator
sub goaling

72. Example of Means-Ends Analysis:
Initial State Goal state

73. Evaluating the initial state
*In the first step,we will evalute the initial state and will compare the
initial and goal state to find the differences between both states.
Applying Delete operator:
First we will apply the Delete operator to remove this dot.

74. Applying move operator:
The new state occurs which we will again compare with goal state
after comparing these states there is another difference that is the
square is outside the circle ,so we will apply the move operator.

75. Applying Expand Operator :
We will compare this state with the goal state, there is still one
difference in size of the square so we will apply Expand operator.
finally ,it will generate the goal state.
Goal state

76. A* Search Algorithm:
 A* search algorithm finds the shortest path through the search space using the
heuristic function.
 It uses h(n), & cost to reach the node n from the start state g(n).
 This algorithm expands less search tree and provides optimal result faster.
 It is similar to UCS(uniform cost search)except that it uses g(n)+h(n) instead of g(n).
 In uniform cost search we only use the g(n).
 A* use search heuristic as well as the cost to reach the node hence we combine both
costs as,
F(n)=g(n)+h(n) {fitness numbers}
F(n)=Estimated cost of the cheapest solution
g(n)=cost to reach node n from start state
h(n)=cost to reach from node n to goal node .

77. Algorithm of A* Search:
Step1: place the starting node in OPEN list .
Step2: check if the OPEN list is empty or not, if the lists empty the return failure & stops.
Step3: Select the node from the OPEN list which has the small value of evaluation function
(g+h),if node n is goal node then return success & stop ,otherwise.
Step4: Expand node n& generate all of its successors , & put n in the closed list .
- for each successor ‘n’ check whether ‘n’is already in the OPEN or CLOSED list,
- If not then compute evaluation function for ‘n’ and place in the OPEN list.
Step5: Else if node ‘n’ is already in OPEN & CLOSED , then it should be attached to the back pointer
which reflects the lowest g(n) value .
Step 6: Return to step 2.

78. Advantages :
• It is best algorithm than other search algorithm.
• It is optimal & complete.
• It can solve very complex problems.
Disadvantages:
• It does not always produce shortest path.
• It is not practical for various large – scale problems.

79. A* search Algorithm example:

82. AO* Search Algorithm:
AO* Algorithm basically based on problem decomposition (Breakdown problem into small
pieces)
When a problem can be divided into a set of sub problems, where each sub problem can be
solved separately and a combination of these will be a solution, AND-OR graphs or AND -
OR trees are used for representing the solution.

86. Scope of AI
*Theorem Proving
*Game playing
*Natural language process
• Speech processing
• Computer vision
• Robotics
• Expert system

87. Game Playing:
Game playing is a search problem defined by, Initial States,
• Successor function
• Goal test
• Path cost/utility/pay off function.
• AI has continued to improve , with aims set on a player being unable to tell the
difference between computer and human player.
• A game must “Feel” natural
• Obeys law of the game
• Characters aware of the environment
• Path finding (A* algorithm)
• Decision making
• planning

88. The game AI is about the illusion of
human behaviour:
• Smart too a certain extent
• Non –repeating behaviour
• Emotional influences (irrationality, "personality”)
• Body language to communicate emotions
• Being integrated in the environment
• Game AI needs various computer science disciplines
1. Knowledge based systems
2. Machine learning
3. Multi agent systems
4. Computer graphics and animation
5. Data structures

89. Game Tree:
Mini Max Algorithm
Alpha – Beta Pruning

90. Mini Max Algorithm:
 Back tracking algorithm
 Best move strategy used
 Max will try to maximize its utility(best move)
 Min will try to minimize utility(worst move)

91. Example:

92. Time complexity:

93.  The above category of games can be represented as a tree where the
nodes represent the current state of the game and the arcs represent the
moves.
For Example : Game of Tic-Tac-Toe

96. Alpha-Beta Pruning
 ALPHA-BETA pruning is a method that reduces the number of nodes
explored in Minimax strategy.
 Cut off search by exploring less no of nodes.
 It reduces the time required for the search and it must be restricted so
that no time is to be wasted searching moves that are obviously bad
for the current player.
 The exact implementation of alpha-beta keeps track of the best move
for each side as it moves throughout the tree.

97. Example:

99.  The efficiency of the Alpha-Beta procedure depends on the order in which
successors of a node are examined.
 If we were lucky, at a MIN node we would always consider the nodes in
order from low to high score and at a MAX node the nodes in order from
high to low score.
 In general it can be shown that in the most favorable circumstances the
alpha-beta search opens as many leaves as minimax on a game tree with
double its depth.

100.  The algorithm maintains two values, alpha and beta, which represent the
minimum score that the maximizing player is assured of and the
maximum score that the minimizing player is assured of respectively.
Initially alpha is negative infinity and beta is positive infinity. As the
recursion progresses the "window" becomes smaller.
 When beta becomes less than alpha, it means that the current position
cannot be the result of best play by both players and hence need not be
explored further.

102. Quiescence Search
 Quiescence search is an algorithm typically used to
evaluate minimax game trees in game-playing computer programs.
It is a remedy for the horizon problem faced by AI engines for various
games like chess and Go
The Horizon Effect is caused by the depth limitation of the search algorithm,
and became manifest when some negative event is inevitable but
postponable. Because only a partial game tree has been analyzed, it will
appear to the system that the event can be avoided when in fact this is not
the case

103.  This problem occurs because computers only search a certain number
of moves ahead.
 Human players usually have enough intuition to decide whether to
abandon a bad-looking move, or search a promising move to a great
depth.
 A quiescence search attempts to emulate this behavior by instructing a
computer to search "interesting" positions to a greater depth than
"quiet" ones (hence its name) to make sure there are no hidden traps
and, usually equivalently, to get a better estimate of its value.

104.  As the main motive of quiescence search is usually to get a good value
out of a poor evaluation function, it may also make sense to detect wide
fluctuations in values returned by a simple heuristic evaluator over
several ply.

105. Secondary Search
 One proposed solution is to examine the search space beyond the
apparently best move to see if something is looming just over the
horizon. In that case we can revert to the second-best move.
Obviously then the second-best move has the same problem, and
there isn't time to search beyond all possible acceptable moves.

106. Constraint Satisfaction Problem(CSP):
 CSP consists of three components V,D,C.
 V is set of variables {V1,V2,----Vn}.
 D is set of domains {D1,D2,….Dn}.
 C is set of constraints that specify allowable combination of values .
 Ci=(Scope,Relation)
 Where scope is set of variables that participate in constraint
 Where Relation is relation that define the values that variable can take
 C1=((V1,V2),(V1≠V2)).