MUST SEE

1. Q1)Genetic Algorithms:-
is a search method in which multiple search paths are followed in parallel. At each step, current states of different
pairs of these paths are combined to form new paths. This way the search paths don't remain independent,
instead they share information and thus try to improve the overall performance of the complete search space
The Basic Genetic Algorithm:-
Start with a population of randomly generated attempted solutions to a problem
Repeatedly do the following:
• Evaluate each of the attempted solutions
• Keep the “best” solutions
• Produce next generation from these solutions (using “inheritance” and mutation)
Quit when you have a satisfactory solution (or you run out of time)
Simple example:
Suppose your “individuals” are 32-bit computer words
You want a string in which all the bits are ones
Here’s how you can do it:
• Create 100 randomly generated computer words
• Repeatedly do the following:
Count the 1 bits in each word
Exit if any of the words have all 32 bits set to 1
Keep the ten words that have the most 1s (discard the rest)
From each word, generate 9 new words as follows:
•Pick a random bit in the word and toggle (change) it
Note that this procedure does not guarantee that the next “generation” will have more 1 bits, but it’s likely
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2. Q2)Eight Queens Problem:-The problem is to place 8 queens on a chess board so that none
of them can attack the other. A chess board can be considered a plain board with eight columns and eight
rows.
• Now we need a fitness function, a function by which we can tell which board position is nearer to our
goal. Since we are going to select best individuals at every step, we need to define a method to rate
these board positions.
• One fitness function can be to count the number of Queens that do not attack others
• Choose initial population of board configurations
• Evaluate the fitness of each individual (configuration)
• Choose the best individuals from the population for crossover
Parent childern
8 5 7 2 /7 1 6 5
4 5 8 2 /7 1 6 5 4 5 8 2 7 1 3 5
8 5 7 2 /7 1 3 5
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3. • Solution!
Q
Q
Q
Q
Q
Q
Q
Q
4 6 8 2 7 1 3 5
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This process is repeated until an individual with required fitness level is found. If
no such individual is found, then the process is repeated further until the overall
fitness of the population or any of its individuals gets very close to the required
fitness level. An upper limit on the number of iterations is usually put to end the
process in finite time.
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4. Q3)What is Knowledge:-
• Understanding of a subject area” Durkin
• Domain: A well-focused subject area
• Types of Knowledge
• Procedural knowledge: Describes how to do things, provides a set of directions of how to perform certain tasks,
e.g., how to drive a car
• Declarative knowledge: It describes objects, rather than processes. What is known about a situation. e.g. it is
sunny today, cherries are red
• Meta knowledge: Knowledge about knowledge, e.g., the knowledge that blood pressure is more important for
diagnosing a medical condition than eye color.
• Heuristic knowledge: Rule-of-thumb. e.g. if I start seeing shops, I am close to the market.
– Sometimes called shallow knowledge.
– Empirical as opposed to deterministic
• Structural knowledge: Describes structures and their relationships. e.g. the how the various parts of the car fit
together to make a car, or knowledge structures in terms of concepts, sub concepts, and objects.
• Facts:-Facts are a basic block of knowledge (the atomic units of knowledge)
• They represent declarative knowledge.
• A Proposition is the statement of a fact. Each proposition has an associated truth value. It may be true
or false.
• In AI, to represent a fact, we use a proposition and its associated truth value
• e.g. Proposition A: It is raining
– Proposition B: I have an umbrella
– Proposition C: I will go to school
• Types of facts:-
• single-valued or multiple –valued: e.g. an individual can only have one eye color, but may have many cars.
So the value of attribute cars may contain more than one value.
• uncertain facts: e.g. it will probably be sunny today. We may chose to store numerical certainty values with such
facts.
• fuzzy facts, which are ambiguous in nature. e.g. The book is heavy/light. Fuzzy representation used.
Use certainty factor values to specify value of “truth”.
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5. Q4)Rules:-A Knowledge structure that relates some known information to other information that
can be concluded or inferred to be true.” (Durkin)
• Represent procedural knowledge .Components of a rule
– Antecedent or premise: IF part
– Consequent or conclusion: THEN part
• e.g. IF it is raining THEN I will not go to school
– Premise: It is raining
– Conclusion: I will not go to school.
• Compound Rules:-Multiple premises may be joined using AND (conjunctions) and OR (disjunctions)
• e.g. IF it is raining AND I have an umbrella THEN I will go to school.
• IF it is raining OR it is snowing THEN I will not go to school
• TYPES OF RULES:-
1)Relationship e.g. IF you hear a loud sound THEN the silencer is not working
2)Recommendation :-IF it is raining THEN bring an umbrella
3]Directive:-IF it is raining AND you don’t have an umbrella THEN wait for the rain to stop
5]Variable Rules:-If the same type of rule is to be applied to multiple objects, we use variable rules, i.e. rules with
variables. e.g. If X is a Student
AND X’s GPA>3.7
THEN place X on honor roll.
Such rules are called pattern-matching rules. ……The rule is matched with known facts and different possibilities for the
variables are tested, to determine the truth of the fact.
6}Uncertain Rules:-e.g. IF you have never won a match THEN you will most probably not win this time.
7]Meta Rules:-Rules that describe how to use other rules e.g. IF You are coughing AND you have chest congestion
THEN use the set of respiratory disease rules.
8}Rule Sets:-As in the previous example, we may group rules into categories in our knowledge representation, e.g. the
set of respiratory disease rules
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6. Q5)Logic:- Logic representation techniques:
1)Propositional Logic
2)Predicate Calculus
• Algebra is a type of formal logic that deals with numbers, e.g. 2+4 = 6
• Similarly, propositional logic and predicate calculus are forms of formal logic for dealing with propositions
1)Propositional Logic:-
• Proposition: Statement of a fact
• Assign a Symbolic Variable to represent a proposition. e.g.
p = It is raining
q = I carry an umbrella
• A declarative sentence may be classified as either True of False.
– the proposition ‘A rectangle has four sides’ is true
– the proposition ‘The world is a cube’ is false.
• A proposition is a sentence whose truth values may be determined. So, each variable has a truth value.
Compound statements
• Different propositions may be logically related.
• We can form compound statements using logical connectives:
 AND (Conjunction) TRUTH TABLE of Binary Logical
Connectives
 OR (Disjunction)
~ NOT (Negation)
 If … then (Conditional)
 If and only if (bi-conditional)
p = It is raining
q = I carry an umbrella
r = It is cloudy
• s = IF it is raining THEN carry an umbrella
p  q
• t = IF it is raining OR it is cloudy, THEN carry an umbrella
(p  r )  q……>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
TTFFFF
FTTFTF
FFTFFT
TTTTTT
p  qp  qp  qp qqp
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7. Q6)Predicate Calculus:-Extension of Propositional logic
• Allows structure of facts/sentences to be defined
With predicate logic, we can say color( ball, blue)
• Provides a mechanism for proving statements
• Has greater representation power as we will see shortly.
• The Universal Quantifier:-Symbol "
• “for every” or “for all”
• Used in formulae to assign the same truth value
• to all variables in the domain
• e.g. Domain: numbers:-("x) ( x + x = 2x)
– In words: for every x (where x is a number), x + x = 2x is true
• e.g. Shapes:-("x) ( x = square  x = polygon)
– In words: every square is a polygon.
– For every x (where x is a shape), if x is a square, then x is a polygon
– (it implies that x is a p polygon).
• Existential Quantifier:- Symbol: $
• Used in formulae to say that something is true for at least one value in the
domain
• “there exists”, “ for some” “for at least one” “there is one”
• e.g.:-($ x) ( person (x)  father (x,ahmed) )
– In words: there exists some person, x who is Ahmed’s father
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8. • First Order Predicate Logic:- First Order Predicate logic is the simplest form.
• Uses symbols. These may be
– Constants: Used to name specific objects or properties. e.g. Ali, Ayesha, blue, ball.
– Predicates: A fact or proposition is divided into two parts
• Predicate: the assertion of the proposition
• Argument: the object of the proposition
• e.g. Ali likes bananas becomes Likes (ali, bananas)
– Variables: Used to represent general class of objects/properties. e.g. likes (X, Y). X and
Y are variables that assume the values X=Ali and Y=bananas
– Formulae: Use predicates and quantifiers
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9. Q7)Semantic Networks:- Graphs, with nodes representing objects and arcs representing relationships between
objects
• Various types of relationships may be defined.
– IS-A (Inheritance relationship)
– HAS (Ownership)
Semantic Network example:-
Network Operation: How to infer new information from semantic networks. We can ask nodes questions
– Ask node vehicle: ‘How do you travel’
• Looks at arc and replies: road
– Ask node Car X: ‘How do you travel’
• Asks node Car (because of IS-A relationship)
• Asks node Vehicle (IS-A relationship)
• Node Vehicle Replies: road
• Problems with Semantic Networks:- Computationally expensive at run-time. In the worst case, we may
need to traverse entire network and then discover that the requested info does not exist.
• They try to model human associative memory (store info using associations), but in the human brain the number of
neurons and links are in the order of 1015. Such numbers are computationally prohibitive in semantic networks.
• Are logically inadequate. Have no analogues to quantifiers (for all, for some, none).
Vehicle
CarSuzuki Truck
Bedford
Road
IS-A
IS-A
IS-A
IS-A
Travels by
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10. Q8)Reasoning:-Deriving logical conclusions from given facts.
• ‘The process of working with knowledge, facts and problem solving strategies to draw
conclusions’ Durkin
1)Deductive Reasoning:-Deduce new information from logically related known
information
• A deductive argument offers assertions that lead automatically to a conclusion.
– If there is dry wood, oxygen and a spark, there will be a fire
Given: There is dry wood, oxygen and a spark
We can deduce: There will be a fire.
– All men are mortal. Socrates is a man.
We can deduce: Socrates is mortal
• Modus Ponens is the basic form of deductive reasoning
Modus Ponens: If A is true and if A implies B is true, then B is true.
2)Inductive Reasoning:-From a limited set of observations, we form a
‘generalization’.
Example:-Observation: All the crows that I have seen in my life are black. Conclusion:
All crows are black
3)Adductive Reasoning:-Deduction is exact in that the deductions follow in a logically
provable way from the axioms.
• Abduction is a form of deduction that allows for plausible inference, i.e. the conclusion
might be wrong, e.g.
Implication: She carries an umbrella if it is raining
Axiom: she is carrying an umbrella Conclusion: It is raining
• This conclusion might be false, because there could be other reasons that she is carrying an
umbrella, e.g. she might be carrying it to protect herself from the sun. 10

11. 4)Analogical Reasoning:-Draw analogy between two situations, looking for
similarities and differences.
• e.g. when you say driving a truck is just like driving a car
– By analogy you know that there are some similarities, same basic concepts
– But you also know that there are certain other distinguishing characteristics
of each
5)Common-sense Reasoning:-Gained through experience, rule-of-
thumb...Operates on heuristic knowledge and heuristic rules.
6)Non-Monotonic Reasoning:- Used when the facts of the case are not
static, e.g. Rule:(1)IF the wind blows(2)THEN the curtains sway
• When the wind stops blowing, the curtains should sway no longer.
• However, if we use monotonic reasoning, this would not happen. The fact
that the curtains are swaying would be retained even after the wind
stopped blowing
– In non-monotonic reasoning, we have a ‘truth maintenance system’. It keeps
track of what caused a fact to become true. If the cause is removed, that fact
is removed (retracted) also.
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12. Q9)Knowledge representation and reasoning (KR) is the field of artificial
intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to
solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.
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18. Q12)Inference:-Inference is the process of deriving new information from known information
• In the domain of AI, the component of the system that performs inference is called an inference engine.
• We will look at inference within the framework of ‘logic’, which we introduced earlier
Rules of Inference:-Modus Ponens", which means "affirming method“
• From now onwards, anything that is written down in a proof, is a statement that is true.
• If you know that alpha implies beta, and you know alpha to be true, you can automatically say that beta is true.
• Modus Tolens: "alpha implies beta" and "not beta" you can conclude "not alpha".
• If Alpha implies beta is true and beta is known to be not true, then alpha could not have been true. Had alpha been
true, beta would automatically have been true due to the implication.
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19. Q13)Resolution:-The deduction mechanism we discussed may be used in practical systems, but is not feasible. It
uses a lot of inference rules, which introduces a large branch factor in the search for a proof.
• An alternative is Resolution, a strategy used to assert the determine the truth of an assertion.
• Only one Resolution rule:
• Resolution by Refutation:-Proof strategy called Resolution Refutation
– Write all sentences in CNF
– Negate the desired conclusion
– Apply the resolution rule until you derive a contradiction or cannot apply the rule anymore.
• If we derive a contradiction,
then the conclusion follows from the given axioms
• If we cannot apply anymore, then the conclusion
cannot be proved from the given axioms.
        
Prove
R
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20. Q14)History and Evolution:-Expert Systems at the forefront of AI rebirth.
• Realization in the late 60 that the general framework of problem solving was not enough to solve all
kinds of problem. GPS separates knowledge from control
• Realization that specialized knowledge is a very important component of practical systems.
• People observed that systems that were designed for well-focused problems and domains, out
performed more ‘general’ systems.
• Importance of expert systems as the earliest AI systems and the most used systems practically.
• One of the pioneering systems: DENDRAL (1960’s)
• developed at Stanford for NASA
• Chemical analysis of Martian soil for space mission
• Given mass spectral data, determine molecular structure.
• In the laboratory, generate and test method used: various possible hypothesis (molecular structures)
are generated and tested (matched to data)
• Early realization that experts use certain heuristics to rule out certain options. Encode that knowledge
in the system
• Moral: ‘Intelligent behavior is dependent, not so much on the methods of reasoning, but on the
knowledge one has to reason with’ Durkin
• MYCIN( mid 70s)
• Developed at Stanford to aid physicians in diagnosing and treating patients with a particular blood
disease.
• Motivation for MYCIN
– Demonstrated that expert systems could be used for solving practical problems
– Pioneering work on the structure of ES (separate knowledge and control), as opposed to
Denderal, Mycin used the same structure that is now formalized for ESs.
• R1/XCON (late 70s) one of the most cited ES.
• Developed by DEC( Digital Equipment Corporation)
• Computer configuration assistant
• One of the most successful expert systems in routine use.
• Estimated saving is $25million per year
• Example of how ES can increase productivity of organization, by assisting existing experts
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21. Q15)What is an Expert?
What characterizes an ‘Expert’
– Specialized knowledge in a certain area
– Exerience in the given area
– Explanation of decisions
– A skill set that enables the expert to translate the specilized knowledge gained through
experience into solutions.
– e.g. Skin specialist, heart specialist, car mechanic, architect, software designer.
What is an expert system?
“A computer program designed to model the problem solving ability of a human expert” Durkin
• Aspects of the human expert that we wish to model
– Knowledge ----Reasoning
Comparison of a Human Expert and an Expert System:-
• Example: medical ES modelling a doctor, discuss each of the following issues in that context.
• The ES outperforms the ‘average’ doctor and is available in regions where people may not have access to any medical
care at all otherwise.
Comparison
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22. Q16)How are Expert Systems Used?1)Diagnosis (2)
Interpretation(3)Prescription(4)Design(5)Planning(6)Control(7) Instruction(8)
Prediction(9)Simulation
Diagnosis and Prescription:-Identify system malfunction points.
• Have knowledge of possible faults as well as diagnosis methodology extracted from
technical experts
• E.g. diagnosing patients syptoms.
• Malfunctioning electronic structures
• Most diagnosis ES have a prescription subsystem.
• Such system are usually interactive, building on user information to narrow down
diagnosis.
Design:-Configure objects under given constraints.e.g. XCON
• Often use non-monotonic reasoning, because of implications of steps on previous steps.
• Eg. PEACE (Dincbas 1980) CAD tool to assist in design of electronic structures.
Instruction and Simulation:-Guide the instruction of a student in some topic. Tutoring
applications
• Example: GUIDON( Clancey 1979)
– Instructs students in diagnosis of bacterial infections
– Strategy: presents user with case (of which it has solution), analyzes the students
response. It compares the students approach to its own and directs student based on
differences.
CONTINUSE……. 22

23. EXPERT SYSTEM USED
Simulation
– Model a process or system for operational study.
– May be used along with tutoring applications
Interpretation:- Produces an understanding of situation from given information’
Durkin
• Example FXAA (1988) provides financial assistance for a commercial bank. Looks
at thousands of transactions and identifies irregularities. Automated audit.
Planning and Prediction:-E.g. recommending steps for a robot to carry out
certain steps
• Cash management planning
• SMART Plan is a strategic market planning expert (Beeral, 1993). Suggests
appropriate marketing mix required to achieve economic success.
• Predictions systems infer likely consequences from a given situation.
– Reason with time ordered information
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24. Q17)Alpha Beta Pruning
• In Minimax Procedure, it seems as if the static evaluator must be used on each leaf node. Fortunately
there is a procedure that reduces both the tree branches that must be generated and the number of
evaluations. This procedure is called Alpha Beta pruning which “prunes” the tree branches thus
reducing the number of static evaluations. We use the following example to explain the notion of
Alpha Beta Pruning. Suppose we start of with a game tree in the diagram below. Notice that all
nodes/situations have not yet been previously evaluated for their static evaluation score. Only two
leaf nodes have been evaluated so far
Q18)What is Learning? Learning denotes changes in the system that are adaptive in the
sense that they enable the system to do the same task or tasks drawn from the same population more effectively the
next time (Simon, 1983).A computer program learns if it improves its performance at some task through experience
(Mitchell, 1997).
)WHAT IS MACHINE LEARNING? Machine learning is an application of artificial intelligence (AI) that provides
systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine
learning focuses on the development of computer programs that can access data and use it learn for themselves
Q19)weak AI? The principle behind Weak AI is simply the fact that machines can be made to act as if
they are intelligent. For example, when a human player plays chess against a computer, the human player
may feel as if the computer is actually making impressive moves. But the chess application is not thinking
and planning at all. All the moves it makes are previously fed in to the computer by a human,
Strong AI?The principle behind Strong AI is that the machines could be made to think or in other
words could represent human minds in the future. If that is the case, those machines will have the ability to
reason, think and do all functions that a human is capable of doing. But according to most people, this
technology will never be developed or at least it will take a very long time. However, Strong AI.
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