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ASSIGNMENT 3

CSE 452 ARTIFICIALINTELLIGENCE

Submitted to:- Submitted by:-

Miss. Ruchi Uday Pratap

10804711

A1810A11

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Q1:- Write a pseudo code for BSF.

Solution 1:.

# Pseudo code for breadth first search:

i. Declare two empty lists: Open and Closed.ii. Add Start node to our Open list.

iii. While our Open list is not empty, loop the following:

a. Remove the first node from our Open List.b. Check to see if the removed node is our destination.

i. If the removed node is our destination, break out of the loop,

add the node to our Closed list, and return the value of our Closed list.

ii. If the removed node is not our destination, continue the loop (goto Step c).

c. Extract the neighbors of our above removed node.

Add the neighbors to the end of our Open list, and add the removed node to our Closed list

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earchMethod = function () {

var origin:Node = nodeOrigin;

var destination:Node = nodeDestination;

// Creating our Open and Closed Lists

var closedList:Array = new Array();

var openList:Array = new Array();

// Adding our starting point to Open List

openList.push(origin);

// Loop while openList contains some data.

while (openList.length != 0) {

// Loop while openList contains some data.

var n:Node = Node(openList.shift());

// Check if node is Destinationif (n == destination) {

closedList.push(destination);

trace("Closed!");

break;

}

// Store n's neighbors in array

var neighbors:Array = n.getNeighbors();

var nLength:Number = neighbors.length;

// Add each neighbor to the end of our openList

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for (i=0; i

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conjunctions (occasionally, disjunctions) of predicates. In some systems, someconditions correspond to sensor data. In a cognitive model, this would correspond todirect access to sensory input, rather than to its representation in STM.

The action can be one of the following:

Another symbol or set of symbols to be added to STM. In many systems these will beexpressed logically as conjunctions of predicates.

Some other action on STM, e.g. ``delete the symbols XyZ''.

Some other action, e.g. turning a motor off, printing. In a cognitive model, this would

correspond to a motor command.

The inference engine applies the rules to working memory. There are various ways of doing this: see later. Why so called? See Crevier p 157.

The user interface sits between the inference engine and the user. It translates thesystem's answers from an internal representation to something the user can understand;it passes questions from the system to the user and checks the replies (rejecting, for example, a negative number as the answer to a request for your weight). Introductionto Rule-Based Systems

Using a set of assertions, which collectively form the working memory, and a set of rules that specify how to act on the assertion set, a rule-based system can be created.Rule-based systems are fairly simplistic, consisting of little more than a set of if-thenstatements, but provide the basis for so-called expert systems which are widely usedin many fields. The concept of an expert system is this: the knowledge of an expert isencoded into the rule set. When exposed to the same data, the expert system AI willperform in a similar manner to the expert.

Rule-based systems are a relatively simple model that can be adapted to any number of problems. As with any AI, a rule-based system has its strengths as well as limitationsthat must be considered before deciding if its the right technique to use for a givenproblem. Overall, rule-based systems are really only feasible for problems for whichany and all knowledge in the problem area can be written in the form of if-then rulesand for which this problem area is not large. If there are too many rules, the systemcan become difficult to maintain and can suffer a performance hit.

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First Applicable: If the rules are in a specified order, firing the first applicable oneallows control over the order in which rules fire. This is the simplest strategy and has apotential for a large problem: that of an infinite loop on the same rule. If the workingmemory remains the same, as does the rule-base, then the conditions of the first rule

have not changed and it will fire again and again. To solve this, it is a commonpractice to suspend a fired rule and prevent it from re-firing until the data in workingmemory, that satisfied the rules conditions, has changed.

Random: Though it doesnt provide the predictability or control of the first-applicablestrategy, it does have its advantages. For one thing, its unpredictability is an advantagein some circumstances (such as games for example). A random strategy simplychooses a single random rule to fire from the conflict set. Another possibility for arandom strategy is a fuzzy rule-based system in which each of the rules has aprobability such that some rules are more likely to fire than others.

Most Specific: This strategy is based on the number of conditions of the rules. Fromthe conflict set, the rule with the most conditions is chosen. This is based on theassumption that if it has the most conditions then it has the most relevance to theexisting data.

Least Recently Used: Each of the rules is accompanied by a time or step stamp, whichmarks the last time it was used. This maximizes the number of individual rules that arefired at least once. If all rules are needed for the solution of a given problem, this is aperfect strategy.

"Best" rule: For this to work, each rule is given a weight, which specifies how muchit should be considered over the alternatives. The rule with the most preferableoutcomes is chosen based on this weight.

Part B

Q4:- Explain Bayesian Network with example.

Solution 4:-

Bayesian Network.

A Bayesian network, belief network or directed acyclic graphical model is aprobabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesiannetwork could represent the probabilistic relationships between diseases andsymptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

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joint distribution of X to be the product of these conditional

distributions, then X is a Bayesian network with respect to G

Example:-

Burglar Alarm Example

Im at work. Is there is a burglary at home?

Neighbour John calls to say my alarm is ringing.

Neighbour Mary does not call.

Sometimes alarm is set off by minor earthquakes.

Boolean variables: Burglary, E arthquake, A larm,

JohnCalls, MaryCallsConstruct network to reect causal knowledge

A burglar may set the alarm off

An earthquake may set the alarm off

The alarm may cause Mary to call

The alarm may cause John to call

Burglar Alarm Example

Less space: Max. k parents O(nd

k

) numbers vs. O(d

n

(

Faster to answer queries

Simpler to nd CPTs

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Q5:- How Dempster Shafer Theory can be used to solve AI

problems?

Solution 5:-

dampster shafer theory:-

The DempsterShafer theory (DST) is a mathematical theory of

evidence[1]. It allows one to combine evidence from different sources

and arrive at a degree of belief (represented by a belief function) that

takes into account all the available evidence. The theory was first

developed by Arthur P. Dempster[2] and Glenn Shafer.

In a narrow sense, the term DempsterShafer theory refers to theoriginal conception of the theory by Dempster and Shafer. However, it

is more common to use the term in the wider sense of the same general

approach, as adapted to specific kinds of situations. In particular, many

authors have proposed different rules for combining evidence, often

with a view to handling conflicts in evidence better

.

Shafer's framework allows for belief about propositions to be represented as intervals,

bounded by two values, belief (or support ) and plausibility :

belief plausibility .

Belief in a hypothesis is constituted by the sum of the masses of all sets enclosed

by it (i.e. the sum of the masses of all subsets of the hypothesis). It is the amount

of belief that directly supports a given hypothesis at least in part, forming a lower

bound. Plausibility is 1 minus the sum of the masses of all sets whose intersection

with the hypothesis is empty. It is an upper bound on the possibility that the

hypothesis could be true, i.e. it could possibly be the true state of the system up

to that value, because there is only so much evidence that contradicts that

hypothesis.

For example, suppose we have a belief of 0.5 and a plausibility of 0.8 for a

proposition, say the cat in the box is dead.meaning that the cat could either be

dead or alive. This interval represents the level of uncertainty based on the

evidence in your system.

Hypothesis Mass Belief Plausibility

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Null (neither alive nor dead) 0 0 0

Alive 0.2 0.2 0.5

Dead 0.5 0.5 0.8

Either (alive or dead) 0.3 1.0 1.0

The null hypothesis is set to zero by definition (it corresponds to no solution).

The orthogonal hypotheses Alive and Dead have probabilities of 0.2 and 0.5,

respectively. This could correspond to Live/Dead Cat Detector signals, which

have respective reliabilities of 0.2 and 0.5. Finally, the all-encompassing Either

hypothesis (which simply acknowledges there is a cat in the box) picks up the

slack so that the sum of the masses is 1. The belief for the Alive and Dead

hypotheses matches their corresponding masses because they have no subsets;

belief for Either consists of the sum of all three masses (Either, Alive, and

Dead) because Alive and Dead are each subsets of Either. The Alive

plausibility is 1 m (Dead) and the Dead plausibility is 1 m (Alive). Finally,

the Either plausibility sums m(Alive) + m(Dead) + m(Either). The universal

hypothesis (Either) will always have 100% belief and plausibility it acts as a

checksum of sorts.

Here is a somewhat more elaborate example where the behavior of belief and

plausibility begins to emerge. We're looking through a variety of detector systems

at a single faraway signal light, which can only be coloured in one of three

colours (red, yellow, or green):

Hypothesis Mass Belief Plausibility

Null 0 0 0

Red 0.35 0.35 0.56

Yellow 0.25 0.25 0.45

Green 0.15 0.15 0.34

Red or Yellow 0.06 0.66 0.85

Red or Green 0.05 0.55 0.75

Yellow or Green 0.04 0.44 0.65

Any 0.1 1.0 1.0

Events of this kind would not be modeled as disjoint sets in probability space as

they are here in mass assignment space. Rather the event "Red or Yellow" would

be considered as the union of the events "Red" and "Yellow", and (see the axioms

of probability theory) P (Red or Yellow) P (Yellow), and P (Any)=1,

where Any refers to Red or Yellow or Green . In DST the mass assigned

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to Any refers to the proportion of evidence that can't be assigned to any of the

other states, which here means evidence that says there is a light but doesn't say

anything about what color it is. In this example, the proportion of evidence that

shows the light is either Red or Green is given a mass of 0.05. Such evidence

might, for example, be obtained from a R/G color blind person. DST lets us

extract the value of this sensor's evidence. Also, in DST the Null set is considered

to have zero mass, meaning here that the signal light system exists and we are

examining it's possible states, not speculating as to whether it exists at all.

Q6:- Explain ATN in NLP with examples?

Solution 6:-

An augmented transition network (ATN) is a recursive transition network that can

perform tests and take actions during arc transitions.

An ATN uses a set of registers to store information.

A set of actions is defined for each arc, and the actions can look at and modify theregisters.

An arc may have a test associated with it. The arc is traversed (and its action is taken)only if the test succeeds.

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When a lexical arc is traversed, it is put in a special variable (*) that keeps track of thecurrent word.

Natural language understanding is a subtopic of natural language processing inartificial intelligence that deals with machine reading comprehension.

The process of disassembling and parsing input is more complex than the reverseprocess of assembling output in natural language generation because of the occurrenceof unknown and unexpected features in the input and the need to determine theappropriate syntactic and semantic schemes to apply to it, factors which are pre-determined when outputting language.

An ATN is simply an RTN that has been equipped with a memory and the ability toaugment arcs with actions and conditions that make reference to that memory (seeChapter 3 for a detailed discussion of RTNs and ATNs). ATN-based parsers wereprobably the most common kind of parser employed by computational linguists in the1970s, but they have begun to fall out of favour in recent years. This is largely becausethe augmentations destroy the declarative nature of the formalism and because a parser using an ATN is limited in the search strategies it can employ (see Chapter 5 for acomprehensive account of the relation between parsing and search).

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A much larger range of search strategies become practical once a data structure knownas a chart is adopted for parsing, and chart parsers have now become one of the basictools of modern NLP. A chart is basically a data structure in which the parser recordsits successful attempts to parse subconstituents of the string of words.

Once the parser has recorded the presence of a constituent in one part of the string, itnever needs to look for the same kind of constituent there again. This represents asignificant improvement on the backtracking algorithms used in most ATN systems.The ability of the chart to record, in addition, the current goals of the parser leads tothe possibility of implementing very sophisticated algorithms (see Chapter 6 for adetailed discussion of chart-based parsers).

Prolog is an inherently declarative language and so it is not surprising that one of thefirst of the new breed of declarative grammar formalisms emerged from that language.Definite Clause Grammars (DCG's) were developed from ideas of Colmerauer andhave been quite widely used within the Prolog community. A DCG is essentially aphrase structure grammar (see Chapter 4) annotated with Prolog variables which mapsstraightforwardly into ordinary Prolog code. This total compatibility with Prolog is themajor attraction of DCG's. Even though they look like grammars, and are in fact

grammars, they can be used as parsers directly, given the way that Prolog works.REMOVED IN LIGHT OF REVIEWER COMMENT: However, using them in thisway can prove inefficient since Prolog does not, by itself, employ any analogue of thewell-formed substring table or chart discussed in the preceding section. The DCGformalism is provably powerful enough to describe languages (both natural andartificial) of arbitrary complexity. It is not, however, especially well-adapted for providing elegant accounts of some of the complexities that show up in naturallanguages (e.g., the unbounded dependency type of construction discussed in Chapter 4), although this has been ameliorated in some subsequent extensions of the

formalism.Ambiguity is arguably the single most important problem in NLP (see Chapter 5).Natural languages are riddled with ambiguities at every level of description, from thephonetic