soft computing unit 1 by varun

7/28/2019 Soft Computing Unit 1 by Varun

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By varun galar

Soft Computing

Unit - 1

Jan 2013


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Table of contents

1 1. Soft Computing .......................................................................................... 4

1.1 Introduction of Soft Computing ................................................................................................... 4

1.2 Soft Computing Vs Hard Computing ........................................................................................... 5

1.3 Various types of Soft Computing ................................................................................................ 6

1.4 Soft Computing Applications ...................................................................................................... 7

2. Artificial Intelligence ................................................................................................ 8

2.1 Introduction:- .................................................................................................................................... 8

2.2 Production System ............................................................................................................................ 9

2.3 Search Techniques:- ....................................................................................................................... 11

2.3.1 Depth First Search ....................................................................................................................... 12

2.3.2 Breadth First Search .................................................................................................................... 14

2.3.3 Hill Climbing Search .................................................................................................................... 16

2.3.4 Best First Search ......................................................................................................................... 17

2.3.5 A* Search Technique ................................................................................................................... 19

2.3.6 AO* Search................................................................................................................................. 21

2.3.7 Control Strategies ........................................................................................................................ 22

2.4 Knowledge Representation Issues .................................................................................................. 23

2.5 Propositional and predicate logic..................................................................................................... 24

2.6 Forward and Backward Reasoning ................................................................................................. 25

2.7 Natural Language Processing(NLP) ................................................................................................ 27


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Table of exhibits

Exhibit 1: Soft computing VS Hard Computing..5

Exhibit 2: Types Of Production System9

Exhibit 3: Forward vs Backward Reasoning..26


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1 1. Soft Computing

1.1 Introduction of Soft Computing

Soft computing is a term applied to a field of computer science with the aim of exploiting the

robustness,good report with reality,low cost solution,uncertainty and partial truth to obtain tractability and

tolerance of imprecision. It is the fusion of methodologies that were designed to model and enable solutions

to real world problems, which are not modeled, or too difficult to model, mathematically. The principal

constituents of soft computing are fuzzy logic, neurocomputing, and probabilistic reasoning, with the latter

subsuming genetic algorithms, belief networks, chaotic systems, and parts of learning theory. fuzzy logic is

mainly concerned with imprecision and approximate reasoning; neurocomputing with learning and curve-

fitting; and probabilistic reasoning with uncertainty and belief propagation".Its goal is to attempt to match the

human brain as near as possible.Soft computing would cover all the advangates of different soft computing

componenets.

Figure 1 Soft Computing

Soft Computing is a new multidisciplinary field that was proposed by Dr. Lotfi Zadeh, whose goal was to

construct new generation Artificial Intelligence, known as Computational Intelligence. The idea of SoftComputing was initiated in 1981 when Dr. Zadeh published his first paper on soft data analysis. Since then,

the concept of Soft Computing has evolved. Dr. Zadeh defined Soft Computing in its latest incarnation as the

fusion of the fields of Fuzzy Logic, Neuro-computing, Evolutionary and Genetic Computing, and Probabilistic

Computing into one multidisciplinary system. The main goal of Soft Computing is to develop intelligent

machines and to solve nonlinear and mathematically unmodelled system problemsIn effect, the role model

for soft computing is the human mind.

.


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1.2 Soft Computing Vs Hard Computing

S.no Soft computing Hard computing

1. Soft computing differs from conventional (hard)

computing in that, unlike hard computing, it is

tolerant of imprecision, uncertainty, partial truth,and approximation. In effect, the role model for

soft computing is the human mind.

Hard computing, i.e., conventional computing,

requires a precisely stated analytical model and

often a lot of computation time.

2. Soft computing based on fuzzy logic, neural nets

and probabilistic reasoning.

Hard computing based on binary logic, crisp

systems, numerical analysis and crisp software.

3. Soft computing has the characteristics of

approximation and dispositionality.

Hard computing has the characteristics of

precision and categoricity

4. In soft computing the tolerance for imprecision and

uncertainty is exploited to achieve tractability,

lower cost, high Machine Intelligence Quotient(MIQ) and economy of communication

In hard computing, imprecision and uncertainty

are undesirable properties

5. Soft computing can evolve its own programs Hard computing requires programs to be

written.

6. Soft computing can use multivalued or fuzzy

logic

Hard computing uses two-valued logic.

7. Soft computing incorporates stochasticity Hard computing is deterministic.

8. Soft computing can deal with ambiguous and

noisy data

Hard computing requires exact input data

9. Soft computing allows parallel computations Hard computing is strictly sequential

10. Soft computing can yield approximate answers Hard computing produces precise answers

11. It uses Soft constraints It uses Real-time constraints

12. It need robustness rather than accuracy. It need accuracy and precision in calculations

and outcomes.

13. It is useful for routine tasks that are not critical It is useful in critical systems.

Table 1. Soft computing VS Hard Computing

Figure 2: Soft and Hard Computing


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1.3 Various types of Soft Computing

The various types of Soft Computing techniques are as follows:-

1,Neural Network

2.Fuzzy Logic

3.Genetic Algorithm

1.3.1 Neural Network:-

It is a mathematical model inspired by biological neural networks. A neural network consists of an

interconnected group of artificial neurons, and it processes information using a connectionist approach to

computation. In most cases a neural network is an adaptive system that changes its structure during a learning

phase. Neural networks are used to model complex relationships between inputs and outputs or to find

patterns in data.

The inspiration for neural networks came from examination of central nervous systems. In an artificial neural

network, simple artificial nodes, called "neurons", "neurodes", "processing elements" or "units", are connected

together to form a network which mimics a biological neural network.

1.3.2. Fuzzy Logic:-

It is a form of many-valued logic derived from fuzzy set theory to deal with reasoning that is fluid or

approximate rather than fixed and exact. In contrast with "crisp logic", where binary sets have two-valued logic,

fuzzy logic variables may have a truth value that ranges in degree between 0 and 1.

In simple words we can say fuzzy logic is a super set of conventional (Boolean) logic that has been extended

to handle the concept of partial truth--the truth values between completely true and completely false.

3.Genetic Algorithms:-

A genetic algorithm (or GA) is a search technique used in computing to find true or approximate

solutions to optimization and search problems.

Genetic algorithms are categorized as global search heuristics.

Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by

evolutionary biology such as inheritance, mutation, selection, and crossover (also called

recombination).

Genetic algorithms are so powerful that they can exhibit more efficiency if programmed perfectly.

Applications include learning Robot behavior, molecular structure optimization, automated design of

mechatronic systems, and electronic circuit design.


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1.4 Soft Computing Applications

Heavy industry

o Matsushita, Siemens

o robotic arms, humanoid robots

Home appliances

o Canon, Sony, Goldstar, Siemens

o washing machines, ACs, refrigerators, cameras

Automobiles

o Nissan, Mitsubishi, Daimler-Chrysler, BMW, Volkswagen

o Travel Speed Estimation, Sleep Warning Systems, Driver-less cars

Spacecrafts

o NASA

o Manoeuvering of a Space Shuttle(FL), Optimization of Fuel-efficient Solutions for a

Manoeuvre(GA), Monitoring and Diagnosis of Degradation of Components andSubsystems(FL), Virtual Sensors(ANN)


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2. Artificial Intelligence

2.1 Introduction:-

A.I. can be define as the artificial brain having capability of thinking and understanding.

A.I. is branch of computer science concerned with the study and creation of computer system that

exhibits some form of intelligence

AI is the process to make computers do things which, at the moment, people do better.

AI is the study and design of intelligent agents, where an intelligent agent is a system that perceives

its environment and takes actions that maximize its chances of success.

The definitions of AI gives four possible goals to pursue :

1. Systems that think like humans.

2. Systems that think rationally.

3. Systems that act like humans

4. Systems that act rationally

Various techniques that have evolved, can be applied to a variety of AI tasks.The techniques are

concerned with how we represent, manipulate and reason with knowledge in order to solve problems.


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2.2 Production System

An Artificial Intelligence System developed for solution of any problem is called Production System.Once the

problem is defined, analyzed and represented in a suitable formalism. The Production System is used for

application of rules and obtaining the solution.

A production systemconsists of four basic components:

1. A set of rulesof the form Ci Aiwhere Ciis the condition part and Aiis the action part. These rules are

interpreted as follows: given condition, Ci, take action Ai. The condition determines when a given rule is

applied, and the action determines what happens when it is applied.

2. One or more know ledge databasesthat contain whatever information is relevant for the given problem.

Some parts of the database may be permanent, while others may temporary and only exist during the solution

of the current problem. The information in the databases may be structured in any appropriate manner.

3. A control strategythat determines the order in which the rules are applied to the database, and provides a

way of resolving any conflicts that can arise when several rules match at once.

4. A rule applierwhich is the computational system that implements the control strategy and applies the rules.

Four classes of production systems:-

1. A monotonic production system

2. A non monotonic production system

3. A partially commutative production system

4. A commutative production system.

Table 2 : Types Of Production System

Production systems describe the operations that can be performed in a search for a solution to the

problem.

They can be classified as follows.

Monotonic production system :- A system in which the application of a rule never prevents the later

application of another rule, that could have also been applied at the time the first rule was selected.

Partially commutative production system:-A production system in which the application of a particular sequence of rules transforms state X into

state Y, then any permutation of those rules that is allowable also transforms state x into state Y.


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Theorem proving falls under monotonic partially communicative system. Blocks world and 8 puzzle

problems like chemical analysis and synthesis come under monotonic, not partially commutative

systems. Playing the game of bridge comes under non monotonic , not partially commutative system.

For any problem, several production systems exist. Some will be efficient than others. Though it may

seem that there is no relationship between kinds of problems and kinds of production systems, inpractice there is a definite relationship.

Partially commutative , monotonic production systems are useful for solving ignorable problems. These

systems are important for man implementation standpoint because they can be implemented without the

ability to backtrack to previous states, when it is discovered that an incorrect path was followed. Such

systems increase the efficiency since it is not necessary to keep track of the changes made in the search

process.

Monotonic partially commutative systems are useful for problems in which changes occur but can be

reversed and in which the order of operation is not critical (ex: 8 puzzle problem).

Production systems that are not partially commutative are useful for many problems in which irreversible

changes occur, such as chemical analysis. When dealing with such systems, the order in which

operations are performed is very important and hence correct decisions have to be made at the first time

itself.

Advantages of production systems:-

1. Production systems provide an excellent tool for structuring AI programs.

2. Production Systems are highly modular because the individual rules can be added, removed or modified

independently.

3. The production rules are expressed in a natural form, so the statements contained in the knowledge base

should the a recording of an expert thinking out loud.

Disadvantages of Production Systems:-

One important disadvantage is the fact that it may be very difficult analyse the flow of control within aproduction system because the individual rules dont call each other.


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2.3 Search Techniques:-

Search refers to the search for a solution in a problem space.

Search proceeds with different types of search control strategies.

Search is fundamental to the problem-solving process.

Search is a general mechanism that can be used when more direct method is not known.

There are two types of search Techniques:-

Uninformed search algorithms or Brute-force algorithms search, through the search

space, all possible candidates for the solution checking whether each candidate satisfies

the problem's statement.

Informed search algorithms use heuristic functions, that are specific to the problem,

apply them to guide the search through the search space to try to reduce the amount of

time spent in searching.

Example ofUninformed search are

Depth First Search

Breadth First Search

Example ofInformed Search are

Hill Climbing

Best First Search

A* Search

AO* Search


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2.3.1 Depth First Search

A search strategy that extends the current path as far as possible before backtracking to the last

choice point and trying the next alternative path is called Depth-first search.

DFS is an uninformed search that progresses by expanding the first child node of the search tree that

appears and thus going deeper and deeper until a goal node is found, or until it hits a node that has

no children. Then the search backtracks, returning to the most recent node it hasn't finished exploring.

In a non-recursive implementation, all freshly expanded nodes are added to a stack for exploration.

Depth first search works by taking a node, checking its neighbors, expanding the first node it finds

among the neighbors, checking if that expanded node is our destination, and if not, continue exploring

more nodes.Depth First Search uses last-in first-out stack for keeping the unexpanded nodes.

More commonly, depth-first search is implemented recursively, with the recursion stack taking the

place of an explicit node stack.

This procedure finds whether the goal can be reached or not but the path it has to follow has not been

mentioned. Diving downward into a tree as quickly as possible performs DFS searches.

This strategy does not guarantee that the optimal solution has been found.

In this strategy, search reaches a satisfactory solution more rapidly than breadth first, an advantage

when the search space is large.

ALGORITHM: Depth First Search

Put the root node on a stack;

while (stack is not empty)

{ remove a node from the stack;

if (node is a goal node) return success;

put all children of node onto the stack; }

return failure;

Example of Depth First Search

Let us take a example of searching of a lost Car keys at home.In this car key example, the BFS search orderfollows node sequence: 1, 2, 3, 4, 5 and so on in Depth -first order.

I'll start with the Room 1 . If I can't see them at a glance, I should check in the wardrobe 1: I can't see my key,

but there are some clothes. These trousers? Nothing in the pockets but some old wallet... Maybe inside the

wallet? No. What else is there in the wardrobe? Check everything thoroughly.Then I will check desk 1.After

completely elliminating any possibility that the keys might be in the Room 1, I'll move on to the next room i.e.

Room2. Once I start checking the hallway in such minute detail, one item at a time, I will find them inside thecoat, after I start inspecting the rack

This way Depth First Search works

Depth-first traversal: Home Room AWardrobe 1 Desk 1 Wardrobe 2 CAR KEY


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ADVANTAGES OF DEPTH-FIRST SEARCH

1. The advantage of depth-first Search is that memory requirement is only linear with respect to thesearch graph. This is in contrast withbreadth-first search which requires more space. The reason is thatthe algorithm only needs to store a stack of nodes on the path from the root to the current node.

2. The time complexity of a depth-first Search to depth d is O(b^d) since it generates the same set ofnodes as breadth-first search, but simply in a different order. Thus practically depth-first search is time-limited rather than space-limited.

3. If depth-first search finds solution without exploring much in a path then the time and space it takeswill be very less.

DISADVANTAGES OF DEPTH-FIRST SEARCH

1. The disadvantage of Depth-First Search is that there is a possibility that it may go down the left-mostpath forever. Even a finite graph can generate an infinite tree.

2. Depth-First Search is not guaranteed to find the solution.

3. And there is no guarantee to find a minimal solution, if more than one solution exists


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2.3.2 Breadth First Search

Breadth-first search (BFS) technique is a systematic search strategy which begins at an initial node

(an initial state) and from the initial node search actions are applied downward on a tree structure in a

breadth-wise order.

The search generates all nodes at a particular level before proceeding to the next level of the tree.

The search systematically proceeds testing each node that is reachable from a parent node before it

expands to any child of those nodes.

In this strategy, no viable solution is omitted and therefore guarantee that optimal solution is found.

BFS is often not feasible when the search space is large.

The search action consists of:

1. Selecting a node as a current node

2. Testing the current node for meeting the goal test criteria

3. If the goal state is not reached, select the next node in a breadth-wise order

4. The search ends either when all the nodes have been searched or if the goal has been found.

ALGORITHM: BREADTH-FIRST SEARCH

Put the root node on a queue;

while (stack is not empty)

{ remove a node from the queue;

if (node is a goal node) return success;

put all children of node onto the queue; }

return failure;

ADVANTAGES OF BREADTH-FIRST SEARCH

1. Breadth first search will never get trapped exploring the useless path forever.

2. If there is a solution, BFS will definitely find it out.

3. If there is more than one solution then BFS can find the minimal one that requires less number of

steps.

DISADVANTAGES OF BREADTH-FIRST SEARCH

1. The main drawback of Breadth first search is its memory requirement. Since each level of the treemust be saved in order to generate the next level, and the amount of memory is proportional to the

number of nodes stored, the space complexity of BFS is O(bd). As a result, BFS is severely space-

bound in practice so will exhaust the memory available on typical computers in a matter of minutes.

2. If the solution is farther away from the root, breath first search will consume lot of time.

Example of BFS Search


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In the car key example, the BFS search order follows node sequence: 1, 2, 3, 4, 5 and so on in breadth -

first order.

The BFS search will first go through all the rooms, then all the desks, then all the drawers in that sequence.

Breadth First Traversal:

Home Room ARoom BRoom CWardrobe 1Desk 1Wardrobe 2Car Keys


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2.3.3 Hill Climbing Search

Hill Climbing Search Technique is a variety of depth-first (generate - and - test) search. A feedback is used

here to decide on the direction of motion in the search space. In the depth-first search, the test function will

merely accept or reject a solution. But in hill climbing the test function is provided with a heuristic function

which provides an estimate of how close a given state is to goal state

Hill climbing is an optimization technique for solving computationally hard problems. It is best used in problems

with the property that the state description itself contains all the information needed for a solution. The

algorithm is memory efficient since it does not maintain a search tree: It looks only at the current state and

immediate future states.

Hill climbing attempts to iteratively improve the current state by means of an evaluation function. Consider all

the [possible] states laid out on the surface of a landscape. The height of any point on the landscape

corresponds to the evaluation function of the state at that point

In contrast with other iterative improvement algorithms, hill-climbing always attempts to make changes that

improve the current state. In other words, hill-climbing can only advance if there is a higher point in the

adjacent landscape.

Hil climbing is an example of an informed search method because it uses information about the search space

to search in a reasonably efficient manner. Hill Climbing is like climbing Everest in thick fog with an altimeter

without map and having problem of amnesia(i.e. loss of memory)


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2.3.4 Best First Search

The best-first search is a way of combining the advantages of both depth-first and breadth-first search.Best-

first search, rather than plunging as deep as possible into the tree (as in depth-first search), or traversing eachlevel of the tree in succession (as in breadth-first search), uses a heuristic to decide at each stage which is the

best place to continue the search.The best-first search can be implemented using priority queue.

The depth-first search is good because it allows a solution to be found without all competing branches having

to be expanded. Breadth-first search is good because it does not get trapped on dead ends of paths.The way

of combining this is to follow a single path at a time but swiches between paths whenever some competing

path looks more promising than the current one does.Hence at each step of best-first search process, we

select most promising node out of successor nodes that have been generated so far.

In Best-First search, the search space is evaluated according to a heuristic function. Nodes yet to be evaluated

are kept on an OPEN list and those that have already been evaluated are stored on a CLOSED list. The

OPEN list is represented as a priority queue, such that unvisited nodes can be dequeued in order of their

evaluation function.

The evaluation function f(n) is made up of two parts.

These are the heuristic function (h( n )) and the estimated cost (g(n)), where

f (n) = g(n)+h(n)

We can think of the estimated cost as a value measurable from our search space, and the heuristic function as

an educated guess. The OPEN list is then built in order of fl n ). This makes best-first search fundamentally

greedy because it always chooses the best local opportunity in the search frontier

The search frontier is defined as the set of node opportunities that can be searched next. In Best-First search,

the fivntieris a priority queue sorted in f( n) order Given the strict order of f( n ), the selection of the node to

evaluate from the priority queue is greedy.

2.3.4.1 Algorithm for Best-First Search

1. Use two ordered lists OPEN and CLOSED.

2. Start with the initial node n0 and put it on the ordered list OPEN.

3. Create a list CLOSED. This is initially an empty list.

4. If OPEN is empty exit with failure.

5. Select first node on OPEN. Remove it from OPEN and put it on CLOSED.Call this node n.

6. If n is the goal node then terminate search with Success, else

7. Expand node n. This will generate all the successors of node n.Find out the value of heuristic

function of all nodes. Sort all the children generated so far on the basis of their utility value. Select the

node of minimum heuristic value for further expansion

8. Reorder the list OPEN according to the heuristic and go back to step 4.


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2.3.4.2 APPLICATIONS

Best-first search and its more advanced variants have been used in such applications as games and web

crawlers. In a web crawler, each web page is treated as a node, and all the hyperlinks on the page are treated

as unvisited successor nodes. A crawler that uses best-first search generally uses an evaluation function that

assigns priority to links based on how closely the contents of their parent page resemble the search query. In

games, best-first search may be used as a path-finding algorithm for game characters. For example, it could

be used by an enemy agent to find the location of the player in the game world. Some games divide up the

terrain into tiles which can either be blocked or unblocked. In such cases, the search algorithm treats each

tile as a node, with the neighbouring unblocked tiles being successor nodes, and the goal node being the

destination tile


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2.3.5 A* Search Technique

Most widely used best first search form is called A*, which is pronounced as A star. It is a heuristic searchingmethod, and used to minimize the search cost in a given problem. It aims to find the least-cost path from a

given initial node to the specific goal. It is an extended form of best-first search algorithm. Best first-search

algorithm tries to find a solution to minimize the total cost of the search pathway, too. However, the difference

from Best-First Search is that A* also takes into account the cost from the start, and not simply the local cost

from the previously been node. Best-first search finds a goal state in any predetermined problem space.

However, it cannot guarantee that it will choose the shortest path to the goal . For instance, if there are two

options to chose from, one of which is a long way from the initial point but has a slightly shorter estimate of

distance to the goal, and another that is very close to the initial state but has a slightly longer estimate of

distance to the goal, best-first search will always choose to expand next the state with the shorter estimate.

The A* algorithm fixes the best first searchs this particular drawback .

In short, A* algorithm searches all possible routes from a starting point until it finds the shortest path or

cheapest cost to a goal. The terms like shortest path, cheapest cost here refer to a general notion. It could be

some other alternative term depending on the problem. For instance, in a map problem the cost is replaced by

the term distance. This may reduce the necessity to search all the possible pathways in a search space, and

result in faster solution. A* evaluates nodes by combining g(n) and h(n). In the standard terminology used

when talking about A*:

f(n)= g(n)+h(n)

The purpose of this equation is to obtain the lowest f score in a given problem. n being node number crossed

until the final node,

f(n) is the total search cost, g(n) is actual lowest cost( shortest distance traveled) of the path from initial start

point to the node n, h(n) is the estimated of cost of cheapest(distance) from the node n to a goal node. This

part of the equation is also called heuristic function/estimation.

At each node, the lowest f value is chosen to be the next step to expand until the goal node is chosen and

reached for expansion. Whenever the heuristic function satisfies certain conditions, A* search is both complete

and optimal

Algorithm of A*

1.The algorithm maintains two sets

OPEN list: The OPEN list keeps track of those nodes that need to be examined.

CLOSED list: The CLOSED list keeps track of nodes that have already been examined.

2.Initially, the OPEN list contains just the initial node, and the CLOSED list is empty. Each node n maintains

the following:

g(n) = the cost of getting from the initial node to n.


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h(n) = the estimate, according to the heuristic function,of the cost of getting from n to the goal node.

F(n) = g(n) + h(n); intuitively, this is the estimate of the best solution that goes through n.

3. Each node also maintains a pointer to its parent, so that later the best solution if found can be retrieved. A-

Star has a main loop that repeatedly gets the node, call it n, with the lowest f(n) value from the OPEN list. If n

is the goal node, then we are done, and the solution is given by backtracking from n. Otherwise, n is removed

from the OPEN list and added to the CLOSED list. Next all the possible successor nodes of n are generated.

4. For each successor node n, if it is already In the CLOSED list and the copy there has an equal or lower f

estimate, and then we can safely discard the newly generated n and move on. Similarly, if n is already in the

OPEN list and the copy there has an equal or lower f estimate, we can discard the newly generated n and

move on.

Practical Applications of A*

A* is the most popular choice for path finding, because it's fairly flexible and can be used in a widerange of contexts such as games (8-puzzle and a path finder).

Bidirectional search

Iterative deepening

Beam search

Dynamic weighting

Bandwidth search


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2.3.6 AO* Search

AO* Search Procedure.

1. Place the start node on open.

2. Using the search tree, compute the most promising solution tree TP .

3. Select node n that is both on open and a part of tp, remove n from open and place it no closed.

4. If n is a goal node, label n as solved. If the start node is solved, exit with success where tp is the solution

tree, remove all nodes from open with a solved ancestor.

5. If n is not solvable node, label n as unsolvable. If the start node is labeled as unsolvable, exit with failure.

Remove all nodes from open ,with unsolvable ancestors.

6. Otherwise, expand node n generating all of its successor compute the cost of for each newly generated

node and place all such nodes on open.

7. Go back to step(2)

Note: AO* will always find minimum cost solution.


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2.3.7 Control Strategies

Search for a solution in a problem space, requires "Control Strategies" to control the search processes.

Which rule to apply next?

More than one rule will have its left side match the current state.

The first requirement of a good control strategy is that it causes motion

The second requirement of a good control strategy is that it be systematic.

Construct a tree and weigh out the various options at each step.

This is where BFS and DFS comes into use

Heuristic Search can also be used

The search control strategies are of different types, and are realized by some specific type of "Control

Structures".

Strategies for search

Some widely used control strategies for search are stated below.

Forward search: Here, the control strategies for exploring search proceeds forward from initial state

to wards a solution;the methods are called data-directed. Backward search: Here, the control strategies for exploring search proceeds backward from a goal

or final state towards either a solvable sub problem or the initial state; the methods are called goal

directed.

Both forward and backward search: Here, the control strategies for exploring search is a mixture

of both forward and backward strategies .

Systematic search : Where search space is small, a systematic (but blind) method can be used to

explore the whole search space.One such search method is depth-first search andthe other is breath-

first search.


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2.4 Knowledge Representation Issues

The fundamental goal of Knowledge Representation is to facilitate inferencing (conclusions) from knowledge.

The issues that arise while using KR techniques are many. Some of these are explained below.

Important Attributes :

Any attribute of objects so basic that they occur in almost every problem domain ?

Relationship among attributes:

Any important relationship that exists among object attributes ?

Choosing Granularity :

At what level of detail should the knowledge be represented ?

Set of objects :

How sets of objects be represented ?

Finding Right structure :

Given a large amount of knowledge stored, how can relevant parts be accessed ?


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2.5 Propositional and predicate logic

Propositional Logic:-

The simplest form of logic is Propositional Logic. A proposition is a statement that is either true or false. A

string of symbols separated by conjunctions (AND), disjunctions (OR) negations (NOT). Propositional Logic

assumes that knowledge contains only facts. The truth of a statement is known, it becomes a premise that can

be used to derive new propositions.

Propositional logic is declarative

Propositional logic allows partial/disjunctive/negated information

(unlike most data structures and databases)

Propositional logic is compositional:

meaning of B1,1 U P1,2 is derived from meaning of B1,1 and of P1,2

Meaning in propositional logic is context-independent

(unlike natural language, where meaning depends on context)

Propositional logic has very limited expressive power

(unlike natural language)

Predicate Logic:

Language like representation of knowledge that facilitates inference using predicate calculus. Propositional

logic is useful, but it has one big drawback: we cannot talk generally, but only about specific examples

(propositions).

A predicate is a proposition whose truth depends on the value of one or more variables.

For example, n is a perfect square is a predicate whose truth depends on the value of n.

A function like notation is used to denote a predicate supplied with specific variable values. P(n)=n is a perfect

square

P(4) is true and P(5) is false.


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2.6 Forward and Backward Reasoning

2.6.1 Forward Reasoning: In a forward chaining system you start with the initial facts, and keep using the rules to draw new

conclusions (or take certain actions) given those facts

Forward chaining systems are primarily data-driven.facts in the system are represented in a working

memory which is continually updated.Rules in the system represent possible actions to take when

specified conditions hold on items in the working memory They are sometimes called condition-action

rules. The conditions are usually patterns that must match items in the working memory.Actionsusually involve addingordeletingitems from the working memory. Interpreter controls the application

of the rules, given the working memory, thus controlling the system's activity. It is based on a cycle of

activity sometimes known as a recognize-actcycle

The system first checks to find all the rules whose conditions hold selects one and performs the

actions in the action part of the rule selection of a rule to fire is based on fixed strategies, known as

conflict resolution strategies.Actions usually involve adding or deleting items from the working

memory. Interpreter controls the application of the rules, given the working memory, thus controlling

the system's activity. It is based on a cycle of activity sometimes known as a recognize-act cycle. The

system first checks to find all the rules whose conditions hold selects one and performs the actions inthe action part of the rule. Selection of a rule to fire is based on fixed strategies, known as conflict

resolution strategies

Forward chaining may be better if you have lots of things you want to prove.

Forward chaining like an exhaustive search.

Forward chaining system, includes writing rules to manage sub goals.

Lots of output Hypothesis + Lots of data up front => Use Forward Chaining

2.6.2 Backward Reasoning:-

In a backward chaining system you start with some hypothesis (or goal) you are trying to prove, and

keep looking for rules that would allow you to conclude that hypothesis, perhaps setting new subgoals

to prove as you go.

You COULD keep on forward chaining until no more rules apply or you have added your hypothesis to

the working memory but in the process the system is likely to do a lot of irrelevant work, adding

uninteresting conclusions to working memory

This can be done by backward chainingfrom the goal state (or on some hypothesized state that we

are interested in). Note that a backward chaining system does NOTneed to update a working

memory. Instead it needs to keep track of what goals it needs to prove its main hypothesis.

Backward chaining may be better if you are trying to prove a single fact, given a large set of initialfacts, and where, if you used forward chaining, lots of rules would be eligible to fire in any cycle.

Backward chaining is more focused and tries to avoid exploring unnecessary paths of reasoning.


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Backward chaining systems automatically manage sub goals.

Fewer output Hypothesis + Must query for data => Use Backward Chaining.

=

Table 3.Forward vs Backward Reasoning

Attribute Backward Reasoning Forward Reasoning

Also known as Goal-driven Data-driven

Starts from Possible conclusion New data

Processing Efficient Somewhat wasteful

Aims for Necessary data Any Conclusion(s)

Approach Conservative/Cautious Opportunistic

Practical if Number of possible final answers is

reasonable or a set of known

alternatives is available.

Combinatorial explosion creates an

infinite number of possible right answers.

Appropriate for Diagnostic, prescription and

debugging application

Planning, monitoring, control and

interpretation application

Reasoning Top-down reasoning Bottom-up reasoning

Type of Search Depth-first search Breadth-first search

Who determine search Consequents determine search Antecedents determine search

Flow Consequent to antecedent Antecedent to consequent


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2.7 Natural Language Processing(NLP)

Natural language is any language naturally used by humans such as English, French, or Japanese. NLP is a

hallmark of human intelligence. Natural language processing (NLP) can be defined as the automatic (or semi-

automatic) processing of human language. Natural-language-processing programs use artificial intelligence to

allow a user to communicate with a computer in the user's natural language. The computer can both

understand and respond to commands given in a natural language.So Natural Language Processing is a fieldof computer science and linguistics concerned with the interactions between computers and human

languages. .NLP is also known as Computational Linguistics. NLP is used to get computers to perform useful

tasks involving human language, tasks like enabling human-machine communication, improving human-

human communication, or simply doing useful processing of text or speech.

Computer languages are artificial languages, invented for the sake of communicating instructions to computers

and enabling them to communicate with each other. Most computer languages consist of a combination of

symbols, numbers, and some words. These languages are complex and may take years to master. By

programming computers (via computer languages) to respond to our natural languages, we make them easier

to use.

Four problems arise that can cause misunderstanding:

(1) Ambiguityconfusion over what is meant due to multiple meanings of words and phrases.

(2) Imprecisionthoughts are sometimes expressed in vague and inexact terms.

(3) Incompletenessthe entire idea is not presented, and the listener is expected to "read between the lines."

(4) Inaccuracyspelling, punctuation, and grammar problems can obscure meaning.

NLP covers a broad range of activities with the eventual goal of enabling people to communicate with

machines using natural communication skills. It is the scientific study of language from a computational

perspective.

2.7.1Steps in Natural Language Processing:

1) Morphological Analysis: Individual worlds are analyzed into their components and non word tokens, such

as punctuation are separated from the words.

2) Syntactic Analysis: Linear sequences of words are transformed into structures that show how the words

relate each other. Some word sequences may be rejected if they violate the languages rules for how words

may be combined.


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3) Semantic Analysis: The structures created by the syntactic analyzer are assigned meanings.

4) Discourse Integration: The meaning of an individual sentences may depend on the sentences that

precede it and may influence the meanings of the sentence( may depend on the sentences that precede it)

that follow it.

5) Pragmatic Analysis: The structure representing what was said is reinterpreted to determine that what was

actually meant. For example, the sentence Do you know what time it is? should be interpreted as a request

to be told the time.

In its simplest form, a natural language processing program works like this: a sentence is typed in on the

keyboard; if the program can derive meaningthat is, if it has a reference in its knowledge base for every

word and phraseit will respond, more or less appropriately.

An example of a computer with a natural language processor is the computerized card catalog available inmany public libraries. The main menu usually offers four choices for looking up information: search by author,

search by title, search by subject, or search by keyword. If you want a list of books on a specific topic or

subject you type in the appropriate phrase. You are asking the computerin Englishto tell you what is

available on the topic. The computer usually responds in a very short timein Englishwith a list of books

along with call numbers so you can find what you need.

One of the challenges inherent in natural language processing is teaching computers to understand the way

humans learn and use language. Take, for example, the sentence "Baby swallows fly." This simple sentence

has multiple meanings, depending on whether the word "swallows" or the word "fly" is used as the verb, whichalso determines whether "baby" is used as a noun or an adjective. In the course of human communication, the

meaning of the sentence depends on both the context in which it was communicated and each persons

understanding of the ambiguity in human languages. This sentence poses problems for software that must first

be programmed to understand context and linguistic structures.

2.7.2 NLP Applications

Question answering

Who is the first Indian president?

Text Categorization/Routing

e.g., customer e-mails.

Text Mining

Machine (Assisted) Translation

Language Teaching/Learning

Usage checking

Spelling correction


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soft computing unit 1 by varun

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