Unit 1 Introduction to Artificial Intelligence.pptx

Dr.M.Karthika
Assistant Professor/Information Technology
Mannar Thirumalai Naicker College
Pasumalai,Madurai-4.

 Although most attempts to define complex and widely used terms precisely
are exercises in futility, it is useful to draw at least an approximate
boundary around the concept to provide a perspective on the discussion
that follows. To do this, we propose the following by no means universally
accepted definition.
 Artificial intelligence (AI) is the study of how to make computers do things
which, at the moment, people do better. This definition is, of course,
somewhat ephemeral because of its reference to the current state of
computer science. And it fails to include some areas of potentially very
large impact, namely problems that cannot now be solved well by either
computers or people. But it provides a good outline of what constitutes
artificial intelligence, and it avoids the philosophical issues that dominate
attempts to define the meaning of either artificial or intelligence.

Mundane Task
• Perception
-Vision
-Speech
• Natural language
-Understanding
-Generation
-Translation
• Commonsense reasoning
• Robot control

Formal Tasks
• Games
-Chess
-Backgammon
-Checkers –Go
• Mathematics
- Geometry
- Logic
- Integral calculus
- Proving properties of programs

Expert Tasks
• Engineering
-Design
-Fault finding
-Manufacturing planning
• Scientific analysis
• Medical diagnosis
• Financial analysis

 The Physical Symbol System HypothesisA
physical symbol system has the necessary
and sufficient means for general intelligent
action
 In other words:Computers (Turing Machines)
have the power for general intelligent action.

 Artificial Intelligence problems span a very broad
spectrum. They appear to have very little in common
except that they are hard.
 AI Research of earlier decades results into the fact
that intelligence requires knowledge.
Knowledge possess following properties:
 It is voluminous.
 It is not well-organised or well-formatted.
 It is constantly changing.
 It differs from data. And it is organised in a way that
corresponds to its usage.

AI technique is a method that exploits knowledge that
should be represented in such a way that:
 Knowledge captures generalisation. Situations that share
common properties are grouped together. Without the
property, inordinate amount of memory and modifications will
be required.
 It can be easily modified to correct errors and to reflect
changes in the world.
 It can be used in many situations even though it may not be
totally accurate or complete.
 It can be used to reduce its own volume by narrowing range of
possibilities.

Tic-Tac-Toe
The first approach (simple)
 The Tic-Tac-Toe game consists of a nine element vector called
BOARD; it represents the numbers 1 to 9 in three rows.
1 2 3
4 5 6
7 8 9

 An element contains the value 0 for blank, 1 for X and 2 for O. A
MOVETABLE vector consists of 19,683 elements (39) and is needed where each
element is a nine element vector. The contents of the vector are especially chosen
to help the algorithm.
 The algorithm makes moves by pursuing the following:
1. View the vector as a ternary number. Convert it to a decimal number.
2. Use the decimal number as an index in MOVETABLE and access the vector.
3. Set BOARD to this vector indicating how the board looks after the move.
This approach is capable in time but it has several disadvantages.
4. It takes more space and requires stunning effort to calculate the decimal
numbers. This method is specific to this game and cannot be completed.

Question Answering
 In this section we look at a series of programs that read in English text and then answer questions,
also stated in English, about that text. This task differs from the last one in that it is more difficult
now to state formally and precisely what our problem is and what constitutes correct solutions to it.
For example, suppose that the input text were just the single sentence
Russia massed troops on the Czech border.
Then either of the following question-answering dialogues might occur (and in fact did occur with
the POLITICS program [Carbonell, 19801):
 Q: Why did Russia do this?
 A: Because Russia thought that it could take political control of Czechoslovakia by sending troops.
 Q: What should the United States do?
 A: The United States should intervene militarily.
 This program attempts to answer questions using the literal input text. It simply matches text
fragments in the questions against the input text.
 Question Patterns A set of templates that match common question forms and produce patterns to be
used to match against inputs. Templates and patterns (which we call text patterns) are paired so that
if a template matches successfully against an input question then its associated text

patterns are used to try to find appropriate answers in the text. For example, if the
template "Who did xy" matches an input question, then the text pattern "xy 2" is
matched against the input text and the value of z is given as the answer to the
question.
Text The input text stored simply as a long character string.
Question The current question also stored as a character string.
The Algorithm
To answer a question, do the following:
1. Compare each element of Question Patterns against the Question and use all those
that match successfully
to generate a set of text patterns.
2. Pass each of these patterns through a substitution process that generates alternative
forms of verbs so that, for example, "go" in a question might match "went" in the
text. This step generates a new, expanded set of text patterns.
3. Apply each of these text patterns to Text, and collect all the resulting answers.
4. Reply with the set of answers just collected.

We have just examined two series of programs to solve two very different
problems. In each series, the final program exemplifies what we mean by
an AI technique. These two programs are slower to execute than the carlier
ones in their respective series, but they illustrate three important AI
techniques:
• Search-Provides a way of solving problems for which no more direct
approach is available as well as a framework into which any direct
techniques that are available can be embedded..
• Use of Knowledge-Provides a way of solving complex problems by
exploiting the structures of the objects that are involved.
• Abstraction-Provides a way of separating important features and variations
from the many unimportant ones that would otherwise overwhelm any
process.

 Programs in the first class attempt to solve problems that a computer can easily solve and do not
usually use AI techniques. AI techniques usually include a search, as no direct method is available,
the use of knowledge about the objects involved in the problem area and abstraction on which allows
an element of pruning to occur, and to enable a solution to be found in real time; otherwise, the data
could explode in size
. Examples :EPAM (Elementary Perceiver and Memorizer) which memorized garbage syllables.
The second class of problems attempts to solve problems that are non-trivial for a computer and use
AI techniques. We wish to model human performance on these:
1.To test psychological theories of human performance. Ex. PARRY [Colby, 1975] – a program to
simulate the conversational behavior of a paranoid person.
2.To enable computers to understand human reasoning – for example, programs that answer
questions based upon newspaper articles indicating human behavior.

 3.To enable people to understand computer reasoning. Some people are
reluctant to accept computer results unless they understand the mechanisms
involved in arriving at the results.
4. To exploit the knowledge gained by people who are best at gathering
information. This persuaded the earlier workers to simulate human
behavior in the SB part of AISB simulated behavior. Examples of this type
of approach led to GPS (General Problem Solver).
This last motivation is probably the most pervasive of the four. It
motivated several very early systems that attempted to produce intelligent
behavior by imitating people at the level of individual neurons.

 In 1950, Alan Turing proposed the following method for
determining whether a machine can think. His method has since
become known as the Turing Test. To conduct this test, we need two
people and the machine to be evaluated. One person plays the role
of the interrogator, who is in a separate room from the computer and
the other person. The interrogator can ask questions of either the
person or the computer by typing questions and receiving typed
responses. However, the interrogator knows them only as A and B
and aims to determine which is the person and which is the
machine. The goal of the machine is to fool the interrogator into
believing that it is the person.

1. An Al system must contain a lot of knowledge if it is to
handle anything but trivial toy problems.
2. But as the amount of knowledge grows, it becomes
harder to access the appropriate things when needed, so
more knowledge must be added to help. But now there
is even more knowledge to manage, so more must be
added, and so forth.

 Problem definitions
 A problem is defined by its ‘elements’and their ‘relations’. To provide a formal description of a problem, we need to
do the following:
a. Define a state space that contains all the possible configurations of the relevant objects, including some
impossible ones.
b. Specify one or more states that describe possible situations, from which the problem- solving process may
start. These states are called initial states.
c. Specify one or more states that would be acceptable solution to the problem.
1. State Space Search
 A state space represents a problem in terms of states and operators that change states. A state space consists
of:
 A representation of the states the system can be in. For example, in a board game, the board
represents the current state of the game.
 A set of operators that can change one state into another state. In a board game, the operators are
the legal moves from any given state. Often the operators are represented as programs that change a
state representation to represent the new state.
 An initial state.
 A set of final states; some of these may be desirable, others undesirable. This set is often
represented implicitly by a program that detects terminal states.

 Production systems provide appropriate structures for performing and describing search
processes. A production system has four basic components as enumerated below.
• A set of rules each consisting of a left side that determines the applicability of the rule
and a right side that describes the operation to be performed if the rule is applied.
• A database of current facts established during the process of inference.
• A control strategy that specifies the order in which the rules will be compared with
facts in the database and also specifies how to resolve conflicts in selection of several
rules or selection of more facts.
• A rule firing module.

 The first requirement is that it causes motion, in a game playing program, it
moves on the board and in the water jug problem, filling water is used to fill
jugs. It means the control strategies without the motion will never lead to the
solution.
 The second requirement is that it is systematic, that is, it corresponds to the
need for global motion as well as for local motion. This is a clear condition
that neither would it be rational to fill a jug and empty it repeatedly, nor it
would be worthwhile to move a piece round and round on the board in a
cyclic way in a game. We shall initially consider two systematic approaches
for searching. Searches can be classified by the order in which operators are
tried: depth-first, breadth-first, bounded depth-first.

Algorithm:Breath-First Search
1. Create a variable called LIST and set it to be the starting state.
2. Loop until a goal state is found or LIST is empty, Do
a.Remove the first element from the LIST and call it E. If the LIST is empty, quit.
b.For every path each rule can match the state E, Do
(i) Apply the rule to generate a new state.
(ii) If the new state is a goal state, quit and return this state.
(iii) Otherwise, add the new state to the end of LIST.
 Advantages
1. Guaranteed to find an optimal solution (in terms of shortest number of steps to reach the
goal).
2. Can always find a goal node if one exists (complete).
 Disadvantages
 1. High storage requirement: exponential with tree depth.

 Algorithm :Depth-first search
 Depth-first search applies operators to each newly generated state, trying to drive directly toward the goal.
1. If the starting state is a goal state, quit and return success.
2. Otherwise, do the following until success or failure is signalled:
a. Generate a successor E to the starting state. If there are no more successors, then signal failure.
b. Call Depth-first Search with E as the starting state.
c. If success is returned signal success; otherwise, continue in the loop.
 Advantages
1.Low storage requirement: linear with tree depth.
2. Easily programmed: function call stack does most of the work of maintaining state of the search.
The Traveling Salesman Problem: A salesman has a list of cities, each of which he must visit exactly
once. There are direct roads between each pair of cities on the list. Find the route the salesman should
follow for the shortestpossible round trip that both starts and finishes at any one of the cities.
A simple, motion-causing and systematic control structure could, in principle, solve this problem. It would
simply explore all possible paths in the tree and return the one with the shortest length. This approach will
even work in practice for very short lists of cities.

 A heuristic is a method that improves the efficiency of the search
process. These are like tour guides. There are good to the level that
they may neglect the points in general interesting directions; they
are bad to the level that they may neglect points of interest to
particular individuals. Some heuristics help in the search process
without sacrificing any claims to entirety that the process might
previously had. Others may occasionally cause an excellent path to
be overlooked. By sacrificing entirety it increases efficiency.
Heuristics may not find the best solution every time but guarantee
that they find a good solution in a reasonable time. These are
particularly useful in solving tough and complex problems, solutions
of which would require infinite time, i.e. far longer than a lifetime
for the problems which are not solved in any other way.

Heuristic search is a very general method applicable to a large class of
problems. It encompasses a variety of specific techniques, each of which is
particularly effective for a small class of problems. In order to choose the most
appropriate method (or combination of methods) for a particular problem, it is
necessary to analyze the problem along several key dimensions:
 Is the problem decomposable into a set of (nearly) independent smaller or easier
subproblems? . Can solution steps be ignored or at least undone if they prove unwise?
 Is the problem's universe predictable?
 Is a good solution to the problem obvious without comparison to all other possible
solutions?
 Is the desired solution a state of the world or a path to a state?• Is a large amount of
knowledge absolutely required to solve the problem, or is knowledge importantonly
to constrain the search?
 Can a computer that is simply given the problem return the solution, or will the
solution of the problemrequire interaction between the computer and a person?

Problem Decomposition
Suppose to solve the expression is: + (X³ + X² + 2X + 3sinx) dx
This problem can be solved by breaking it into smaller problems, each of which we can solve by using a small
collection of specific rules. Using this technique of problem decomposition, we can solve very large problems very easily. This can
be considered as an intelligent behaviour.
Can Solution Steps be Ignored?
Suppose we are trying to prove a mathematical theorem: first we proceed considering that proving a lemma will be useful.
Later we realize that it is not at all useful. We start with another one to prove the theorem. Here we simply ignore the first method.
Consider the 8-puzzle problem to solve: we make a wrong move and realize that mistake. But here, the control strategy must
keep track of all the moves, so that we can backtrack to the initial state and start with some new move.
Consider the problem of playing chess. Here, once we make a move we never recover from that step. These problems are
illustrated in the three important classes of problems mentioned below:

1. Ignorable, in which solution steps can be ignored. Eg: Theorem Proving
2. Recoverable, in which solution steps can be undone. Eg: 8-Puzzle
3. Irrecoverable, in which solution steps cannot be undone. Eg: Chess
Is the Problem Universe Predictable?
 Consider the 8-Puzzle problem. Every time we make a move, we know exactly what will happen. This means
that it is possible to plan an entire sequence of moves and be confident what the resulting state will be. We can
backtrack to earlier moves if they prove unwise.
 Suppose we want to play Bridge. We need to plan before the first play, but we cannot play with certainty. So,
the outcome of this game is very uncertain. In case of 8-Puzzle, the outcome is very certain. To solve uncertain
outcome problems, we follow the process of plan revision as the plan is carried out and the necessary feedback
is provided. The disadvantage is that the planning in this case is often very expensive.

Consider the problem of answering questions based on a database of simple facts
such as the following:
 Siva was a man.
 Siva was a worker in a company.
 Siva was born in 1905.
 All men are mortal.
 All workers in a factory died when there was an accident in 1952.
 No mortal lives longer than 100 years. Suppose we ask a question: ‘Is Siva
alive?’
 By representing these facts in a formal language, such as predicate logic, and then
using formal inference methods we can derive an answer to this question easily.

 Consider the problem of finding a consistent interpretation for
the sentenceThe bank president ate a dish of pasta salad with
the fork.There are several components of this sentence, each
of which, in isolation, may have more than one interpretation.
But the components must form a coherent whole, and so they
constrain each other's interpretations. Some of the sources of
ambiguity in this sentence are the following:
The word "bank" may refer either to a financial institution
or to a side of a river. But only one of these may have a
president.

 The names of the candidates in each party.
 The fact that if the major thing you want to see done is
have taxes lowered, you are probably supporting the
Republicans.
 The fact that if the major thing you want to see done is
improved education for minority students, youare
probably supporting the Democrats.
 And so on...

 Sometimes it is useful to program computers to solve
problems in ways that the majority of people would not be
able to understand. This is fine if the level of the interaction
between the computer and its human users is problem in
solution-out. But increasingly we are building programs
that require intermediate interaction with people, both to
provide additional input to the program and to provide
additional reassurance to the user.
Consider, for example, the problem of proving mathematical
theorems. If
1.All we want is to know that there is a proof
2. The program is capable of finding a proof by itself

 Problem ClassificationWhen actual problems are examined from
the point of view of all of these questions, it becomes apparent
that there are several broad classes into which the problems fall.
These classes can each be associated with a generic control
strategy that is appropriate for solving the problem. For example,
consider the generic problem of classification. The task here is to
examine an input and then decide which of a set of known
classes the input is an instance of. Most diagnostic tasks,
including medical diagnosis as well as diagnosis of faults in
mechanical devices, are examples of classification. Another
example of a generic strategy is propose and refine. Many design
and planning problems can be attacked with this strategy.

 Production systems provide us with good ways of describing the operations that can be performed in
a search for a solution to a problem.
 At this time, two questions may arise:
1. Can production systems be described by a set of characteristics? And how can they be easily
implemented?
2. What relationships are there between the problem types and the types of production systems well
suited for solving the problems?
 To answer these questions, first consider the following definitions of classes of production systems:
1. A monotonic production system is a production system in which the application of a
 rule never prevents the later application of another rule that could also have been applied at the
time the first rule was selected.
2. A non-monotonic production system is one in which this is not true.
3. A partially communicative production system is a production system with the property that if the
application of a particular sequence of rules transforms state P into state Q, then any combination of
those rules that is allowable also transforms state P into state Q.
4. A commutative production system is a production system that is both monotonic and partially
commutative.

 Check duplicate nodes
1.Observe all nodes that are already generated, if a new node is present.
2.If it exists add it to the graph.
3.If it already exists, then
a. Set the node that is being expanded to the point to the already existing
node corresponding to its successor rather than to the new one. The new
one can be thrown away.
b. If the best or shortest path is being determined, check to see if this path
is better or worse than the old one. If worse, do nothing.
 Better save the new path and work the change in length through the chain
of successor nodes if necessary.

 The Missionaries and Cannibals Problem
Three missionaries and three cannibals find
themselves on one side of a river. They have agreed that
they would all like to get to the other side. But the
missionaries are not sure what else the cannibals have
agreed to. So the missionaries want to manage the trip
across the river in such a way that the number of
missionaries on either side of the river is never less than the
number of cannibals who are on the same side. The only
boat available holds only two people at a time. How can
everyone get across the river without the missionaries
risking being eaten?

Book: ARTIFICIAL INTELLIGENCE
Third Edition
Elaine Rice, Kevin Knight , Shivashankar B Nair

Unit 1 Introduction to Artificial Intelligence.pptx

Unit 1 Introduction to Artificial Intelligence.pptx

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