Artificial Intelligence
Artificial Intelligence is a branch of Science which deals with helping machines find
solutions to complex problems in a more human-like fashion. This generally involves
borrowing characteristics from human intelligence, and applying them as algorithms in a
computer friendly way. A more or less flexible or efficient approach can be taken
depending on the requirements established, which influences how artificial the intelligent
behaviour appears.
AI is generally associated with Computer Science, but it has many important links with
other fields such as Maths, Psychology, Cognition, Biology and Philosophy, among many
others. Our ability to combine knowledge from all these fields will ultimately benefit our
progress in the quest of creating an intelligent artificial being.
Why Artificial Intelligence?
Computers are fundamentally well suited to performing mechanical computations, using
fixed programmed rules. This allows artificial machines to perform simple monotonous
tasks efficiently and reliably, which humans are ill-suited to. For more complex problems,
things get more difficult... Unlike humans, computers have trouble understanding specific
situations, and adapting to new situations. Artificial Intelligence aims to improve
machine behaviour in tackling such complex tasks.
Together with this, much of AI research is allowing us to understand our intelligent
behaviour. Humans have an interesting approach to problem-solving, based on abstract
thought, high-level deliberative reasoning and pattern recognition. Artificial Intelligence
can help us understand this process by recreating it, then potentially enabling us to
enhance it beyond our current capabilities.
When will Computers become truly Intelligent?
Limitations...
To date, all the traits of human intelligence have not been captured and applied together
to spawn an intelligent artificial creature. Currently, Artificial Intelligence rather seems to
focus on lucrative domain specific applications, which do not necessarily require the full
extent of AI capabilities. This limit of machine intelligence is known to researchers as
narrow intelligence.
There is little doubt among the community that artificial machines will be capable of
intelligent thought in the near future. It's just a question of what and when... The
machines may be pure silicon, quantum computers or hybrid combinations of
manufactured components and neural tissue. As for the date, expect great things to
happen within this century!
How does Artificial Intelligence work?
Technology...
There are many different approaches to Artificial Intelligence, none of which are either
completely right or wrong. Some are obviously more suited than others in some cases,
but any working alternative can be defended. Over the years, trends have emerged based
on the state of mind of influencial researchers, funding opportunities as well as available
computer hardware.
Over the past five decades, AI research has mostly been focusing on solving specific
problems. Numerous solutions have been devised and improved to do so efficiently and
reliably. This explains why the field of Artificial Intelligence is split into many branches,
ranging from Pattern Recognition to Artificial Life, including Evolutionary Computation
and Planning.
Who uses Artificial Intelligence?
Applications...
game playing
You can buy machines that can play master level chess for a few hundred dollars.
There is some AI in them, but they play well against people mainly through brute
force computation--looking at hundreds of thousands of positions. To beat a world
champion by brute force and known reliable heuristics requires being able to look
at 200 million positions per second.
speech recognition
In the 1990s, computer speech recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its keyboard tree for flight
information by a system using speech recognition of flight numbers and city
names. It is quite convenient. On the the other hand, while it is possible to instruct
some computers using speech, most users have gone back to the keyboard and the
mouse as still more convenient.
understanding natural language
Just getting a sequence of words into a computer is not enough. Parsing sentences
is not enough either. The computer has to be provided with an understanding of
the domain the text is about, and this is presently possible only for very limited
domains.
computer vision
The world is composed of three-dimensional objects, but the inputs to the human
eye and computers' TV cameras are two dimensional. Some useful programs can
work solely in two dimensions, but full computer vision requires partial three-
dimensional information that is not just a set of two-dimensional views. At
present there are only limited ways of representing three-dimensional information
directly, and they are not as good as what humans evidently use.
expert systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to
embody their knowledge in a computer program for carrying out some task. How
well this works depends on whether the intellectual mechanisms required for the
task are within the present state of AI. When this turned out not to be so, there
were many disappointing results. One of the first expert systems was MYCIN in
1974, which diagnosed bacterial infections of the blood and suggested treatments.
It did better than medical students or practicing doctors, provided its limitations
were observed. Namely, its ontology included bacteria, symptoms, and treatments
and did not include patients, doctors, hospitals, death, recovery, and events
occurring in time. Its interactions depended on a single patient being considered.
Since the experts consulted by the knowledge engineers knew about patients,
doctors, death, recovery, etc., it is clear that the knowledge engineers forced what
the experts told them into a predetermined framework. In the present state of AI,
this has to be true. The usefulness of current expert systems depends on their
users having common sense.
heuristic classification
One of the most feasible kinds of expert system given the present knowledge of
AI is to put some information in one of a fixed set of categories using several
sources of information. An example is advising whether to accept a proposed
credit card purchase. Information is available about the owner of the credit card,
his record of payment and also about the item he is buying and about the
establishment from which he is buying it (e.g., about whether there have been
previous credit card frauds at this establishment).
Automated reasoning
Automated reasoning is an area of computer science dedicated to understand different
aspects of reasoning. The study in automated reasoning helps produce software which
allows computers to reason completely, or nearly completely, automatically. Although
automated reasoning is considered a sub-field of artificial intelligence it also has
connections with theoretical computer science and even philosophy.
The most developed subareas of automated reasoning are automated theorem proving
(and the less automated but more pragmatic subfield of interactive theorem proving) and
automated proof checking (viewed as guaranteed correct reasoning under fixed
assumptions). Extensive work has also been done in reasoning by analogy induction and
abduction. Other important topics are reasoning under uncertainty and non-monotonic
reasoning. An important part of the uncertainty field is that of argumentation, where
further constraints of minimality and consistency are applied on top of the more standard
automated deduction. John Pollock's Oscar system is an example of an automated
argumentation system that is more specific than being just an automated theorem prover.
Tools and techniques of automated reasoning include the classical logics and calculi,
fuzzy logic, Bayesian inference, reasoning with maximal entropy and a large number of
less formal ad-hoc techniques.
Applications
Automated reasoning has been most commonly used to build automated theorem provers.
In some cases such provers have come up with new approaches to proving a theorem.
Logic Theorist is a good example of this. The program came up with a proof for one of
the theorems in Principia Mathematica which was more efficient (less number of steps
for solving a theorem) than the one provided by Whitehead and Russel. Automated
reasoning programs are being applied to solve a growing number of problems in formal
logic, mathematics and computer science, logic programming, software and hardware
verification, circuit design, and many others. The TPTP (Sutcliffe and Suttner 1998) is a
library of such problems that is updated on a regular basis. There is also a competition
among automated theorem provers held regularly at the CADE conference (Pelletier,
Sutcliffe and Suttner 2002); the problems for the competition are selected from the TPTP
library.
Problem Solving in games
The Water Jugs Problem
You are given two jugs, a 4-gallon one and 3-gallon one. Neither has any measuring
marks on it. There is a pump that can be used to fill the jugs with water. How can you get
exactly 2 gallons of water into the 4-gallon jug?
Missionaries and cannibals problem
The missionaries and cannibals problem, are classic river-crossing problems.[1] The
missionaries and cannibals problem is a well-known toy problem in artificial intelligence.
The problem
In the missionaries and cannibals problem, three missionaries and three cannibals must
cross a river using a boat which can carry at most two people, under the constraint that,
for both banks, if there are missionaries present on the bank, they cannot be outnumbered
by cannibals (if they were, the cannibals would eat the missionaries.) The boat cannot
cross the river by itself with no people on board.[1]
Solving
Amarel devised a system for solving the Missionaries and Cannibals problem whereby
the current state is represented by a simple vector <a,b,c>. The vector's elements
represent the number of missionaries on the wrong side, the number of cannibals on the
wrong side, and the number of boats on the wrong side, respectively. Since the boat and
all of the missionaries and cannibals start on the wrong side, the vector is initialized to
<3,3,1>. Actions are represented using vector subtraction/addition to manipulate the state
vector. For instance, if a lone cannibal crossed the river, the vector <0,1,1> would be
subtracted from the state to yield <3,2,0>. The state would reflect that there are still three
missionaries and two cannibals on the wrong side, and that the boat is now on the
opposite bank. To fully solve the problem, a simple tree is formed with the initial state as
the root. The five possible actions (<1,0,1>, <2,0,1>, <0,1,1>, <0,2,1>, and <1,1,1>) are
then subtracted from the initial state, with the result forming children nodes of the root.
Any node that has more cannibals than missionaries on either bank is in an invalid state,
and is therefore removed from further consideration. The valid children nodes generated
would be <3,2,0>, <3,1,0>, and <2,2,0>. For each of these remaining nodes, children
nodes are generated by adding each of the possible action vectors. The algorithm
continues alternating subtraction and addition for each level of the tree until a node is
generated with the vector <0,0,0> as its value. This is the goal state, and the path from the
root of the tree to this node represents a sequence of actions that solves the problem.
Solution
The earliest solution known to the jealous husbands problem, using 11 one-way trips, is
as follows. The married couples are represented as α (male) and a (female), β and b, and γ
and c.
Trip number
Starting bank
Travel
Ending bank
(start)
αa βb γc
1
βb γc
αa →
2
βb γc
←α
a
3
αβγ
bc →
a
4
5
6
7
8
9
10
11
(finish)
αβγ
αa
αa
ab
ab
b
b
←a
βγ →
← βb
αβ →
←c
ac→
←β
βb →
bc
bc
γc
γc
αβγ
αβγ
αa γc
αa γc
αa βb γc
Traveling Salesman Problem:
1. Introduction
1.1 Origin
The traveling salesman problem (TSP) were studied in the 18th century by a
mathematician from Ireland named Sir William Rowam Hamilton and by the British
mathematician named.Thomas Penyngton Kirkman. Detailed discussion about the work
of Hamilton & Kirkman
can be seen from the book titled Graph Theory (Biggs et al. 1976). It is believed that the
general form of the TSP have been first studied by Kalr Menger in Vienna and Harvard.
The problem was later promoted by Hassler, Whitney & Merrill at Princeton. A detailed
dscription about the connection between Menger & Whitney, and the development of the
TSP can be found in
1.2 Definition
Given a set of cities and the cost of travel (or distance) between each possible pairs, the
TSP,is to find the best possible way of visiting all the cities and returning to the starting
point that minimize the travel cost (or travel distance).
1.3 Complexity
Given n is the number of cities to be visited, the total number of possible routes covering
all
cities can be given as a set of feasible solutions of the TSP and is given as (n-1)!/2.
2. Applications and linkages
2.1 Application of TSP and linkages with other problems
i. Drilling of printed circuit boards
A direct application of the TSP is in the drilling problem of printed circuit boards (PCBs)
(Grötschel et al., 1991). To connect a conductor on one layer with a conductor on another
layer, or to position the pins of integrated circuits, holes have to be drilled through the
board. The holes may be of different sizes. To drill two holes of different diameters
consecutively, the head of the machine has to move to a tool box and change the drilling
equipment. This is quite time consuming. Thus it is clear that one has to choose some
diameter, drill all holes of the same diameter, change the drill, drill the holes of the next
diameter, etc. Thus, this drilling problem can be viewed as a series of TSPs, one for each
hole diameter, where the 'cities' are the initial position and the set of all holes that can be
drilled with one and the same drill. The 'distance' between two cities is given by the time
it takes to move the drilling head from one position to the other. The aim is to minimize
the travel time for the machine head
ii. Overhauling gas turbine engines
(Plante et al., 1987) reported this application and it occurs when gas turbine engines of
aircraft have to be overhauled. To guarantee a uniform gas flow through the turbines
there are nozzle-guide vane assemblies located at each turbine stage. Such an assembly
basically consists of a number of nozzle guide vanes affixed about its circumference. All
these vanes have individual characteristics and the correct placement of the vanes can
result in substantial benefits (reducing vibration, increasing uniformity of flow, reducing
fuel The problem of placing the vanes in the best possible way can be modeled as
a TSP with a special objective function
iii. X-Ray crystallography
Analysis of the structure of crystals (Bland & Shallcross, 1989; Dreissig & Uebach,
1990) is an important application of the TSP. Here an X-ray diffractometer is used to
obtain information about the structure of crystalline material. To this end a detector
measures the intensity of Xray reflections of the crystal from various positions. Whereas
the measurement itself can be accomplished quite fast, there is a considerable overhead in
positioning time since up to hundreds of thousands positions have to be realized for some
experiments. In the two examples that we refer to, the positioning involves moving four
motors. The time needed to move from one position to the other can be computed very
accurately. The result of the experiment does not depend on the sequence in which the
measurements at the various positions are taken. However, the total time needed for the
experiment depends on the sequence. Therefore, the problem consists of finding a
sequence that minimizes the total positioning time. This leads to a traveling salesman
problem.
iv. Computer wiring
(Lenstra & Rinnooy Kan, 1974) reported a special case of connecting components on a
computer board. Modules are located on a computer board and a given subset of pins has
tobe connected. In contrast to the usual case where a Steiner tree connection is desired,
here the requirement is that no more than two wires are attached to each pin. Hence we
have the problem of finding a shortest Hamiltonian path with unspecified starting and
terminating points. A similar situation occurs for the so-called testbus wiring. To test the
manufactured board one has to realize a connection which enters the board at some
specified point, runs through all the modules, and terminates at some specified point. For
each module we also have a specified entering and leaving point for this test wiring. This
problem also amounts to solving a Hamiltonian path problem with the difference that the
distances are not symmetric and that starting and terminating point are specified.
v. The order-picking problem in warehouses
This problem is associated with material handling in a warehouse (Ratliff & Rosenthal,
1983). Assume that at a warehouse an order arrives for a certain subset of the items
stored in the warehouse. Some vehicle has to collect all items of this order to ship them to
the customer. The relation to the TSP is immediately seen. The storage locations of the
items correspond to the nodes of the graph. The distance between two nodes is given by
the time needed to move the vehicle from one location to the other. The problem of
finding a shortest route for the vehicle with minimum pickup time can now be solved as a
TSP. In special cases this problem can be solved easily, see (van Dal, 1992) for an
extensive discussion and for references
vi. Vehicle routing
Suppose that in a city n mail boxes have to be emptied every day within a certain period
of time, say 1 hour. The problem is to find the minimum number of trucks to do this and
the shortest time to do the collections using this number of trucks. As another example,
suppose that n customers require certain amounts of some commodities and a supplier
has to satisfy all demands with a fleet of trucks. The problem is to find an assignment of
customers to the trucks and a delivery schedule for each truck so that the capacity of each
truck is not exceeded and the total travel distance is minimized. Several variations of
these two problems, where time and capacity constraints are combined, are common in
many real world applications. This problem is solvable as a TSP if there are no time and
capacity
vii. Mask plotting in PCB production
For the production of each layer of a printed circuit board, as well as for layers of
integrated semiconductor devices, a photographic mask has to be produced. In our case
for printed circuit boards this is done by a mechanical plotting device. The plotter moves
a lens over a photosensitive coated glass plate. The shutter may be opened or closed to
expose specific parts of the plate. There are different apertures available to be able to
generate different structures on the board. Two types of structures have to be considered.
A line is exposed on the plate by moving the closed shutter to one endpoint of the line,
then opening the shutter and moving it to the other endpoint of the line. Then the shutter
is closed. A point type structure is generated by moving (with the appropriate aperture) to
the position of that point then opening the shutter just to make a short flash, and then
closing it again. Exact modeling of the plotter control problem leads to a problem more
complicated than the TSP and also more complicated than the rural postman problem. A
real-world application in the actual production environment is reported in
Monkey and banana problem
A monkey is in a room. Suspended from the ceiling is a bunch of bananas, beyond the
monkey's reach. However, in the room there are also a chair and a stick. The ceiling is
just the right height so that a monkey standing on a chair could knock the bananas down
with the stick. The monkey knows how to move around, carry other things around, reach
for the bananas, and wave a stick in the air. What is the best sequence of actions for the
monkey to take to acquire lunch
Purpose of the problem
There are many applications of this problem. One is as a toy problem for computer
science.
Another possible purpose of the problem is to raise the question: Are monkeys
intelligent? Both humans and monkeys have the ability to use mental maps to remember
things like where to go to find shelter, or how to avoid danger. They can also remember
where to go to gather food and water, as well as how to communicate with each other.
Monkeys have the ability not only to remember how to hunt and gather but to learn new
things, as is the case with the monkey and the bananas: despite the fact that the monkey
may never have been in an identical situation, with the same artifacts at hand, a monkey
is capable of concluding that it needs to make a ladder, position it below the bananas, and
climb up to reach for them.
The degree to which such abilities should be ascribed to instinct or learning is a matter of
debate. In December 2007, a pigeon was observed as having the capacity to solve the
problem
Solution
The monkey can perform the following
actions:
– Walk on the floor
– Climb the box
– Push the box around (if it is already at the box)
– Grasp the banana if standing on the box
directly under the banana.
Monkey World is described by some 'state'
that can change in time.
• Current state is determined by the position of
the objects
• State:
– Monkey Horizontal
– Monkey Vertical
– Box Position
– Has Banana
• Initial State:
– Monkey is at the door
– Monkey is on floor
– Box is at window
– Monkey does not have banana
• In prolog:
– state(atdoor, onfloor, atwindow, hasnot).
Function
• Goal:
– state(_, _, _, has).
Anonymous Variables
• Allowed Moves:
– Grasp banana
– Climb box
– Push box
– Walk around
• Not all moves are possible in every possible
state of the world e.g. grasp is only possible
if the monkey is standing on the box directly
under the banana and does not have the
banana yet.
• Move from one state to another
• In prolog:
– move(State1, Move, State2)
Grasp
Climb
Push
Walk
• Grasp
– move(state(middle, onbox, middle, hasnot),
grasp,
state(middle, onbox, middle, has)).
• Climb
– move(state(P, onfloor, P, H),
climb,
state(P, onbox, P, H)).
• Push
– move(state(P1, onfloor, P1, H),
push(P1, P2),
state(P2, onfloor, P2, H)).
• Walk
– move(state(P1, onfloor, B, H),
walk(P1, P2),
state(P2, onfloor, B, H)).
• Main question our program will pose:
– Can the monkey in some initial state get the
banana?
• Prolog predicate:
– canget(State)
• Canget(State)
– (1) For any state in which the monkey already
has the banana the predicate is true
– canget(state(_, _, _, has)).
• Canget(State)
– (2) In other cases one or more moves are
necessary. The monkey can get the banana in
any state (State1) if there is some move
(Move) from State1 to some state (State2),
such that the monkey can get the banana in
State2 (in zero or more moves).
– canget(State1) :move(State1, Move, State2),
– canget(State2).
• Questions:
– ?- canget(state(atwindow, onfloor, atwindow, has)).
– Yes
– ?- canget(state(atdoor, onfloor, atwindow, hasnot)).
– Yes
– ?- canget(state(atwindow, onbox, atwindow, hasnot)).
– No
• Clause Order
– Grasp
– Climb
– Push
– Walk
• Effectively says that the monkey prefers
grasping to climbing, climbing to pushing
etc...
• This order of preferences helps the monkey
to solve the problem.
• Reorder Clauses
– Walk
– Grasp
– Climb
– Push
• This results in an infinite loop!
– As the first move the monkey chooses will
always be move, therefore he moves
aimlessly around the room.
• Conclusion:
– A program in Prolog may be declaratively
correct, but procedurally incorrect.
– i.e. Unable to find a solution when a solution
actually exists.
• However, there are methods that solve this
problem.
8 Puzzle Problem.
The 8 puzzle consists of eight numbered, movable tiles set in a 3x3 frame. One cell of the
frame is always empty thus making it possible to move an adjacent numbered tile into the
empty cell. Such a puzzle is illustrated in following diagram.
The program is to change the initial configuration into the goal configuration. A solution
to the problem is an appropriate sequence of moves, such as “move tiles 5 to the right,
move tile 7 to the left ,move tile 6 to the down, etc”.
To solve a problem using a production system, we must specify the global database the
rules, and the control strategy. For the 8 puzzle problem that correspond to these three
components. These elements are the problem states, moves and goal. In this problem each
tile configuration is a state. The set of all configuration in the space of problem states or
the problem space, there are only 3,62,880 different configurations o the 8 tiles and blank
space. Once the problem states have been conceptually identified, we must construct a
computer representation, or description of them . this description is then used as the
database of a production system. For the 8-puzzle, a straight forward description is a 3X3
array of matrix of numbers. The initial global database is this description of the initial
problem state. Virtually any kind of data structure can be used to describe states.
A move transforms one problem state into another state. The 8-puzzle is convenjently
interpreted as having the following for moves. Move empty space (blank) to the left,
move blank up, move blank to the right and move blank down,. These moves are
modeled by production rules that operate on the state descriptions in the appropriate
manner.
The rules each have preconditions that must be satisfied by a state description in order for
them to be applicable to that state description. Thus the precondition for the rule
associated with “move blank up” is derived from the requirement that the blank space
must not already be in the top row.
The problem goal condition forms the basis for the termination condition of the
production system. The control strategy repeatedly applies rules to state descriptions until
a description of a goal state is produced . it also keep track of rules that have been applied
so that it can compose them into sequence representing the problem solution. A solution
to the 8-puzzle problem is given in the following figure.
Example:- Depth – First – Search traversal and Breadth - First - Search traversal
for 8 – puzzle problem is shown in following diagrams
Tower of Hanoi Problem
Consider the following problem. We have 3 pegs and 3 disks.
Operators: one may move the topmost disk on any needle to the topmost position to any
other needle
In the goal state all the pegs are in the needle B as shown in the figure below..
The initial state is illustrated below
Now we will describe a sequence of actions that can be applied on the initial state.
Step 1: Move A → C
Step 2: Move A → B
Step 3: Move A → C
Step 4: Move B→ A
Step 5: Move C → B
Step 6: Move A → B
Step 7: Move C→ B
Natural language
In the philosophy of language, a natural language (or ordinary language) is any
language which arises in an unpremeditated fashion as the result of the innate facility for
language possessed by the human intellect. A natural language is typically used for
communication, and may be spoken, signed, or written. Natural language is distinguished
from constructed languages and formal languages such as computer-programming
languages or the "languages" used in the study of formal logic, especially mathematical
logic.
A human language. For example, English, French, and Chinese are natural languages.
Computer languages, such as FORTRAN and C, are not.
Probably the single most challenging problem in computer science is to develop
computers that can understand natural languages. So far, the complete solution to this
problem has proved elusive, although a great deal of progress has been made. Fourthgeneration languages are the programming languages closest to natural languages.
Natural language processing
Natural language processing (NLP) is a field of computer science and linguistics
concerned with the interactions between computers and human (natural) languages; it
began as a branch of artificial intelligence.[1] In theory, natural language processing is a
very attractive method of human–computer interaction. Natural language understanding
is sometimes referred to as an AI-complete problem because it seems to require extensive
knowledge about the outside world and the ability to manipulate it.
Whether NLP is distinct from, or identical to, the field of computational linguistics is a
matter of perspective. The Association for Computational Linguistics defines the latter as
focusing on the theoretical aspects of NLP. On the other hand, the open-access journal
"Computational Linguistics", styles itself as "the longest running publication devoted
exclusively to the design and analysis of natural language processing systems"
(Computational Linguistics (Journal))
Modern NLP algorithms are grounded in machine learning, especially statistical machine
learning. Research into modern statistical NLP algorithms requires an understanding of a
number of disparate fields, including linguistics, computer science, and statistics. For a
discussion of the types of algorithms currently used in NLP
Why Study Natural Language Processing?
Natural language processing is the technology for dealing with our most ubiquitous
product: human language, as it appears in emails, web pages, tweets, product descriptions,
newspaper stories, social media, and scientific articles, in thousands of languages and
varieties. In the past decade, successful natural language processing applications have
become part of our everyday experience, from spelling and grammar correction in word
processors to machine translation on the web, from email spam detection to automatic
question answering, from detecting people's opinions about products or services to
extracting appointments from your email. In this class, you'll learn the fundamental
algorithms and mathematical models for human language processing and how you can
use them to solve practical problems in dealing with language data wherever you
encounter it
Visual perception
Visual perception is the ability to interpret information and surroundings from the effects
of visible light reaching the eye. The resulting perception is also known as eyesight, sight,
or vision (adjectival form: visual, optical, or ocular). The various physiological
components involved in vision are referred to collectively as the visual system, and are
the focus of much research in psychology, cognitive science, neuroscience, and molecular
biology.
In order to receive information from the environment we are equipped with sense organs
e.g. eye, ear, nose. Each sense organ is part of a sensory system which receives sensory
inputs
and
transmits
sensory
information
to
the
brain.
A particular problem for psychologists is to explain the process by which the physical
energy received by sense organs forms the basis of perceptual experience. Sensory inputs
are somehow converted into perceptions of desks and computers, flowers and buildings,
cars and planes; into sights, sounds, smells, taste and touch experiences.
Or we can say
Visual perception is a function of our eyes and brain. We see images as a whole rather
then in parts. However, images can be broken down into their visual elements: line, shape,
texture, and color. These elements are to images as grammar is to language. Together
they allow our eyes to see images and our brain to recognize them. In this section, we
will talk about each of these elements except color, because color perception is a big
subject and deserves a section of it own. Therefore we will talk about color perception in
the next section. Here we are concerned with line, shape and form, and texture.
Line
A line is the path made by a pointed instrument, such as a pen, a crayon, or a stick. A line
implies action because work needs to be done to make it. Moreover, the impression of
movement suggests sequence, direction, or force. In other words, a line can be seen as a
distinct series of points.
Line is believed to be the most expressive of the visual elements because of several
reasons. First, it outlines things and the outlines are key to their identity. Most of the time,
we recognize objects or images only from their outlines. Second, line is important
because it is a primary element in writing and drawing, and because writing and drawing
are universal. Third, unlike texture, shape and form, line is unambiguous. We know
exactly when it starts and ends. Finally, line leads our eyes by suggesting direction and
movement.
It is not easy to categorize lines because there are so many aspects to them. One can
group them by using thickness, smoothness or origin. However, for the purpose of art
education and communication, we categorize lines into five groups. There are horizontal
lines which run parallel to the ground (figure A), vertical lines which run up and down
(figure B), diagonal lines which are slanting lines (figure C), zigzag lines which are made
from combining diagonal lines (figure D), and curved lines which do not fall into the first
four categories. Curved lines (figure E) are used to express natural movement.
Shape
Shape is related to line. Closed lines become the boundaries of shapes. The shapes that
artists create are inspired by many different sources, such as nature and man-made
objects. Like with lines, there are many ways of categorizing shapes. We can use their
dimensions, for example, distinguishing between two-dimensional shape and threedimensional shape. Or we can use their style (realism, abstraction, etc), or their origin
(organic or geometric)to classify them.
Geometric shapes look as though they were made with a ruler or a drawing tool. The five
basic geometric shapes are: the square, the circle, the triangle, the rectangle, and the oval.
Organic shapes, which are also called Free Form shapes, are not regular or even. Their
outlines are curved or angular, or a combination of both. However, there is no clear-cut
line to separate the geometric and organic categories. In the figure below, on the left side
is a perfect geometric shape; while on the right side is an organic shape.
Texture
Texture is an element of art that refers to the way things feel, or look as though they
might feel, if touched. For example, sandpaper looks and feels rough; a cotton ball looks
and feels soft. The connection between visual and tactile sensation is very well developed.
The next question is what are the tactile properties of surfaces that enable us to see them.
In the other words, why do we see texture? We see texture because of the light-absorbing
and light-reflecting qualities of materials. These qualities are together represented by
light and dark patterns. The light and dark patterns give us the appearance of texture.
Like the other elements discussed above, texture has been used a lot in art work.
Color perception
Our sensations of colour are within us and colour cannot exist unless there is an
observer to perceive them. Colour does not exist even in the chain of events between the
retinal receptors and the visual cortex, but only when the information is finally
interpreted in the consciousness of the observers
Nature of color
What we perceive as color is primarily the wavelength the light stimulation. The shortest
viewable wavelength (about 380 nm) is what we see as blue and the longest wavelength
(about 760 nm) is what we see as red. The other wavelengths that fall between them are
what we see as other colors, as shown in the figure below. However, color perception is
very subjective. We do not have a way of proving that two different people perceive the
same color, yet we refer to 760-nm wavelength as RED and 380-nm wavelength as
BLUE.
We see color in the objects around us because they absorb most of the wavelengths from
the sun, called white light; and they reflect only a particular wavelength into our eyes.
For example, a red apple absorbs all but the 760-nm wavelength. Therefore, we see it as
red in color. Objects that are white in color are objects that do not absorb any viewable
wavelengths; while objects that are black absorb almost all viewable wavelengths. We
know that the white light from the sun consists of many different wavelengths because of
Newton's prism (shown below). Because of the prism's refraction, the white light is split
into rays, emitting different colors of light, each of which has a different wavelength. The
same phenomenon happens in nature, as we can see in rainbows.