Download 13: The relationship between artificial intelligence and psychological

International Journal of Conceptions on Computing and Information Technology
Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808
The relationship between artificial intelligence and
psychological theories
Pratik and Rahul Abhishek
Dept. of CSE & IT,
Majhighariyani Institute of Technology and Science,
Rayagada, Odisha, India.
[email protected] and [email protected]
Abstract— Psychology is one of the parent elements of artificial
intelligence or we can also say that it is the main source for
artificial intelligence. In this paper we are discussing about the
theories of psychology used in AI. Since psychology is the study
of human brain and its nature and AI is the branch which deals
with the intelligence in machine, so for understanding the
intelligence of a machine we have to compare with human
intelligence because AI means the intelligence shown by a
machine like a human being.
Keywords- Artificial Intelligence (AI), Psychology, Perception,
Learning, Neural network, Cognitive Science, Human Computing
Interaction (HCI).
I. INTRODUCTION
Artificial Intelligence the word coined by John McCarthy
three decade ago now becomes a revolutionary word in the
field of computer science and information technology.
What actually we mean by AI?
According to M.L. Minsky, "Artificial intelligence is the
science of making machines do things that would require
intelligence if done by men." Artificial Intelligence (AI) is a
perfect example of how sometimes science moves more slowly
than we would have predicted. Another representative
definition of AI is pivoted around the comparison of
intelligence of computing machine with human beings. None
of these definitions or the like has been accepted, perhaps
because of their references to the word “intelligence, which at
present is an abstract and immeasurable quantity. A better
definition of artificial intelligence, therefore, calls for
formalization of the term “intelligence”. Psychologist and
Cognitive theorists are of the opinion that intelligence helps in
identifying the right piece of knowledge at the appropriate
instances of decision making. The phrase “artificial
intelligence” thus defined by C Bane as “the simulation of
human intelligence on a machine, so make the machine
efficient to identify and use the right place of knowledge at a
given step of solving a problem
II. PERCEPTION IN AI
The definition of AI is based on the nature of the problems
it tackles, namely those for which humans currently outperform
computers. Perception involves interpreting sights, sounds,
Figure 1. Parent element of Artificial Intelligence
smells and touch. Action includes the ability to negative
through the world and manipulate objects. In perception the
environment is scanned by means of various sensory organs,
real or artificial, and the scene is decomposed into separate
objects in various spatial relationships. Analysis is complicated
by the fact that an object may appear different depending on
the angle from which it is viewed, the direction and intensity of
illumination in the scene, and how much the object contrasts
with the surrounding field.
The difficulty of implementing analogues of human
perception has been underestimated by AI researchers. At
present, artificial perception is sufficiently well advanced to
enable optical sensors to identify individuals, autonomous
vehicles to drive at moderate speeds on the open road, and
robots to roam through buildings collecting empty soda cans.
One of the earliest systems to integrate perception and action
was FREDDY, a stationary robot with a moving television eye
and a pincer hand, constructed at the University of Edinburgh,
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International Journal of Conceptions on Computing and Information Technology
Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808
Scotland, during the period 1966–73 under the direction of
Donald Michie. FREDDY was able to recognize a variety of
objects and could be instructed to assemble simple artifacts,
such as a toy car, from a random heap of components.
There are basically two approaches for perception
i.
Feature Extraction -
It detects some small number of features in sensory input
and passes them to their agent programand this agent program
will combine features with other information
ii.
Model Based -
Sensory stimulus is used to reconstruct a model of the
world it start with a function that maps from a state of the
world to a stimulus.
In reality, both feature extraction and model-based
approaches are needed.
The recent extraordinary growth of artificial intelligence
and its application mainly in the development of expert
systems, practical implementations of natural languageunderstanding systems, significant advances in computer vision
and speech understanding. According to psychology Learning
can be defined as the process leading to relatively permanent
behavioural change or potential behavioural change and this
definition also fits on machines. The experimental programs
developed in the course of machine learning research, current
AI systems have very less learning ability or we can say none
at all. All of their knowledge must be programmed into them.
When they contain an error, they cannot correct it on their own;
they will repeat it again and again. They can neither improve
gradually with experience nor learn domain knowledge by
experimentation. They cannot automatically generate their
algorithm, formulate new abstractions, or develop new
solutions by drawing analogies to old ones, or through
discovery and when we look at human intelligence we see that
among its most striking aspects is the ability to acquire new
knowledge, and to improve with practice. The ability to learn
from error is considered fundamental to the individual. So
learning ability is so intimately entwined with intelligence
behaviour research in AI gives us new insights and powerful
tools to study it, the new central goals for research in artificial
intelligence should be understand the nature of learning and
implementing learning capabilities in machines.
According to psychology a human learn by his experiences
in daily life.
IV.
HOW A HUMAN BRAIN LEARNS?
The learning process in human is done through nervous
system this nervous system consists of neuron. These neurons
collect signals with the help of dendrites; these tiny protrusions
receive information from other neurons and transmit electrical
stimulation to the soma. The neuron sends out spikes of
electrical activity through a long, thin stand known as an axon,
which splits into thousands of branches. At the end of each
branch, a structure called a synapse converts the activity from
the axon into electrical effects that inhibit or excite activity
from the axon into electrical effects that inhibit or excite
activity in the connected neurons. When neuron receive
sufficient amount of input then it sends an electrical signal.
Learning occurs by changing the effectiveness of the synapses
so that the influence of one neuron on another changes.
Figure 2. Road map of computer vision
III. LEARNING IN AI
There are a number of different forms of learning as applied
to artificial intelligence. The simplest is learning by trial and
error. For example, a simple computer program for solving
mate-in-one chess problems might try moves at random until
mate is found. The program might then store the solution with
the position so that the next time the computer encountered the
same position it would recall the solution. This simple
memorizing of individual items and procedures is known as
rote learning.
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Figure 3. Structure of neuron
International Journal of Conceptions on Computing and Information Technology
Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808
V. MACHINE LEARNING THOUGH ARTIFICIAL NEURON
An artificial neuron is a computational model inspired in
the natural neurons. Natural neurons receive signals through
synapses located on the dendrites or membrane of the neuron.
When the signals received are strong enough (surpass a certain
threshold), the neuron is activated and emits a signal though the
axon. This signal might be sent to another synapse, and might
activate other neurons.
The complexity of real neurons is highly abstracted when
modeling artificial neurons. These basically consist of inputs
(like synapses), which are multiplied by weights (strength of
the respective signals), and then computed by a mathematical
function which determines the activation of the neuron.
Another function (which may be the identity) computes the
output of the artificial neuron (sometimes in dependence of a
certain threshold). ANNs combine artificial neurons in order to
process information.
The information processing model of intelligence itself has
been a stimulant to psychology. Cognitive psychology and AI
share many metaphors. Clowes discusses the relationship
between artificial intelligence and psychology by considering
as an examine problem which is one of the central problem of
AI: computer vision.
The brain is an information processing device. AI supports
also this idea. The fig 5 illustrates the information processing
model of the brain.
In the human brain there are roughly 20 billion neurons (the
number depends on various factors, including age and gender).
Each neuron will be connected through synapses to roughly
10,000 other neurons. There's no way we can mimic 20 billion
neurons with 10,000 connections each, but there are several
interesting things we can do with much less firepower.
Way back in 1957, Frank Rosenblatt modeled a single
neuron with something he called a 'perceptron', and used it to
investigate pattern recognition. Unfortunately, the perceptron
was unable to recognize even simple functions like XOR
(proved formally by Marvin Minsky and Seymour Papert in
1969) and so it was abandoned in favor of something called
multilayer feed forward networks.
Figure 5. Information Processing Model of the Human Brain
a. Stimulus has to be interpreted to representation
b. Representation is manipulated via cognitive processes, and
builds new inside representation
c. Process may end in an action.
According to Craik (scientists dealing with knowledge
based agents, and died in an unfortunate accident) the organism
contains a possible small model of the outer world, and the
possible actions, is also able to try different alternatives, and
decide by the best, react before an expected future happens, or
to analyze the consequences of the past, and react the most
competent and safe way on a situation. (Craik, 1943).
Figure 4. Artificial neural network
VI. COGNITIVE SCIENCE AND ARTIFICAL INTELLIGENCE
How can we connect artificial intelligence with cognitive
psychology? What kind of models and approaches were
developed in these scientific fields? AI is more and more
evolving into a science of intelligence, and a report of work on
computer semantics make clear that there is a great deal of
emphasis on how the mind might, if not does, go about doing
some of the intelligence tasks involved in resolving meaning.
We may come in a contact with Human Computing
Interaction (HCI) every day, because this field includes the
everyday use of computer, the user interfaces and expert
programs which may use cognitive psychology in order to
manipulate or help people. It is important in the HCI to
understand the goals, intention of the user the problem solving
ability (with psychology), to understand the interaction
(sociology), and to understand the physical ability of the users
(ergonomic), to develop useful interface (graphical design), and
to develop a system (computer science).
We can find HCI application in virtual reality and virtual
environment.
Virtual Reality is a new way of the human machine
communication, which enables an interaction connecting to
human senses.
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International Journal of Conceptions on Computing and Information Technology
Vol. 1, Issue. 1, November 2013; ISSN: 2345 – 9808
Virtual Environment is an important part of Virtual Reality,
because more and more people connect to them. In these
artificial environments (for example: games), human like
attributes are relevant. Technologies of Artificial Intelligence
provide a basis for the dream of the virtual reality.
they know far more about Artificial Intelligence than they
actually do.
VII. CONCLUSION
The paper provide sort but broad summery about the
psychological theories which is related to artificial intelligence.
The area of AI and psychology is dynamic it is changing day
by day. The field of AI has a reputation for making huge
promises and then failing to deliver on them. The role of
perception and action in current AI systems has been analyzed.
Learning techniques are discussed with comparison of human
learning process and artificial learning process. It is difficult to
build a star from hydrogen, but the field of stellar astronomy
does not have a terrible reputation for promising to build stars
and then failing. The critical inference is not that AI is hard,
but that, for some reason, it is very easy for people to think
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