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JOURNAL OF APPLIED
COMPUTER SCIENCE
Vol. 20 No. 1 (2012), pp. 23-33
Knowledge and Artificial Intelligence in Virtual
Environment
Edward Kacki
˛
The College of Computer Science
Department of Expert Systems and Artificial Intelligence
ul. Rzgowska 17a, 93-008 Łódź
ekacki@ics.p.lodz.pl
Abstract. The paper based on the definition of knowledge accepted in computer science proposes very simple concepts for measuring knowledge and
its saturation level with artificial intelligence in virtual environment. The
presented method based on cybernetic definition of algorithms takes three
levels of algorithms into consideration adequate to elementary, secondary
or university education of users in frame of defined specialization. It can
be very useful and easy in application for knowledge in algorithms form
and therefore it deals with knowledge in virtual environment. The paper also
prerequisites for the origin of artificial intelligence and virtual knowledge
and points out difficulties encountered when attempts to introduce scales
and measures in this area are undertaken. In view of a tremendous variety
of ways in which the artificial intelligence and virtual knowledge manifest
themselves and a multiplicity of methods in which they can be represented,
their measures and scales can only be of fuzzy character and thus must be
burdened with imprecision.
Keywords: Artificial Intelligence, Knowledge, Measure, Virtual Environment.
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Knowledge and Artificial Intelligence in Virtual Environment
1. Introduction
The rapid development of information societies with automation of information processing becoming a common and widespread phenomenon, in our various
activities we frequently find it necessary to make use of knowledge bases. The rational exploitation of knowledge resources stored on disks or dispersed over huge
computer networks accompanied by growing easiness in access to them enable
efficient decision-making in different fields of activity [1, 2]. More and more frequently nowadays one faces the need not only to estimate and compare largeness
of knowledge and its saturation level with artificial intelligence contained in available resources but also to automate estimation processes, which is impossible unless measures with appropriate scales are introduced. The present article aims at
indicating essential issues associated with problems of these types. Moreover, its
goal is also to present certain solutions concerning the question of artificial intelligence levels and to discuss them on the background of changes in perception of the
essence of natural intelligence, i.e. against the methods of defining and evaluating
intelligence by philosophers and other scholars [3].
2. From natural to artificial intelligence
Psychologists, philosophers or educationalists have defined the intelligence in
many different ways, although generally the same purpose underlines all formulated definitions. This purpose is determination of the level of mental capabilities
of individual people, though a wider class of living creatures may be referred to as
well.
Usually, the definitions of intelligence are based on a commonly shared understanding of the sense of this word. Intelligence is thus the ability of gaining
knowledge about the reality and then using it in life with the capacity of adapting
oneself to new tasks and new living conditions. Intelligence is considered to be
the most valuable capability of the human mind. It is not difficult to notice how
diverse in meaning the above definition of intelligence is, and consequently how
imperfect it can be considered. There have been efforts to give this notion a more
precise form but no success has been achieved so far.
Intelligence can be either developed or primitive, like its forms observed in the
behaviour of animals. An intelligent adult may have primary, secondary or higher
E. Kacki
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education or no schooling at all. A child can be said to be intelligent independently
of their age or social background.
We often say that someone is more or less intelligent but despite tests whose
results fulfill their practical role we actually do not have any precise criteria to
evaluate the degree of intelligence.
Before we discuss a few different ways of defining intelligence by scholars
working in the last two centuries, let us go back to the more remote times, since
already in the ancient times philosophers would devote much attention to mental
abilities of man. What should be mentioned here are the ideas of Plato’s and of
his disciple’s, Aristotle, concerning mind viewed by them as the whole of knowledge, experience and mental capabilities of man. This is exactly what presently is
referred to as intellect and frequently identified with intelligence.
For Plato, the whole of the mental capabilities of man constitutes means for
idea cognition as an expression of relation of the soul to the idea, whereas in Aristotle’s formulation it is the power of the rational soul endowed with abstraction
ability.
Most philosophers of the middle Ages, like Gilbert de la Porree, Avicenna or
Saint Thomas Aquinas, dealt with capabilities of the human mind, and in their
considerations they were, to a great extent, inspired by Aristotle’s concepts.
Associating intelligence with abstraction capacity plays a considerable role in
the contemporary understanding of this notion. The term “intelligence”, naming
a new concept subject to shaping for years, goes back to the first century BC to
the word “intelligentia” used by Cicero, the inventor of the Latin philosophical
terminology. A considerable part of works devoted to the question of intelligence
and to a definition of it has appeared in the last 300 years. Among them, there is
some of essential significance, which needs to be mentioned here.
Living in the years 1723-1816, Scottish philosopher Adam Ferguson defined
intelligence as an ability to learn, and consequently his followers and representatives of this approach claimed that the intelligence level depended on the degree of
difficulty characterizing the material subject to learning, on the speed of processing the material, and on the complication and variety of the required repertoire of
behaviors. The notion of intelligence defined in this way may be related to most
animals as well.
W. Stern, a German philosopher who lived in the years 1871-1938, known for
his concept of intelligence quotient to be used to determine the level of intellectual
capacity of people, as well as for his general intelligence theory, devoted much
attention to the investigation into development of speech and reasoning in children
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Knowledge and Artificial Intelligence in Virtual Environment
and young people. Intelligence, as defined by him, is an ability to adapt oneself to
new tasks and new living conditions.
Of essential importance for the research into the capabilities of human mind are
works deriving from the factorial description of intelligence. The forerunner of this
approach is C. E. Spearman (1863-1945). His two-factorial theory of intelligence
distinguishes a general factor indispensable in solving cognitive tasks of all types,
and a specific factor involved in solving tasks of a particular type, which makes
a person more skilled at a cognitive task of this particular type than at another.
Thus human intelligence as defined by C. E. Spearman at the beginning of the
20th century is reduced to the factors mentioned above occurring in all human
behaviours involving mental processes such as reasoning and inference.
C. E. Spearman’s theory of intelligence based on factor analysis was further
developed by C. Burt and G. Thomson in Scotland, and then by T. L. Kelley, K. J.
Holzinger and L. L. Thurstone in the United States.
In 1949, C. Burt published his conception of hierarchical structure of intelligence in which, like in earlier works by P. E. Vernon, particular factors are assigned
different levels.
Among hierarchical factorial models of intelligence a significant position is
taken by a concept published in 1971 by an American psychologist – R. Cattel. In
his theory, Cattel distinguishes three factors which he names fluid, crystallized,
and instrumental intelligence. The central role is assigned to fluid intelligence
defined as general abilities taking part in mental tasks of all kinds. Crystallised
intelligence is acquired through learning and experience, whereas instrumental intelligence encompasses so-called primary abilities, like for example word fluency,
number operations, reasoning, and memory. In opposition to the hierarchical theory, the theory of equivalent factors of intelligence was created. After expressing
a critical opinion about Spearman’s conception, in 1938 American psychologist T.
L. Kelley published his own intelligence theory based on five equivalent factors
standing for verbal, numerical, and special abilities together with memory, and
perceptual speed.
Created by T. L. Kelley, the theory was further developed by Thurstone, who
added word fluency and reasoning to the five factors listed above. These seven
distinct and equivalent factors were treated as functional units relating to different
aspects of cognitive behaviour.
The factorial theory of intelligence had a significant impact on the development
of methods for intelligence level testing.
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Russian psychologist and philosopher S. L. Rubinstein examined problems
connected with growth of consciousness and with cognitive processes, treating
thinking as interaction between a subject carrying out studies and an object undergoing the studies. The most essential of all the mental abilities of which intelligence is composed is, according to him, the ability to analyze and synthesize
relations occurring in the domain to which a given ability relates. A theory of intelligence that is also worth mentioning is the one built by J. Piaget, a Swiss biologist
and psychologist, for whom the point of departure was the assumption that intelligence was an advanced form of biological adaptation. Piaget believed that the
development of intelligence consists, above all, in shaping new cognitive schemes
and is of qualitative character. Consequently, intelligence in his theory was treated
as a dynamic phenomenon.
The works of two great scientists, J. B. Watson’s - who established the psychological school of behaviorism, and N. Wiener’s - known as the father of cybernetics
[3], laid the foundation for the concept of artificial intelligence. The need for this
new notion stemmed from an avalanche development of intelligent behaviour algorithms and implementation of them in the software for computers, robots, and
systems of adaptive control of processes of different types that took place in the
20th century. In consequence, artificial intelligence is associated only with a particular way in which a given system behaves no matter what kind of equipment or
what nature of energy change is involved in this behavior. Relating a definition or
rules only to behaviour has its roots in the philosophical and psychological trend
originated by J. B. Watson. It should be remembered, however, that behaviorism
was built basing on earlier works of American zoologists J. Loeb, M. R. Yerkes,
and E. Throndike as well as on the results obtained by Russian scientists, namely
I. Pavlov – the originator of the reflex theory, and W. Bechterev – a physiologist
who was concerned with reflexology and to whom we owe this term.
Behaviorism reduced psychological research to an analysis of perceptible behaviors of man or animal only while abstracting entirely from the phenomena of
consciousness. In the result, in that approach behaviour is viewed as a set of adaptive reactions and physiological changes by means of which an organism responds
to stimuli reaching it from the environment.
Created by Norbert Wiener, cybernetics – a general science dealing with the
theory of adaptive control, is the second important factor that paved the way for
the concept of artificial intelligence. The origin of cybernetics is associated with
the publication of Wiener’s “Control and Communication in the Animal and the
Machine” in 1948. This work gives a very general view on the control process
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Knowledge and Artificial Intelligence in Virtual Environment
independently of whether it concerns phenomena taking place in machines, living
organisms or economic systems. It concerns almost every activity with feedback
in which three elements repeat cyclically, namely identification, decision-making,
and decision implementation. At present, artificial intelligence constitutes one of
the fundamental concepts of cybernetics. Artificial intelligence is widely used
nowadays in the world and Polish scientific literature. What is therefore meant
by artificial intelligence?
There are a few definitions of this notion which are not fully equivalent. Each
of them, however, is sufficiently good to be employed to solving both theoretical
as well as practical problems.
Two of these definitions will be quoted here. The first one is quite simple, and
formulated in a clear and intelligible way: ”By artificial intelligence this property
of a system is meant which allows statement that the system behaves as an intelligent being.” The other definition refers to the notion of algorithm and says:
“Artificial intelligence is a scientific domain concerned with algorithms of intelligent behaviours.”
Artificial intelligence algorithms are formalized descriptions of mental processes taking place when everyday problems as well as those encountered in professional, artistic or scientific activity need to be solved.
The aim of constructing these algorithms is to enable automation [3] of the
considered intellectual processes. Consequently, it is required that the elementary
components of artificial intelligence algorithms be computer-feasible operations
[4]. For more complex AI algorithms, their construction is preceded by the building
of a mathematical model of the entire process constituting a solution of a given task
or its parts.
3. Measure of knowledge and its saturation with artificial
intelligence
Knowledge is the total contents recorded in the mind of an individual acquired
through accumulation of one’s experience and learning.
The above is a general definition of knowledge where data subjected to traditional forms of information processing are distinguished as sets of numbers or
verbal descriptions of facts (events). Thus the data in this definition can be viewed
as coded images or photographs which provide answers to the questions like: How
many are there? What is this? What is it like? This definition differs quite signifi-
E. Kacki
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cantly from the one accepted in computer science. Using the definition describing
a system as an ordered pair {X, R}, where X is a set of elements and R is a set of relations between the elements selected so that the system fulfils the tasks for which
it has been created, we define knowledge in terms of computer science as follows.
Data together with relations between them create a system which can be treated as
knowledge [5, 3] if it is able to provide an answer to at least one of the questions
below:
• How can something be done?
• How to attain a given goal?
• Why is the system’s behavior of the nature it actually is and not of different
one, and what consequences does it have?
Consequently, this way of reasoning allows the statement that knowledge resources in the information sense consist mainly of operational algorithms, which
may be presented at user’s request, for instance by information systems contained
in computer networks, where by an algorithm we mean a sequence of well defined
operations ordered so that their execution leads to the achievement of a desired
target.
The notion of algorithm defined in this way is one of the basic concepts of cybernetics [3, 6], i.e. of the general theory of control and communication. The term
“well-defined operation” in the definition is not univocal and therefore needs additional comment. The meaning of this phrase is connected with both a prescribed
goal of action and means that are available to reach the goal. Consequently, before
an algorithm is constructed both the goal and the conditions (means) under which
actions leading to the goal can be performed should be formulated. By “simple
operations” we mean operations that, under assumed conditions, are (easily) performable by a performer, i.e. by a particular individual or a given machine. Therefore the meaning of the term “simple operation” depends on the level of the performer for whom the considered algorithm has been built as well as on the means
that are available to the performer.
In the general case, the sequence of operations can be either finite or infinite.
For an infinite operation sequence closeness to a chosen goal can be a focus of
consideration. This usually leads to the question of approximation error estimation,
which requires a norm, metrics or limit to be defined in the examined space.
In practical problems, algorithms that are feasible in a finite number of steps
are usually considered.
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Knowledge and Artificial Intelligence in Virtual Environment
Figure 1. Diagram of an algorithms
In the general case, the sequence of operations can be either finite or infinite.
For an infinite operation sequence closeness to a chosen goal can be a focus of
consideration. This usually leads to the question of approximation error estimation,
which requires a norm, metrics or limit to be defined in the examined space.
In practical problems, algorithms that are feasible in a finite number of steps
are usually considered.
On the 1 is shown a fragment of an algorithm diagram with 7 blocks of the
unconditional operations {S 1, S 2, S 3, S 4, S 5} and 2 blocks of the conditional operations {C1, C2}. Each block of type S has only one output and each block of
type C has n > 1 outputs. We assume that algorithm having only unconditional
operations (simple actions) is deprived of artificial intelligence and conditional
operations are the carriers of artificial intelligence.
The principal aim of this chapter is to present a preparatory concept for estimation of knowledge measure being given in algorithms form and next for estimation
of the level of artificial intelligence contained in the algorithm or the set of algorithms. We consider the set P of N algorithms A(1), A(2), . . . , A(k), . . . , A(N). The
following formula defines the measure M of the knowledge represented by set P:
E. Kacki
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Figure 2. Algorithms containing a counter of repetitions
M = Nu + Nc
(1)
for
Nu =
k=N
X
U(k),
(2)
k=1
Nc =
j=nk
k=N
XX
C(k, j) .
(3)
k=1 j=1
where: Uk – the number of unconditional operation blocks in the algorithm A(k),
C(k, j) – the number of outputs of conditional operation j in the algoritm A(k). The
level p of saturation with artificial intelligence in the considered algorithm set is
defined by following formula
Nc
100% .
(4)
M
For instance, for the algorithm fragment shown on 1 we have Nu = 7, Nc = 5,
M = 12 and p 62% and for the algorithm fragments to be shown on 2 we have
Nu = 3, Nc = 2, M = 5 and p 40% in each case (A or B).
The presented procedure is very easy and simple in application but a difficulties may arise in the case of distributed knowledge and artificial intelligence
gathered in artificial neural network. In this situation we distinguish two options:
p=
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Knowledge and Artificial Intelligence in Virtual Environment
1) the preparation algorithm of the learning set for the artificial neural network is
known and we have the use of presented method; 2) the learning algorithm and
the preparation algorithm of the learning set isn’t known and in this case we are
dealing with phenomenon similar to that in the case of biological neural net works,
where knowledge and artificial intelligence are distributed in whole network as in
biological neural network. In this case we use the method presented in the paper
[7] similar to the method used by Binet-Simon scale [3] application for measure
of natural intelligence knowing that the natural intelligence has been defined in
many different ways by psychologists, philosophers or educationalists, although
generally the same purpose underlines all formulated definitions.
4. Final remarks and conclusions
Presented in the paper the method of estimation of largeness of gathered knowledge and its saturation with atificial intelligence deals with only that parts of algorithms which includes the substantive text. It can’t be applicated to the parts of
algorithms, which aim is to increase the clearness of communication and impact
power. Therefore it is worth to pay attention to very extensive use of multimedia [8, 9, 7, 6] and virtual reality [10, 11] in computer systems, so that systems
become user friendly, bringing immense benefits to the various human activities,
including technology, medicine, economics, creative arts, science and education,
politics, military, etc., in its various aspects. And that includes multimedia sound,
light, moving images in natural colors with spoken commentary and background
music make the information transmitted by the computer can take the form of a
very communicative dialogue, and virtual reality [4, 5] software system allows
the user the illusion of movement and residence in nonexistent three dimensional
world.
The evaluation of clearness level of communication and of impact power level
for programmatic algorithm implementations institutes a separate problem of investigation being to ahigh degree connected with suitable application of multimedia, wirtual reality and artificial intelligence. The presented method of estimation
of largeness of gathered knowledge and its saturation with atificial intelligence can
be use with successful to the books containing whole substance given in algorithmic form, as is the case, for example in the book [12].
According to conception presented in the work, the knowledge in virtual environment expressed by algorithm without any conditional block is depresived of
artificial intelligence.
E. Kacki
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˛
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˛
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