Islamization of Attitudes and Practics in Science and Technology

edited by M.A.K Lodhi
IIIT and II Publishing House

Islamic Perspectives on Knowledge Engineering
S. Imtiaz Ahmad

Abstract

This paper provides Islamic perspectives on the recent developments in
knowledge engineering. The paper starts by introducing the concepts
relevant to knowledge engineering. The use of the science of knowledge,
and the scientific method for knowledge based systems is described and
discussed. The role of knowledge engineering is defined. Relationship
of knowledge engineering to the study of human mental faculties is
described in intelligence and psychology. Finally, a brief description
of the historical developments and current trends is presented.

I. Introduction
The purpose of this research is to find answers to some basic questions
regarding the human mental processes that occur in dealing with
knowledge, and the manner in which this knowledge is organized for
innovation and problem solving.

In reading the Qur'an and literature on Hadlth of the Prophet, the
basic sources of information on Islam, one finds a great deal of
emphasis on knowledge and the special position given to those with
knowledge. One, therefore, wonders as to why the Muslims, professing
Islam, do not appear to be the leaders in knowledge. In this case
however it is not only necessary to understand why that may be the case
but also to search for ways to correct this situation.

Research in human problem solving has resulted in paradigms about the
working of the human mind. Advances in computers and the use of
computers in problem solving, in particular the use of the techniques
based on artificial intelligence, are having further impact on these
paradigms. It is to be noted that while these paradigms may give a
rational explanation to what we perceive through our senses, they do
not necessarily represent the ultimate reality of the human mind.

Knowledge comes from learning about things, and it requires mental
apprehension or cognition. The process of learning consists of using
existing knowledge, gaining new knowledge, organizing, and storing the
new and old knowledge. Stored knowledge is recalled and used in
responding to the events in the environment. Our present understanding
of how the human mind organizes knowledge points to the following two
characteristics: what the mind stores is a refined form of received
information, and it also retains the context of this information.

The main topic of the Qur'an is also the human mind. In particular, it
deals with the question of how the humans do use or should use their
mind in responding to the events in the environment. Based on our
current understanding, it appears that the style of Qur'anic
descriptions is well suited to human cognitive skills, e.g.,

• Details are relevant to a context.
• Reasoning is goal oriented.
• The same subject is presented using several alternate perspectives.
• The message is conveyed through patterns of things or parables,
called amthal-ul Quran.
• Positive as well as contrary-to-positive templates of behavior,
Mujibat al Falah and Mujibat al Khusran, are presented.

Phillip Selznick, in his book, Leadership and Administration, argues
(15) that the human values are not usually transmitted through formal
written procedures. They are more often diffused by softer means:
specifically the stories, myths, legends, and metaphors that we have
already seen. This argument is based on the impact on human mind
produced by a certain style of stating facts, and it conforms well to
the cognitive skills stated above.
Some examples of the concepts and terms that the Qur'an uses about
knowledge are:

• Being aware of, or having cognizance of (dirayah).
• Insight, literary and spiritual.
• Reasoning and rationalizing with facts and rules (Qur'an 29:49).
• Contrary to guesswork or conjecture (Qur'an 7:7).
• Learning and discovering truth (Qur'an 22:54).

Knowledge engineering deals with building systems based on the
knowledge of someone who is well experienced in dealing with the events
of some domain of application. Events generate stimuli and require
responses. One must properly recognize the event, and then generate an
appropriate response; this process is called problem solving. In
solving a problem, one must have access to what may be previously
known, retrieve relevant data and rules, and apply this knowledge
effectively. This may be described as a goal seeking process. Given
some premise and some desired goal, one may use the knowledge to move
forward from the premise toward the desired goal until that goal is
reached. Alternatively, one may use the knowledge to move backward from
the goal to the premise in order to establish that the goal can be
reached from the premise. In either case, one goes through several
intermediate steps, and collectively these steps constitute a chain of
thought. Reasoning from the premise to the goal is called forward
chaining, and reasoning from goal to the premise is called backward
chaining; the choice generally depends on the situation.

Many people do not possess the knowledge that may be required to solve
problems in a given area, or they are unable to use effectively the
knowledge that they have. Those who do, are known as experts. It takes
a human being many years, possibly decades, to become an expert in some
area. Direct use of an expert's knowledge is limited by the
possibilities of personal contacts. However, if one can acquire
successfully the knowledge of how an expert solves problems then it can
be put to widespread use. Furthermore, if one can successfully transfer
the expert's knowledge to a machine, then the access and use of this
knowledge can be increased manifold. Moreover, one is now able to
exploit the inherent capabilities of the machine to store vast amounts
of information, recall it when needed, and put it to use at lightning
speed.

II. Knowledge Engine
A mechanism for storing and organizing facts and rules from known
situations, and using them for resolution of new situations is called a
knowledge engine. Designed properly, a knowledge engine can unleash the
problem solving power contained in knowledge. Traditionally, the human
mind has served as the knowledge engine. It is fueled by the stimuli
from the environment, uses existing knowledge to process information,
solves problems, acquires new knowledge in the process, organizes and
updates existing knowledge, and generates information leading to the
creation of new knowledge.

Before the industrial revolution, tools for enhancing the mechanical
abilities of humans were rather limited. With the industrial revolution
came the steam, oil, electric, hydro, and nuclear powered engines which
allowed the human race to alter the physical environment for its
purpose. The changes affected the quality of life dramatically. With
powered machines, it became unnecessary for human beings to exert their
body to lift heavy leads, walk long distances, or endure harsh
climates. Moreover, the power of one such engine could out perform a
large number of human beings.

The power machines of the industrial revolution gave vast amounts of
physical power to the human beings for their use. However, in order to
keep up with these machines, the human beings were often required to
perform repetitive tasks. Also, as the power and operational
capabilities of the machines were increased, they replaced an
increasing number of human beings in the work environment.

In a similar fashion, knowledge engines, also called information
machines in a more general sense, are bringing another kind of
revolution. One may call it an information revolution. A mechanical
machine can easily exceed the physical capacity of hundreds of human
beings, particularly for tasks that do not require physical dexterity.
An information machine, likewise, can exceed the mental capacity of
hundreds of human beings for tasks that do not require mental
adroitness. Furthermore, a human being can make the information machine
to act as an intelligent assistant in his work, allowing him to be more
productive mentally, much the same way that the mechanical machine can
make him more productive physically (1, 16).

III. Of Mind and Machines
A human being has physical faculties of force and motion, sensory
faculties of seeing, hearing, touching and smelling, as well as the
mental faculties. Examples of mental faculties are perceiving on
seeing, discerning on hearing, and reasoning with facts and rules.
Other sensory faculties such as the touch and smell also produce
messages to be appropriately processed by the mental faculties. The
information processing machine has the potential to enhance one's
mental faculties. Those who use these machines can enhance their mental
faculties, produce more goods and exert greater power and control in
society. Unless the overall opportunities grow at a faster rate, the
potential for others will continue to diminish. Examples of these are
given below in items a to d.

An information machine consists of a computer, a knowledge base of data
and models, and a mechanism for controlling the operations. Control of
operations includes the selection and application of data and models
relevant to a situation. There is indeed no doubt that the information
machine, when used properly, serves to increase human productivity. The
machine helps to organize and store the information needed to generate
responses to events in the environment, select required information
from thousands of stored pages, and scan for specific items from
hundreds of pages in a matter of seconds.

Machines have a potential for benefit as well as detriment. Benefits
come from one's ability to enhance the mental processes. Detriment lies
in letting the machine take over one's normal mental processes. With
proper use, an information machine may be visualized as a mind
expander. Because of the unlimited potential that these machines offer,
uses and abuses of information technology are likely to be far more
profound than those brought about by the industrial revolution.
Consider the following examples:

a . Planning, control, and review of complex business and government
enterprises requires vast amounts of rapidly accessible information.
Those who have information technology can run the enterprise
productively, increase their ability to produce more, enhance quality,
and use less resources. They can, thus, dominate their competitors
quantitatively, qualitatively, and intellectually.

b. An architect, an engineer, or an accountant may be able to do high
quality work better than ten architects, engineers, or accountants,
respectively by using the information technology. The other nine
architects, engineers, or accountants thus replaced must adapt or be
eliminated.

c. The rapid changes occurring in information technology create rapid
obsolescence, and new learning requirements. There may be many who are
not educated enough to adapt to this rapid change.

d . Products of information technology which successfully model human
mental faculties may gradually take over the work in many areas of
human services, with possible intimidating situations.

Each example points to the benefits for those who can make timely and
effective use of information technology. However, the same technology
becomes detrimental to those who are unable or unwilling to deal with
it effectively.

IV Knowledge Engineering Concepts
The basic material of knowledge engineering is information, in raw and
refined form. This information consists of descriptions of object types
covering their explicit and implicit attributes and instances.
Alternatively, we may say that an object type is described by some
`relevant' attribute names, whereas a specific instance of an object
defines attribute values. Frequently, one may use the term object to
refer to object name, attributes to refer to attribute names, and
values to refer to instances.

As an example, consider a patient in a hospital. Here, one of the
object types is patient, and the other is hospital. For the patient,
the relevant attributes may be name age, symptoms, history, etc. A
specific instance may be John Adams, 33, fever, none, etc. The choice
of attributes for an object depends on what information needs to be
represented. Furthermore, one must consider how this information should
be structured to properly satisfy the requirements of the applications
dealing with the objects. The extent to which an object is described
depends both on our understanding about the object and the assumed
context of the application.

The process of abstracting the attributes of an object may be
difficult, particularly in the absence of any prior experience with it.
Furthermore, the notions of how an object type should be described may
change with time due to changes in our understanding about the object,
or changes in the application context. One must also consider ways of
collecting attribute values, and keeping them current, possibly
maintaining a history of these values. For example, the current value
of weight attribute may be relevant for a patient but so may be the
previous history of the weight values, Le, how the weight has been
changing between checkups. It is not always possible to define
precisely what attributes may describe an object adequately unless one
is an expert on it. Previous knowledge about the object can be quite
helpful in making the right choice.

Consider the first lesson in knowledge engineering given to Adam by
God, and described in the Qur'an as: "And taught Adam the names of
things." [Qur'an 2:31-33.]. It appears that in this lesson a process of
synthesis of knowledge was in the making: patterns were in motion, and
recognition was in action. This was the first phenomena that involved
the human mind in abstracting the attributes and assigning names to
things based on those perceived attributes. The Qur'an describes
knowledge and the principles and tools of knowledge engineering as:

• Ilm al-Yaqin [Qur'an 102:5], certainty or knowledge gained from
reasoning and inference.
• Ayn al-Yaqin [Qur'an 102:7] , certainty or knowledge gained from
sight (from the senses), and
• Haqq al-Yaqin [Qur'an 69:51], certainty or knowledge that is absolute
in truth, not subject to alternation from knowledge received through
sense perceptions, reasoning, or inference.

The first two items are related to the knowledge that is acquired, and
the third item points to the knowledge revealed to mankind through the
ages.

Description of an object is not simply as to what it is, but also what
capabilities it may have. The capabilities describe the operations the
object permits, as well as those it can perform, resource requirements,
and constraints. An object may be manipulated by some objects, and it
may manipulate some of them. The extent of an object description, and
the ability to acquire instances of this description, determine the
scope of the responses which may be generated when events related to
the object occur in the application environment. All these
considerations are relevant to engineering useful knowledge about an
object.

The product of knowledge engineering is a system consisting of a
knowledge base structure, an interface for knowledge acquisition and
user queries, and a mechanism for activating the knowledge base in
order to generate responses all residing in a special or general
purpose computer. Knowledge engineering deals with the concepts, tools,
and techniques for describing the objects, structuring the description
for acquiring and maintaining information, and developing mechanisms
for sequencing of the operations [6, 7, 18, 21]. It also deals with the
mechanisms for creating, mutating, and deleting the objects. The
processor in the computer provides the raw power, fueled by the data
and logic components of the knowledge base, to work as a knowledge
engine.

Speaking broadly, and sounding somewhat futuristic, one may define the
goals of knowledge engineering as:

• Creating intellect from knowledge, i.e., creating a machine that
could reason as a philosopher, offering new insights into historical
and contemporary events.
• Creating mind inside matter, i.e., creating a machine capable of
independent thought.

We will elaborate these goals further in the sections that follow.

V Role of Knowledge Engineer
A knowledge engineer is responsible for creating a mirror image of a
particular reality, i.e., creating an authentic model of what exists in
the application domain. This work requires discovery of what the
reality is or how it is perceived, developing a representation
consistent with the events and responses in the real world, and
maintaining the integrity of the representation. In order to perform
this task, the knowledge engineer must understand the science of
knowledge, and the manner of its application.

Knowledge comes through observations, reasoning, and reflection. There
are two categories of knowledge- axiomatic and empirical. Axiomatic
knowledge deals with the possibility of possible things, and the
impossibility of impossible things. Given an event, and axiomatic
knowledge about it, one may describe a definite response. Empirical
knowledge, on the other hand deals with observation and
experimentation. Given an event, and only empirical knowledge about it,
one may develop a response based on experience.

In performing an analysis of the situation which is to be modeled, the
knowledge engineer is required to use all sources of information, to
discern specifics of the application domain, and to describe the
knowledge thus gained. Generally, the knowledge engineer, or in this
phase of the work one may call him the knowledge analyst, is not the
creator or user of the knowledge in the application domain. He must
refer to those who can validate his knowledge of the application
domain. This requires tools and techniques of communication, and their
use in a manner which encourages the vocalization of pertinent
information. The purpose of the validation process is to remove
ignorance about the application domain, and generate the knowledge for
modeling and representation of the reality.

Systems built on ignorance about the domain of application either fail
completely, or perform very poorly. However, at times it may be
necessary to build a system based on incomplete knowledge. This
deficiency may be overcome by a mechanism which explains what knowledge
was used, and how it was used in generating the response to some event.
In this case, the user must have the knowledge to assess the validity
of the response in a given situation. The explanation facility also
indicates the need for further knowledge acquisition whenever it
becomes necessary. Most application domains are dynamic in nature,
i.e., data values are affected by aging, and the applicable policies
are affected by changes. The knowledge engineer plays a key role in
maintaining system integrity with time.

The internal design of a knowledge engine, or knowledge based system,
determines its space and time characteristics. One may assume that the
purpose of the system is to augment human capabilities, and increase
productivity of the operations. The knowledge engineer is, therefore,
responsible for providing the facilities for the users to interact with
the system. This interaction should allow the users to maintain their
normal intellectual thought processes. The subservience, if there is to
be one, should be of the system to the user, and not the other way
around.

VI. Knowledge Based Systems and Artificial Intelligence
One may describe knowledge as a collection of facts and heuristics.
Facts represent that part of knowledge which is widely shared, publicly
available, and generally agreed upon by experts in a field. Heuristics,
on the other hand, represent that part of knowledge which is mostly
private, little discussed rules of plausible reasoning, good judgment,
and good guessing.

Knowledge based systems store facts and heuristics for making
inferences about situations. If the facts and heuristics normally used
by an expert are acquired and properly represented in a system, then
such a system is called a knowledge based expert system [5, 7], or
simply an expert system.

Artificial intelligence is the study of mental faculties through the
use of computational models [2]. If what the brain does can be modeled
as a computation then the work in artificial intelligence will
successfully duplicate the human mental faculties. For example, the
models of human mental faculties in vision and natural language are
useful in building systems for machine vision [9, 17] and machine
processing of natural language [4]. Also see examples of such
applications in [3, 10, 12, 13, 14]. All humans, not just the experts,
have these faculties. The tools and techniques of artificial
intelligence are used in building intelligent systems based on human
mental faculties.

The work in psychology, dealing with the study of human mind, has
influenced the direction of work in artificial intelligence. Looking at
what the psychologists have to say, about the human mind, may help one
better understand the current work and future trends in artificial
intelligence. According to the theory of behaviorism in psychology, all
human behavior can be described in terms of a cause and effect
relationship between the stimuli from the external events and the
responses. Once this relationship is understood and described in the
form of a stimulus-response mechanism, it then becomes possible to
predict and control human behavior. First definitive work on this
subject was published by Watson who said [19]:

Psychology as the behaviorist sees it is a purely objective,
experimental branch of natural science. Its theoretical goal is the
prediction and control of behavior. Introspection forms no essential
part of its methods, nor is the scientific value of its data dependent
upon the readiness with which they lend themselves to interpretation in
terms of consciousness. The behaviorist, in his effort to get a unitary
scheme of animal responses, recognizes no dividing line between man and
brute.

Behaviorism however, has not succeeded in producing a theory of
behavior that is applicable in all situations [8]. Nonetheless, it
continues to play a major role in situations requiring behavior
modification.

Cognitive psychology introduces the notion of thinking, i.e., people
interpret the external stimuli by a thought process in order to produce
a response. The passive cause and effect relationship advanced by the
behaviorist is, therefore, not applicable to human behavior in all
situations. The cognitive psychologist distinguishes the human from the
other animals. It is, however, not clear whether the human ability to
interpret, as seen by the cognitive psychologist, allows for the
possibility of directing ones actions, known as free-will, without
constraint by necessity or fate.
One may summarize the cognitive psychology model of the human mind in
terms of the following features:

• The mind has operationally definable mediators (Logical Behaviorism),
• The mind has a central mechanism for mediators (Central Cognitive
Process -Subprocesses),
• Central mechanism is not reducible to behavioral or peripheral terms
(Contemporary Cognitive Behaviorism, or Information Processing
Approach).

Contrast this model with the classical references to the human mind as
the tabula rasa, i.e., the human mind is a blank tablet at birth, and
the sense experience is the only source of knowledge.

In the Qur'an the words from God are: "When I fashioned him (in due
proportion) and breathed into him My Spirit." [Qur'an 15:20]. Breathing
of God's spirit implies giving the faculty of God-like knowledge and
will. Rightly used, it distinguishes man from other creatures. The
Qur'an, therefore, invalidates the common interpretation of tabula
rasa. The cognitive psychologist seem to have come to the same
conclusion in their own studies of the human mind. We may further add
that the sense experience is the external source of knowledge, and this
knowledge may be internally manipulated in ways that is not always
predictable or reducible to behavioral or peripheral terms.

B.F. Skinner in his book, Beyond Freedom and Dignity, says: "We are all
simply a product of the stimuli we get from the external world. Specify
the environment completely enough and you can exactly predict the
individual's actions:" [15]. This can only be true however, if the
individual does not properly use the free-will given to him by God and
allows himself to be blindly shaped by the changes in the environment.
Bruno Bettelheim in his book, On the Uses of Enchantment, sounded a
positive challenge for the human mind when he said: "If we hope to live
not just from moment to moment, but in a true consciousness of our
existence, then our greatest need and most difficult achievement is to
find meaning in our life."

The fields of psychology and artificial intelligence will continue to
crossfertilize. Of course, a theory in psychology about the human mind
does not mean that the mind actually works that way. It is important to
make this distinction. Furthermore, a machine built on models of the
human mind is just that, i.e., it exhibits intelligent behavior but
does not necessarily duplicate human intelligence. With increasing
intelligence in the model, the reality may still be distinctively
different. The work in artificial intelligence does not claim that its
goal is to produce methods which duplicate exactly those of the people
[2]. Its goal is to build systems which exhibit intelligent behavior,
solving problems in ways that resemble those of the humans. Again, it
is important to make a distinction between a machine which exhibits
intelligent behavior and an intelligent human being. It is necessary to
keep the humans ahead of the machines, retaining the challenge to
improve the machines.

VII. Development of the Human Mind
Our interactions with the environment are permanently recorded in our
mind via the sense perceptions. Roger Penrose, a well known
mathematician once said: "The world is an illusion created by
conspiracy of the senses [15]. Our mind stores the information received
from the senses in a variety of ways. All of this information can be
recalled under appropriate conditions. This is a working premise of
knowledge engineering. The stored information appears to not depend on
the language in which the information is transacted.

Consider now what is said in the Quran about the sense perceptions:

• That day shall We set a seal on their mouth but their hands will
speak to Us, and their feet will bear witness, to all they did. (Qur'an
36:65).
• Their hearing, their sight, and their skins will bear witness.
(Qur'an 41:20).
• On the day when their tongues, their hands, and their feet will bear
witness against them as to their actions (Qur'an 24:24).

Thus, the skin sends signals (speaks) to the brain from the senses of
touch, taste and smell, as does the eyes on seeing and the ear on
hearing. The mind can recall these signals and vocalize them in any
spoken language.

The term nafs (soul) is used in th Quran in a manner cognate to the
human mind. Consider the following quotations:

• Who created you from a single person. Quran 4:1).
• No soul can believe except by the Will of God. (Qur'an 10:100).
• And the soul and the proportion and order given to it. Quran 91:7).
• Do they reflect not in their own mind. (Quran 30:8)
• Soul prone to evil. Quran 12:53).
• Self reproaching soul. (Qur'an 75:2).
• Righteous (at rest and satisfied) soul. Quran 89:21).

The above characterizations in the Qur'an point to various aspects of
the development of the human mind.

VIII. Trends in Knowledge Engineering
Earlier work in the use of computers for knowledge engineering was
limited to areas of axiomatic knowledge, facts consisting of data and
computations. In scientific and business applications, many situations
allowed descriptions of planned responses to events in the environment.
These early systems were called information processing systems, or
simply information systems. Gradually, the developments in information
technology and the understanding of its potential in human
productivity, resulted in emphasis on building systems to support
decision making [ll]. Often, the decision making situations cannot be
described fully in terms of cause and effect relationships. The system,
therefore, consists of data, models, and interfaces to interact and
produce ad hoc responses which the people could analyze and choose for
making decisions. These systems are called decision support systems. In
many situations decisions are based on plausible reasoning, more a
matter of good judgement on the part of an expert. These expert's
knowledge may be represented using the tools of artificial
intelligence. Systems based on this knowledge, detailed and specific to
a domain of application, are called expert systems. All of the above
mentioned systems may be considered as instances of knowledge based
systems, created to serve the potential users.

Acknowledgements: The author gratefully acknowledges the contribution
of Nejma Natalie Heisler who gave many suggestions regarding the
contents, and the style of presentation.

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