Download A Rule-Based Expert System for Mineral Identification

VOL. 3, NO. 2, February 2012
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2012 CIS Journal. All rights reserved.
http://www.cisjournal.org
A Rule-Based Expert System for Mineral Identification
1
1
Folorunso, I. O. , 2 Abikoye, O. C., 2 Jimoh, R. G. and 2 Raji, K.S
Department of geology and Mineral Sciences, University of Ilorin, Ilorin, Nigeria
2
Department of Computer Science, University of Ilorin, Ilorin, Nigeria
ABSTRACT
The emerging knowledge-driven dictate of the economy has no boundary. The use of Information and Communication
Technology (ICT) has enhanced applications of knowledge-based expert system. The idea is that human expert is programmed
into the machine in form of an Artificial Intelligence (AI). This work involves the use of expert system in mineral
identification. The design and implementation of the expert system is based on the physical characteristic of the forty minerals
involved in this study as the knowledge domain. The inference engine is rule-based as suggested to be better by previous
researchers. Visual Basic is used for the implementation while Microsoft Access is used for creating the database.
Keywords: knowledge-driven, ICT, Expert System, AI, Rule-based
1. BACKGROUND
It is believed that the parallel development in
electronics and digital computer constitute the profound
innovations in the history of human progress. Computing
is the latest and most dynamic technology that every
sphere of technology, science, social science and other
sphere have borrowed a hand from (Keller, 1988).
A mineral, by definition, is any naturally
occurring, inorganic substance, often additionally
characterized by an exact crystal structure. Its chemical
structure can be exact, or vary within limits. Elements that
occur naturally are also considered as minerals.
All minerals belong to a chemical group, which
represents their affiliation with certain elements or
compounds. The classified chemical groups are known as:
Elements, Sulfides, Oxides, Halides, Carbonates, Nitrates,
Borates, Sulfates, Chromates, Phosphates, Arsenates,
Vanadates, Tungstates, molybdates, and Silicates (Brain &
Berry, 1959). Some of these chemical groups have subcategories, which may be categorized in some mineral
references as separate groups.
All minerals belong to various crystal structure
groups, classified according to the way the atoms of the
mineral are arranged. Minerals also have distinctive
properties, such as color, hardness, crystal habit, specific
gravity, luster, fracture, and tenacity. Many of these
properties can vary among a single mineral, within limits.
Many minerals exhibit certain properties that others do not,
such as fluorescence and radioactivity [17].
Minerals are an economic commodity; they are
mined because of the need for a valuable element they
contain or an intrinsic property they may have. Other
minerals are mined for their beauty and rareness, thus
giving many specimens an accepted worldwide value.
There are about 3,000 different types of minerals, and new
ones are constantly discovered. Most of them are not
known to professional mineral collectors, because they are
rare, have no economic purpose, and for the most part do
not make good specimens [17].
Application of computer in mineral identification
is never an exception in this regard. Before advent of
computers and its associated technologies, geologists
identified minerals based on the unique physical
characteristics exhibited by the sample under investigation.
The proposed system is a replica of the traditional
approach using the concept of expert system where the
expertise of a geologist is developed in form of a
knowledge-domain. In such cases, an approach capable of
inferring is required which is thereby assisted by
experience and domain knowledge. This technique rely
ultimately on decision arrived at through a variety of
inference mechanism and is conveniently grouped under
the title of Artificial Intelligence.
Artificial Intelligence is concerned with getting
computer to do tasks that requires human intelligence. An
intelligent program is one that exhibits’ behavior similar to
that of a human being when confronted with a similar
problem [1].
An Expert System is computer program that
emulates the behavior of human expert to solve problems
which are real word problems associated with a particular
domain of knowledge [1]. An Expert System which is
sometimes called an Intelligent Knowledge Base System
(IKBS) is essentially a computer system containing
expertise in a particular area. The primary goal of an
Expert System is to make expertise available to decision
makers and technicians who read answers quickly [5]. It is
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VOL. 3, NO. 2, February 2012
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2012 CIS Journal. All rights reserved.
http://www.cisjournal.org
then desirable to establish the application of such robust
technique in geological mineral identification.
Mineral identification in geology has a very wide
scope hence; minerals are classified under various
categories using series of properties and parameters. In this
system, mineral identification is based on physical
properties of the selected minerals. The unique features of
individual mineral are stored in a database. The software
application is designed to have a simple interface and easy
follow up. For this study, forty different minerals were
documented in the system database. The inference engine
developed in this work is based on a rule-base forward
chaining approach.
Also, seven physical mineral
properties will be chosen and indexed on; the software can
easily be expanded to accommodate more minerals.
2. STATEMENT OF THE PROBLEM
The problem of insufficient expert to analyze and
interpret solid minerals has constituted a waste of natural
resources in some parts of the world and also, in most
cases the mineral phases are underexplored [12].
Resources will be there wasted since no one in such area
has the required expertise to identify such valuable
resources. It is hoped that the proposed mineral
identification expert system would cater for this limitation.
A useful and time sharing learning aid could be provided
for anyone interested in mineral resources identification.
Loss of vital knowledge through the death of human expert
can equally be combated by back up storage.
3. REVIEW OF LITERATURE
The key issue in Artificial Intelligence, and
especially in Expert Systems, is the way knowledge is
stored and extracted from the knowledge base. This fact is
not only related to the application field itself, but also to
the feasibility of the application. How nice and intelligent
an Expert System may be, if the time to infer the required
answer is too long, it is completely useless. Therefore,
knowledge representation and extraction mechanism not
only have to fulfill their task, but they do it very quickly
and efficiently [1]. There are five different types of models
used in concept formation. These includes: Classical
model, Exemplar model, Probabilistic model, Hybrid
model and the theory-oriented model [5].
Classical Model: The classical model of concept
structure date back to Aristotle and are familiar from
nearly all mathematics textbooks. A concept is defined by
a set of defining features, which are individually necessary
and jointly sufficient [5].
features need not to be defined. Instead the features of a
concept are salient ones that have a substantial probability
of occurring instances of the concept [5].
Exemplar Model: This model is less
constraining than the probabilistic view. It drops the
requirement that a concept be a summary description of a
category as a set feature. Instead, the general assumption is
that a concept consists of a collection of representations of
some or all of its “exemplars” [5].
Theory-Oriented Model: Despite considerable
differences, the four view of concepts structure presented
above agree on one central point; those concepts are
represented as a set of features and have problem
explaining phenomena [5].
Hybrid Model: Given the difficulties with some
core-only models, an interesting possibility is to look at
models that allows for an independent identification
procedure, which is one that may contain different
information from the core. It allows for another degree of
freedom in developing concept models, since the
representation of core and identification procedure may
now be chosen independently [5].
An Expert System is a computer program which
is designed to carry out tasks associated with a human
expert. It is a program which tries to do things which are
usually regarded as the province of human, involving
judgments and decision making [3]. As human Experts
tends to have a great deal of specialized knowledge in their
fields, Expert Systems are usually knowledge based
systems which contain large database of knowledge
information.
Artificial Intelligence is the science of making
machines do things that require intelligence if done by
men. It is a collection of techniques to handle knowledge
in such a way as to obtain new results and inferences
which are not explicitly (imperatively) programmed. Using
Artificial Intelligence involves a strong need for a number
of tools and methods [1].
In very simple terms, an Expert System is usually said to
be made up of three main parts:
•
•
•
A knowledge base- consisting of the knowledge
relating to the field of the expert.
An inference engine- a system for manipulating
the knowledge so as to draw inference from it.
A user interface- making the knowledge of the
expert accessible to the non expert user.
Probabilistic Models: In probabilistic model, a
concept is again defined by a set of feature but these
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VOL. 3, NO. 2, February 2012
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2012 CIS Journal. All rights reserved.
http://www.cisjournal.org
Expert systems have been applied to the
identification and use of microfossils in petroleum
industries. Expert systems are also applied in evaluation of
energy resources, widely used in Engineering Geology and
Geophysics especially in areas of seismic data evaluations,
Interpretation of Geological signals, and drill site
evaluation and Geological survey. More so, Expert
Systems would find their true home in Geological
applications in aiding the complex multidisciplinary
evaluation processes typically encountered in areas such as
basin studies. More sophisticated expert systems software
and its interfacing with spatial databases are required to
attempt such tasks. Expert Systems have been applied on
map contouring, mineral resources identification, rock
identification as well as mineral exploration and mining
[12].
Inference
Engine
User
Interface
Knowledge
Base
5. DEFINITION OF TERMS
Fig 1: Main parts of an expert system [3]
•
The spectrum of applying expert systems
technology to industrial and commercial problems is so
wide as to defy easy characterization. The applications find
their way into most areas of knowledge work. They are as
varied as helping salesperson sells modular factory-built
homes to helping NASA plan the maintenance of a space
shuttle in preparation for its next flight [11].
•
•
Applications tend to cluster into seven major classes:
4. GEOLOGICAL
EXPERT SYSTEMS
APPLICATION
OF
In the past, the number of Geologist using
computer were very few. However, presently the
percentage of Geologist that has resulted to the use of
computers for the solution of their geological problems is
numerous. Expert systems have been widely used in nearly
all aspect of Geology among numerous applications are the
following:
Expert systems were applied to evaluate
depositional environment. The evaluation is performed
from the knowledge of molecular parameter interpretation
techniques used by Geochemists on organic samples. The
system was written in the prolog language on a Macintosh
and performs to 96% accuracy against a test data set [12].
Furthermore, Expert Systems involving analyzed
program led to prospector which aid Geologist in their
search for ore deposits. Given field data about a geological
region, it can determine the probability of carbonate
lead/zinc, etc. its expertise was based on geological rules
which form models of ore deposits, and a database of
known rocks and minerals [8].
Cleavage: Cleavage is the ability of a mineral to
separate into smaller and smaller particles bounded by
smooth surfaces parallel to the directions of faces of
possible crystal forms [2].
Crystals: Crystals are bodies bounded by surfaces,
usually flat, arranged on a definite plan which is an
expression of the internal arrangement of the atoms
[10].
Diaphaneity: Transparency, also known technically
as diaphaneity, is a function of the way light interacts
with the surface of a substance [12].
There are only three possible interactions. If the light
enters and exits the surface of the substance in relatively
undisturbed fashion, then the substance is referred to as
transparent. If the light can enter and exit the surface of the
substance, but in a disturbed and distorted fashion, then the
substance is referred to as translucent. If the light cannot
even penetrate the surface of the substance, then the
substance is referred to as opaque. Many substances that
are transparent can easily contain flaws and distortions that
will limit a light beam's travels through a substance and
make it translucent [13].
•
•
•
Fracture: Fracture is a description of the way a
mineral tends to break [13]. Fracture occurs in all
minerals even ones with cleavage, although a lot of
cleavage directions can diminish the appearance of
fracture surfaces. Different minerals will break in
different ways and leave a surface that can be
described in a recognizable way. Is the broken area
smooth, Irregular, Jagged, or Splintery? [13].
Hardness: Hardness is one measure of the strength of
the structure of the mineral relative to the strength of
its chemical bonds [13].
Luster: Luster is a description of the way light
interacts with the surface of a crystal. It has nothing to
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VOL. 3, NO. 2, February 2012
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2012 CIS Journal. All rights reserved.
http://www.cisjournal.org
•
do with color or shape, but is related to transparency,
surface conditions, crystal habit and index of
refraction [13].
Mineral: A substance having a definite chemical
composition and atomic structure and formed by the
inorganic process of nature [10].
6. THE KNOWLEDGE BASE
The knowledge Base consists of some encoding
of the domain of expertise for the system. This can be in
the form of semantic net (QU68), procedural
representation (WIT 75), production rules (DBS77), or
frames (BW77) [4].
For this research, consider is only for the
production rules for our knowledge base. These rules occur
in sequences and are expression of the form:
If <conditions>, then <actions>
which lead to actions being executed. Backward chaining
is the reverse. It is a bottom up procedure which starts with
goals (or actions) and queries the user about information
which may satisfy the conditions contained in the rules [4].
It is a verification process rather than an exploration
process. An example of backward chaining is MYCIN
(VMS81), and an example of forward chaining is EXPERT
(WK81). A system which uses both is Prospector
(DGH79) [4].
8. THE USER INTERFACE
The user interface of an expert system is the
means of communication between a user wishing to solve
a problem and the problem solver (expert system).
It is
vital that this communication is as meaningful and useful
as possible. Otherwise, the expert system will neither be
useful nor used. The user interface may be command
driven, event driven and icon oriented (Graphical Objects).
The expert system shell is shown below.
If the conditions are true then, the actions are executed.
When rules are examined by the inference engine, actions
are executed if the information supplied by the user
satisfies the conditions in the rules. Conditions are
expressions involving attribute and logical connective
‘and’.
If
Rule 1 is found in the dbase Rule 2
Then
Display mineral name
Note: Rule 1 (R1) is the information supplied by the user
and Rule 2 (R2) is the rule in the knowledge base for a
particular mineral. No two mineral share the same rule
since every mineral has a unique attribute value.
7. THE INFERENCE ENGINE
The Inference Engine forms the heart of the
expert system; the knowledge base serves as the brain of
the expert system. The inference engine chums through
countless potential paths and possibilities based on some
combinations of rules, cases, models or theories. Some
rules such as predicate logic mimic human reasoning and
offer various mathematical arguments to any query [9].
EXPLANA
USER INTERFACE
A full example of a rule might resemble the
following:
-TION
CASE
SYSTEM
SPECI
ADA
TA
INFERENCE
ENGINE
KNOWLE-
KNOWL
DGE
-EDGE
BASE
EDITOR
BASE
Fig 2: Expert system shell
Two methods of inference often are used, forward
and backward chaining. Forward chaining is a top down
method which takes facts from satisfied conditions in rules
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VOL. 3, NO. 2, February 2012
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2012 CIS Journal. All rights reserved.
http://www.cisjournal.org
8. METHODOLOGY
The inference engine in this study operates by the
method of forward chaining.
In order to execute a rule-base expert system
using the method of forward chaining, we merely need to
fire (execute) actions whenever they appear on the action
list of a rule whose conditions are true. This involves
assigning values to attributes, evaluating conditions and
checking to see if all of the conditions in a rule are
satisfied.
A general algorithm of this might be:
While
Values for attributes remain to be input
Read Value and assign to attributes
list assigned to each attribute then, the rules which need
checking and possibly firing appears on the rule list
allocated to each condition. And, each rule possesses an
action list which enumerates the actions to be executed
when the rule is fired.
For such rules as:
R1: If
X1 is satisfy
Then
Ai,
Where x1 represent the various attributes used in
the knowledge base for a particular mineral.
The condition necessary for x1 is c1
C1↔x1
Evaluate conditions
Fire rules whose conditions are satisfied
The conditions are only evaluated at the time they
might change and that the rules are checked to see if all of
their conditions are satisfied, only when they might be
ready to be fired, not before.
We can represent the basic component in the rulebase system of this inference engine as follows:
Attribute:
x1, x2, ……, xn1
Conditions:
c1, c2, ….. , cn2
Rules:
R1, R2,….., Rn3
Actions: A1, A2, ……., An4
We only need to execute an action when a rule
containing it is fired. We fire a rule only when all of its
conditions are satisfied. To detect this we shall assign a
counter to each rule and use it to keep track of exactly how
many of the conditions in the rule are currently satisfied.
Thus, we only check to see if a rule is ready to fire when
one of its conditions has become true. In turn, a condition
needs to be evaluated only when all of its attributes have
been defined and one has changed. This is kept track of
with a counter assigned to that condition. In addition, an
attribute is flagged as true or false [4].
The condition c1 of a particular attribute x1
represent the rule R1 that must be satisfy for the action Ai
to be executed. The action here is to display the name of
the mineral. If the conditions necessary for the attribute are
satisfy then the action is carried out. Otherwise, it moves
to the next rule in the knowledge base [4].
9. INPUT/ OUTPUT DESIGN
This system is built to perform a unique task, by
entering into a shell (query) all the necessary or required
properties about a task. The task here is to identify a
particular mineral by its name. The building block of this
system consists of principally the knowledge base which is
a database in the system, and the reasoning or inference
engine. For simplicity and portability, the database is of
Microsoft access 2003 which could still run on recent
windows.
We can determine which conditions need to be
checked and maybe evaluated with the aid of a condition
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VOL. 3, NO. 2, February 2012
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2012 CIS Journal. All rights reserved.
http://www.cisjournal.org
Hence, the development of expert system should be
encouraged in all fields where human expert exist.
A well developed expert system provides
consistent answers for repetitive decisions, processes and
tasks; it can hold significant levels of information and
reduce dependence upon one expert as it can be used by
many users more frequently. It is therefore recommended
that more work should be done on developing more expert
system that can aid teaching hence improving the level of
our education system.
REFERENCE
Fig 3: Input/ Output Design
10. CONCLUSION
The Knowledge of computer can be applied in
every study of human Endeavour including Geology.
Expert system for mineral identification in Geology and
Mineral Sciences is achievable. A well functioning expert
system can mean increased distribution of expertise, a new
communication channel for knowledge, more efficient
education and a faster adaptation to change.
A well developed expert system tackles the
problem of insufficient expert and cost reduction.
Teaching of Mineral identification at the undergraduate
level can be supported with expert system hence,
promoting effective and meaningful learning of scientific
observation in Earth Science.
11. RECOMMENDATION
Expert systems captures scarce expert knowledge
and render it archival, it captures expertise before it is lost.
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