Download Knowledge Representation in Competence Management using First

Knowledge Representation in Competence
Management using First Order Predicate
Logic for Machine Learning: A Mathematical
Approach to Artificial Intelligence
Application
Prof Pooja Tripathi
Associate Professor ,IPEC, Research Scholar BIT MESRA Ranchi.
To whom all the contacts may be sent: [email protected],
[email protected]
Dr Jayanthi Ranjan
Professor, Institute of Management Technology , Ghaziabad.
Dr Tarun Pandeya
Dr S.L. Gupta
Professor, BIT Mesra, Ranchi India
Abstract - Almost every day we come
across with some situation that needs
attention where we apply our general
problem solving behavior to address it. In
most of the cases we apply our intelligence
and sometimes the integrated effort by a
group of experts to address the issue.
Competence Assessment and performance
management of the organization is a
complex decision making process. To
identify a right candidate for the job
various factors are to be considered in the
form personality, ability, skills and
knowledge and to be assessed in the
candidate. Competence Management and
Performance Assessment have emerged as
a multidisciplinary complex science
involving several of skills related to
number of technology and the behavioral
indicators. The paper explores the method
for the information gathering from
competence experts and choosing the
proper formats by the knowledge
engineers to represent the information.
The Knowledge is represented in form of
data bases, facts and rules using suitable
data
base
management
system,
programming languages as AI tools to
develop the production rules like if and
then. First Order Predicate Logic is an
advanced form of propositional calculus,
which is the simplest form of knowledge
representation
that
comprises
of
statements. The paper discusses the FOPL
representation for the Competence
Management System.
Keywords - First Order Predicate Logic,
Knowledge Representation, Machine
Learning.
I. INTRODUCTION
Almost every day we come across with
some situation that needs attention where we
apply our general problem solving behavior
to address it. In most of the cases we apply
our intelligence and sometimes the integrated
effort by a group of experts to address the
issue. For a fixed situation, we normally
arrive at a consensus that can be converted to
an algorithm that a machine can apply and
solve the problem. This is like your income
tax calculation, some statistical inferences or
making some predictions based on some
mathematical models. Observing the
The Eighth International Conference on eLearning for Knowledge-Based Society, 23-24 February 2012, Thailand
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Prof Pooja Tripathi, Dr Jayanthi Ranjan, Dr Tarun Pandeya, and Dr S.L. Gupta
capabilities of a modern computer a new area
of application has emerged in which a
machine adopts the problem solving
behaviour of a human. This area is known as
Artificial Intelligence that takes new
approaches to solve problem to a very large
domain. Artificial Intelligence, otherwise
known as AI, is the study and development
of intelligent machines capable of
performing complex tasks that require
thought and behavior normally associated
with human intelligence. Computer programs
are a common area of specialization in this
branch of science. Artificial Intelligence
adapts characteristics of human problemsolving skills and then applies them as
algorithms
easily
comprehended
by
computer systems. Such systems are
routinely and widely used today in robotics,
corporations, militaries and homes around
the world. One application of this technology
is knowledge based systems where
knowledge of human experts is utilized by
the machines to take decisions. The power of
machine comes from the power of its
knowledge base. Information acquired from
experts is stored in it in different forms and
formats chosen by the knowledge engineers.
Knowledge is represented in form of data
bases, facts and rules using suitable data base
management
system,
programming
languages or AI tools using production rules
like if and then. Some of the benefits of IFTHEN rules are that they are modular, each
defining a relatively small and, at least in
principle, independent piece of knowledge.
New rules may be added and old ones
deleted usually independently of other rules.
A Thinking Machine = A Knowledge Base
+ An Inference Engine
These rules that are used for knowledge
representation is used for machine learning.
Machines are trained with these rules with
different form of knowledge representation.
Machine learning programs examine the
database to automatically determine the rules
(Michalski, R. S.,1983). Machine learning
was first used in agriculture to develop rules
for diagnosing soybean diseases (PLANT/ds)
(Michalski, R. S. 1980.). A microcomputerbased, machine-learning system has been
developed
for
agricultural
problems
(Fermanian, T. W.,1988). This system was
first used to generate rules for a grass
identification system (Weeder). First Order
Predicate Logic could also be utilized as a
tool for machine learning for decision
making in human resource and development.
1.1 Theory about First Order Predicate
Logic
First Order Predicate Logic is an
advanced form of propositional calculus,
which is the simplest form of knowledge
representation that comprises of statements.
For example All men are Mortal and
Socrates is Mortal infers Socrates is mortal.
First-order logic permits reasoning about
the propositional connectives (as in
propositional logic) and also about
quantification ("all" or "some"). First-order
logic is symbolized reasoning in which each
sentence, or statement, is broken down into a
subject and a predicate. The predicate
modifies or defines the properties of the
subject. A sentence in first-order logic is
written in the form Px or P(x), where P is the
predicate and x is the subject, represented as
a variable. Complete sentences are logically
combined and manipulated according to the
same rules as those used in Boolean algebra.
First order logic is very well understood,
and has a sound mathematical foundation. It
combines an expressive language with a
sound method of inference in that language.
Therefore FOL has been immensely popular
in AI's attempt to construct intelligent
programs.
First-order predicate logic is composed
of statements that are assumed to be true.
The statements are composed of:
 atoms (symbols),
 predicates (a function with one or more
atomic arguments),
 two substatements joined by a
conjunction, disjunction, or implication,
 a negated substatement, and
Special Issue of the International Journal of the Computer, the Internet and Management, Vol. 19 No. SP2, February, 2012
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Knowledge Representation in Competence Management using First Order Predicate Logic for Machine Learning:
A Mathematical Approach to Artificial Intelligence Application

a statement with an existential or
universal quantifier (in this case, atoms
in the statements can be replaced by
variables in the quantifier).
The predicate calculus includes a wider
range of entities. It permits the description of
relations and the use of variables. It also
requires an understanding of quantification.
The language of predicate calculus
requires Variables and Constants
---these include the logical constants
Predicate
---these relate a number of entities. This
number is usually greater than one. A
predicate with one argument is often used to
express a property e.g. sun(hot) may
represent the statement that ``the sun has the
property of being hot''.
Sentence
---a formula with no free variables is a
sentence.
Two informal examples to illustrate
quantification follow:
Utility of First Order Predicate Logic
(FOPL)
First-order logic can be useful in the
creation of computer programs. It is also of
interest to researchers in artificial
intelligence (AI). There are more powerful
forms of logic, but first-order logic is
adequate for most everyday reasoning. Most
systems use rules (e.g., IF a THEN b) to
represent domain knowledge; however, more
complicated representations do exist.
Another issue of knowledge representation is
uncertainty. Some knowledge bases will
allow rules to be expressed with a degree of
uncertainty. The inference engine will then
combine these uncertainties to give the final
solution a confidence level. A third feature
of the knowledge base is the ability to
express concepts in symbolic terms (e.g., IF
the stove is HOT THEN set dial to
MEDIUM) instead of in numeric terms. This
feature allows more abstract concepts to be
expressed.
The inference engine is responsible for
finding the proper knowledge in the
knowledge base to develop a solution. For
rule-based systems, the inference engine
combines (i.e., chains) rules together using
rules of logic.
1.2 Objectives of the Research
 Acquisition of Knowledge from Domain
Expert on Competence mapping and
assessment
 Development of Production Rules for
Competence Identification
 Representation of these rules using First
Order Predicate Logic
 Utilization of these rules for machine
learning
II. RESEARCH METHODOLOGY
To identify a right candidate for the job
the main factors to be considered are whether
the competencies in the form personality,
ability, skills and knowledge are found in
candidate or not. If the competencies are
found in the candidate, then system identifies
the candidate with the help of production
rules developed for the identification of the
right candidate for performing the right job
or if candidate is not identified, then it
identifies the competencies gap on the basis
of production rules developed for the given
job in terms of the competencies
requirements. A complete detail of the
candidate identification process is acquired
from the published reports and experts.
Production rules are developed using the
acquired information and by the help of
experts. First Order Predicate Logic is
utilized for the representation of these rules.
First Order Predicate Logic is also utilized
for developing the inference mechanism. The
knowledge so represented by the First Order
Predicate Logic is utilized for machine
learning. The inference mechanism so
developed is also applied for machine
learning. A suitable programming language
is applied for the process development.
2.1
Knowledge
Acquiring
through
Questionnaire Technique
The questionnaire used, began with a
brief introduction about the research study
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Prof Pooja Tripathi, Dr Jayanthi Ranjan, Dr Tarun Pandeya, and Dr S.L. Gupta
which specified that the researcher’s interest
in their perceptions of what they think the
competent faculty should have. The survey
was designed to make it as easy, convenient,
less time-consuming and as interesting as
possible. The data was collected through the
personal meet, focus group interviews.
The questionnaire consisted of two parts:
the first section gathers some simple
demographic data like age, education,
gender, teaching experience and other work
responsibilities and so on, followed by the
second section which consists of a list of
competence attributes to be evaluated by the
participant. This section entails attributes of
the job itself as well as the environment and
the physical location of the work place. The
final questionnaire was consisting of 58
items which were chosen from the original
pool of 73 items.A 5-point scale ranging
from 1: ‘Least Important’ to 5: ‘Most
important’ was used to study participants’
assessments of individual attributes and
values. On an average the survey took about
12-15 minutes to complete.
2.2
Knowledge
Representation
in
Competence
Management
and
its
importance
Competence
Management
and
Performance Assessment have emerged as a
multidisciplinary complex science involving
several of skills related to number of
technology and the behavioral indicators.
Every discipline has its own physiology and
needs a specific environment and technology
for its proper growth. Human Resource
research involves a large number of
discipline and areas that generates
competence
profiling,
competence
collection, competence assessment, and
evaluation techniques for the same. If the
knowledge management will be done by
using IT based tools, it will be permanent
information that could be accessible for the
employee and the management whosoever is
interested in the area. It will minimize
duplication, better research planning and
management by better access and expansion.
III. FUNCTIONAL DESCRIPTION OF
DEVELOPED SYSTEM
The system has been developed for the
competence management and performance
assessment of the faculty members and can
be used by the educational institutions. The
development of the proposed system
includes the development of different subsystems. The above section describes the
different sub-systems in the developed
system as well as its functionality.
1. The knowledge base: Developed to
enable the expert or knowledge
engineer to enter and save domain
ontology and domain rules in an
efficient and easy way.
2. Web page interface of the proposed
system: For using purposes includes
entering ontology, knowledge, and
the use of the expert system by
entering the options and get the
output results with reasoning too.
3. The entire control and operation of
the system is done by the inference
engine; that is developed using ASP
dot net (ASP.NET)and the MS SQL
server; which handles the knowledge
in format of XML to get the result
from the XML file (Knowledge base)
that stores the knowledge rules. The
main roles of the inference engine are
summarized as: It applies the expert
domain knowledge to what is known
about the present situation to
determine new information about the
domain. The inference engine is the
mechanism that connects the user
inputs in the form of answers to the
questions to the rules of knowledge
base and further continues the session
to come to conclusions. This process
leads to the solution of the problem.
The inference engine also identifies
the rules of the knowledge base used
to get decision from the system and
also forms the decision tree.
The user submits the query through the
interface to the system. The system generates
a series of questionnaire and takes the input
from the user. The inference engine accepts
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Knowledge Representation in Competence Management using First Order Predicate Logic for Machine Learning:
A Mathematical Approach to Artificial Intelligence Application
these queries and takes the input from the
user and uses this dynamic information
together with the existing knowledge on
competence
management
and
the
performance assessment. The contents of the
knowledge base are used to derive the
conclusion. The inference mechanism is
carried out in four phases.
Select --→ Identify --→ Match -→Execute.
During the match stage the contents of
working memory is compared with the facts
and rules stored in the knowledge base.
When consistent matches are found the rules
are again compared on the basis of the input
given by the user and finally one of the rule
is executed on selection of the image.
IV. CONCLUSION
As result of using the developed system,
a cultural shift occurred within the
educational institution. Faculty members
became more accountable to teacher
candidate competency attainment and
teacher candidates became more attuned to
becoming an effective prospective teacher.
The Competence and Performance expert
system implemented quality principles,
utilized the value added approach to
management and systematically used data
that yielded positive results; it has become a
model for other teacher education programs.
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soybean disease
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Fig.1 Overall Competence based Management
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