Intelligent Advisory System for Supporting University Managers in Law
📝 Abstract
The rights and duties of both staff members and students are regulated by a large and different numbers of legal regulations and rules. This large number of rules and regulations makes the decision-making process time consuming and error boring. Smart advisory systems could provide rapid and accurate advices to managers and give the arguments for these advices. This paper presents an intelligent advisory system in law to assist the legal educational processes in universities and institutes. The aims of the system are: to provide smart legal advisors in the universities and institutes, to integrate rules and regulations of universities and institutes in the e-government, to ease the burden on the legal advisor and the provision of consulting services to users, to achieve accurate and timely presentation of the legal opinion to a given problem and to assure flexibility for accepting changes in the rules and legal regulations. The system is based on experienced jurists and the rules and regulations of the law organizing Saudi Arabia universities and institutes.
💡 Analysis
The rights and duties of both staff members and students are regulated by a large and different numbers of legal regulations and rules. This large number of rules and regulations makes the decision-making process time consuming and error boring. Smart advisory systems could provide rapid and accurate advices to managers and give the arguments for these advices. This paper presents an intelligent advisory system in law to assist the legal educational processes in universities and institutes. The aims of the system are: to provide smart legal advisors in the universities and institutes, to integrate rules and regulations of universities and institutes in the e-government, to ease the burden on the legal advisor and the provision of consulting services to users, to achieve accurate and timely presentation of the legal opinion to a given problem and to assure flexibility for accepting changes in the rules and legal regulations. The system is based on experienced jurists and the rules and regulations of the law organizing Saudi Arabia universities and institutes.
📄 Content
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 3, No. 1, 2009
Intelligent Advisory System for Supporting University Managers in Law
A. E. E. ElAlfi Dept. of Computer Science Mansoura University Mansoura Egypt, 35516 Ael_Alfi@yahoo.com M. E. ElAlami Dept. of Computer Science Mansoura University Mansoura Egypt, 35516 Moh_ElAlmi@mans.eun.eg
Abstract— The rights and duties of both staff members
and students are regulated by a large and different numbers
of legal regulations and rules. This large number of rules and
regulations makes the decision-making process time
consuming and error boring. Smart advisory systems could
provide rapid and accurate advices to managers and give the
arguments for these advices. This paper presents an
intelligent advisory system in law to assist the legal
educational processes in universities and institutes. The aims
of the system are:
to provide smart legal advisors in the universities and
institutes, to integrate rules and regulations of universities
and institutes in the e-government, to ease the burden on the
legal advisor and the provision of consulting services to
users, to achieve accurate and timely presentation of the legal
opinion to a given problem and to assure flexibility for
accepting changes in the rules and legal regulations. The
system is based on experienced jurists and the rules and
regulations of the law organizing Saudi Arabia universities
and institutes.
Keywords: decision support systems, advisory systems, rule
based systems ,university rules and regulations, e-government.
I.
INTRODUCTION
Decision making, often viewed as a form of reasoning
towards action, has raised the interest of many scholars
including philosophers, economists, psychologists, and
computer scientists for a long time. Any decision problem
aims to select the “best” or sufficiently “good” action(s) that
are feasible among different alternatives, given some
available information about the current state of the world and
the consequences of potential actions [1]. Advisory systems
provide the advices and assist for solving problems that are
normally solved by human experts. They can be classified as
a type of expert systems [2,3]. Both advisory systems and
expert systems are problem-solving packages that mimic a
human expert in a special area. These systems are
constructed by eliciting knowledge from human experts and
coding it into a form that can be used by a computer in the
evaluation of alternative solutions to problems within that
domain of expertise. Advisory systems do not make
decisions but rather help guide the decision maker in the
decision-making process, while leaving the final decision-
making authority up to the human user [4]. The decision
maker works in collaboration with the advisory system to
identify problems that need to be addressed, and to
iteratively evaluate the possible solutions to unstructured
decisions. For example, a manager of a firm could use an
advisory system that helps assess the impact of a
management decision on firm value [5] or an oncologist can
use an advisory system to help locate brain tumors [6]. In
these two examples, the manager and the oncologist are
ultimately
(and
legally)
accountable
for
any
decisions/diagnoses made. Traditionally rule-based expert
systems operate best in structured decision environments,
since solutions to structured problems have a definable right
answer, and the users can confirm the correctness of the
decision by evaluating the justification provided by
explanation facility [7]. Luger [8] has presented some
limitations of current expert systems.
Advisory systems are designed to support decision
making in more unstructured situations which have no single
correct answer. In unstructured situations cooperative
advisory systems that provide reasonable answers to a wide
range of problems are more valuable and desirable than
expert systems that produce correct answers to a very limited
number of questions [9].
Advisory systems support decisions that can be classified
as either intelligent or unstructured, and are characterized by
novelty, complexity, and open-endedness [10]. In addition to
these characteristics, contextual uncertainty is ubiquitous in
unstructured decisions, which when combined exponentially
increases the complexity of the decision-making process.
Because of the novel antecedents and lack of definable
solution, unstructured decisions require the use of knowledge
and cognitive reasoning to evaluate alternative courses of
action to find one that has the highest probability of desirable
outcome [11]. The more context-specific knowledge
acquired by the decision maker in these unstructured
decision-making situations, the higher the probability that
they will achieve the des
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