Final term paper

Running Head: EXPERT SYSTEMS 1
A Term Paper on Expert System and its Applications in Nepal
Hannah Gurung and Rajesh Paneru
Kathmandu University School of Management (KUSOM)

EXPERT SYSTEMS 2
A Term Paper on Expert System and its Applications in Nepal
Submitted By:
Hannah Gurung (17312)
Rajesh Paneru (17322)
Submitted To:
Asst. Prof. Sanjay Pudasaini
Kathmandu University School of Management
KUSOM
In partial fulfillment of the requirements for the degree of
Master of Business Administration (MBA)
Balkumari, Lalitpur
25th
November, 2017

EXPERT SYSTEMS 3
ACKNOWLEDGEMENT
Expert systems are undoubtedly the most important participants of Information
Revolution. Companies from diverse industries have been successful in achieving their set goals
using expert systems. However, Nepal’s organizations are still operating primitively, with very
less application of expert systems. This term paper has been a great insight for us since we got
the opportunity to look into Nepalese domain and see how we are lagging decades behind from
the western part of the world.
We would like to express our sincere gratitude towards Mr. Sanjay Pudasaini, Assistant
Professor, Kathmandu University, for giving us this opportunity to work on the applications of
expert systems in Nepalese organizations. While preparing this paper, we also got insight about
how expert systems are necessary but not sufficient conditions for organizational success. Our
sincere gratitude towards Kathmandu University School of Management for identifying this need
to have term paper in our curriculum.
We look forward to similar other opportunities in days to come.
Thank You!
Sincerely,
Hannah Gurung (17312)
Rajesh Paneru (17322)

EXPERT SYSTEMS 4
ABSTRACT
Expert Systems have evolved as Information breakthrough in the field of Artificial
Intelligence. Experts systems have given due consideration to specific knowledge in finding out
the most suitable solutions to organizational problems. This paper explores the business impact
of expert systems in multitude of sectors such as education, marketing, investment and finance,
operations, business processes and the like. The paper reviews literatures to identify the impacts
that ESs have on these sectors and to know what should be done for effective implementation of
ES. Furthermore, the study highlights some success and failure stories of companies that have
used ES to infer applicable areas in Nepalese context. The study highlights crucial importance of
expert system in tourism, agriculture, hydro-projects, defense, spiritual yoga, education, and
marketing sector of Nepal and recommendations have also been based upon these sectors where
expert systems can bring about synergic effects through human- machine interface, i.e. expert
system.

EXPERT SYSTEMS 5
TABLE OF CONTENTS
ACKNOWLEDGEMENT............................................................................................................ 3
ABSTRACT................................................................................................................................... 4
TABLE OF CONTENTS………………………………………………………………………. 5
LIST OF FIGURES……………………………………………………………………………...6
ABBREVIATIONS....................................................................................................................... 7
CHAPTER 1: INTRODUCTION................................................................................................ 8
1.1 Background ........................................................................................................................... 8
1.2 Historical Development of Expert System............................................................................ 9
CHAPTER 2: REVIEW OF LITERATURE........................................................................... 11
CHAPTER 3: SUCCESS AND FAILURE STORIES OF EXPERT SYSTEMS ................. 15
3.1 Success Stories of Expert System ....................................................................................... 15
3.2 Failure Story of Expert System........................................................................................... 16
CHAPTER 4: STATUS OR POSSIBILITIES OF EXPERT SYSTEM IN NEPAL............ 17
CHAPTER 5: CONCLUSION AND RECOMMENDATION ............................................... 21
5.1 Conclusion........................................................................................................................... 21
5.2 Recommendations............................................................................................................... 21
REFERENCES............................................................................................................................ 23
APPENDIX 1: BASIC WORKING PRINCIPLE OF EXPERT SYSTEM .......................... 26
APPENDIX 2: SCHEMATIC REPRESENTATION OF EXPERT SYSTEM .................... 27

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LIST OF FIGURES
Figure 1: Timeline Showing Historical Development of Expert System ..................................... 10

EXPERT SYSTEMS 7
ABBREVIATIONS
CAI Computer Aided Instructions
ES Expert System
ESSMDM Expert System for Management of Malformation Disease of Mango
FAO Food and Agriculture Organization of the United Nations
GDP Gross Domestic Product
i.e. That is
ITS Intelligent Tutoring System
MOOC Massive Open Online Course
Prof. Professor

EXPERT SYSTEMS 8
CHAPTER 1
INTRODUCTION
1.1 Background
An ES is a computer program that exhibits, within a specific domain, a degree of
expertise in problem solving that is comparable with that of a human expert. (Jayaraman &
Srivastava, 1996). Expert systems are knowledge- based systems that have simplified human job
by bringing out the possible and the most favorable outcomes to situations where decision
making is vital. The reason expert systems have gained so much of popularity is because they
combine the capabilities of both computers and human experts in making sound judgment in the
least time interval possible. While computers are capable of processing huge amount of data,
human beings can draw workable inferences from such data by considering the constraints.
Hence, expert systems, by combining human and machine capability, allow companies to
process huge data and get solutions to complex, one-of the kind business problems using
computers with human touch.
A core assumption of the expert systems field is the replacement of the human expert by
the system followed by its low-cost duplication and distribution. (Mitchell & Wittink, 1991).
Expert system, thus, draws inferences from human knowledge itself and solves complicated
problems that would otherwise require extensive human expertise. ES has the ability to solve
complex business problems with high degree of accuracy. ESs prosduce quality decisions that
have yielded to better revenue and profit figures for many companies around.
Expert systems are based on “IF-THEN” rules which make them simpler yet powerful
decision making tool. The basic working principle of expert system is shown in Appendix 1. ES
comprises of two major components- Knowledge Base and Inference Engine. Knowledge Base

EXPERT SYSTEMS 9
contains all the necessary information based upon which expert system is supposed to make
decisions. The knowledge base is where human expertise is stored. The Knowledge base
contains detailed information accumulated from external and internal sources while Inference
engine is the generic control mechanism that applies axiomatic knowledge from knowledge base
to task specific data to make decision. When a user supplies fact or relevant information of query
to the system, the ES uses inference engine that uses the knowledge base to match the facts
supplied and to infer the solution. The schematic representation of expert system and its
components is shown in Appendix 2.
1.2 Historical Development of Expert System
The concept of expert system was first coined by Stanford Programming Project led by
Edward Feigenbaum, who is also known as the “Father of Expert System”, in 1965. Expert
Systems have been considered as the most important participant in the Information Revolution.
The researchers at Stanford University, California looked out for domains where expertise was
highly valued and complex. Initially, researchers developed Mycin, an expert system that
diagnosed infectious diseases, and Dendral, a system to identify unknown organic molecules.
Expert System evolved as the successor of General Purpose Problem Solver (GPS)
system which was developed in 1959. However, during mid-1960s, researchers realized that
problem solving was just a small part of complete intelligent system. They began to highlight
role of knowledge in better decision making. At the advent of 1980s, expert systems proliferated
and universities started teaching the course on the same. In 1981, IBM PC with PC DOS
operating system was introduced that created conducive environment for expert system. Expert
systems before 1981 were outliers that required people to develop skills that they were not eager
to acquire. However, with new and advanced PCs, expert systems found a fit with their goals.

EXPERT SYSTEMS 10
A time line showing historical development of Expert System is presented in Figure 1.
Figure 1: Timeline Showing Historical Development of Expert System

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CHAPTER 2
REVIEW OF LITERATURE
Expert systems have remodeled usual business practices by bringing in efficiency and
productivity. ESs have redefined education system as individual experience. Expert system
introduces Intelligent Tutoring Systems that guide students through instructions according to
their individual strengths and weaknesses. (Khanna, Kaushik, & Barnela, 2010) .ITS uses
adaptation technique to personalize lectures with the environment, prior knowledge of the
students and students’ ability to learn. Thus, expert systems are increasingly becoming an
integral part of engineering, accounting, management and other discipline. By rendering
personalized teaching-learning experience, institutions have encashed fair amount of profit and
have created superior institutional value among students.
Weitz (1990) in his study “Technology, Work and the Organization: The Impact of
Expert Systems” has extensively explored the impact of expert system on work and the
organizations. Human beings are biased and have limited information processing capabilities.
Consequently, in cases when businesses have no recourse but to rely upon human judgments,
forecasting may be inappropriate. What is needed is an expert system. (Weitz, 1990, pp. 53).
However, Weitz also contends that the wide spread acceptance and use of expert system could
simply result in small incremental changes in the nature of work and the organization. The
significant exploitation of expert system is, therefore, a necessary but not sufficient condition for
widespread impact on work and organization.
Expert System has also accentuated the pace of Business Process Reengineering.
Business processes today preempt and react quickly to market place changes and competitive

EXPERT SYSTEMS 12
pressure, much of which has been possible through the application of expert system in business
decision making. Today, Expert Systems have demonstrated their potential, gained credibility,
and are being widely used to solve a variety of business problems in industry and government.
(Hayes-Roth & Jacobstein, 1994). However, there are cases when development and
implementation of ES has failed for several reasons, including rejection of the system from target
group communities. (Coats, 1988; Keyes, 1989b; Sloane, 1991.). Yoon, Guimaraes, &
Clevenson (1998) in their study “Exploring Expert System Success for Business Process
Reengineering” tried to explore the reasons behind failure of ES in BPR and one of the
conclusions that they have made in this regard is that user satisfaction is positively correlated
with the benefits from BPR at a significance level of 0.01. This finding hints that expert system
will definitely contribute to business process reengineering provided that the end users are
considered as prized customers by ES development teams.
Markić, Tomić, & Pavlović (2009) have stated in their study “An Expert System
Approach in Stock Selection Attractive for Investment” that knowledge base of expert systems
like EXFIN can help investors classify all the listed companies in one of four categories- most
attractive, attractive, satisfied and unattractive-based upon the analysis of corporate performance
indicators. EXFIN like expert systems, thus, provide key information base for making low-risk
investment decisions to the investors. However, the final decision still lies at the discretion of the
investor.
Jayaraman & Srivastava (1996) have also highlighted the business impact of expert
system in Productions and Operations Management. They have concluded that current
application of expert system in POM areas like Capacity planning, facility location, facility
layout, project management, etc. has enhanced productivity, improved quality, and increased

EXPERT SYSTEMS 13
profits while minimizing costs and capturing expertise in many business and industrial
environment. However, Jayaraman and Srivastava express their concern that ES systems so far
have been used to tackle problems of very small domain, while operations in an organization are
multi-disciplinary. Production requires constant interaction of production department with sales,
marketing, quality experts. Isolated ESs thus cannot solve the problem of manufacturing
manager.
For every business, information happens to be the pivotal asset and expert systems
influence the effectiveness in businesses by protecting the data and information flow. Siems
(1985) has stated about the use of expert system called EDAAS (Expert Disclosure Analysis and
Avoidance System) used by EPA (Environmental Protection Agency) of United States that
controls the information in large databases. This expert system used by EPA works in a cost-
efficient way to ensure the release of only appropriate information to the public and safeguard
the confidential information regarding the sensitive data of manufacturing and distributing
chemicals. (Siems, 1985).
Expert system also contributes in creating better, easy and novel use of social media.
Adetola et. al (2013 ) in their study have stated about the integration of social media and
intelligent systems like expert system to support software development innovation process.
Complicated collaborations with outside parties is eased from connection through the social
media that is backed up by the innovative expert systems that facilitates the rapid decision
making. (Adetola et. al, 2013). Hence, expert system reinforces the usage of social applications
and boosts the applications of social media in businesses to generate better performance for
organizations.

EXPERT SYSTEMS 14
Marketing is often a complicated platform for expert system considering the interaction
it frequently needs to have with the customers. It requires going beyond than just having a well-
defined knowledge base, processing modules and interfaces to make rapid decisions. (Casey &
Murphy, 1994). The significance of expert system, however, lies as supportive role to reinforce
enhanced decision making in marketing that can save costs as well as deliver value outcomes.
Casey & Murphy (1994) have stated through their findings that the real contribution of expert
system lies as it generates marketing expertise for market practitioners to make improved
decision making on various areas of marketing like product pricing, promotion, brand
reinforcement, new product development and many more. With quality output from marketing, it
is a certainty that this will help the businesses to boost up.
The reception of expert system in business and management has created comparative
advantage to exceed ahead. Liang & Ta (1990) have mentioned about the primary three
advantages arriving among the various benefits of expert system stated by various researchers.
Firstly, it retains the scarce expertise that seems to diminish with the retirement of the experts.
Secondly, even the new employees can use expert system to aid the work of experts by using the
knowledge base of expert system. Thirdly, expert system increases the speed of quality and
response of the work as well as augments the reliability of the operations. (Chau, 1991). These
all benefits as a whole directs towards creating strength for the businesses and hence, gaining the
competitive advantage through efficiency and effectivity. Furthermore, expert system has the
capability to create competitive advantage to the businesses through enhanced decision making
by handling ill-structured dilemmas and matching with the existing information systems. (Liang
& Ta, 1990).

EXPERT SYSTEMS 15
CHAPTER 3
SUCCESS AND FAILURE STORIES OF EXPERT SYSTEMS
Expert Systems are widely used in multitude of sectors such as education, marketing and
sales, production and operations management, business process reengineering, health care,
transportation, tourism, and the like. However, the success factor of such systems is again a
function of multitude of causes- company policies, culture, competition, budgetary constraints,
system acceptance, system suitability, system maintenance, etc. In the section below, we present
the gala stories of companies successfully deploying expert system as well as the horror stories
of the companies that have failed miserably due to the use of expert system.
3.1 Success Stories of Expert System
American Express Company is a multinational financial services corporation that is best
known for its facilities like credit card, charge card and traveler’s cheque. Leonard-Barton &
Sviokla (1988) have talked about the use of expert system by American Express. The expert
system was applied to assist credit authorization staff sort data from various databases and then
determine the credit level for each customer. Through the expert system Authorizer’s Assistant
ES employed, American Express makes the necessary recommendation to the concerned party
who is making the significant authorization decision. This helps to make the quick decisions
regarding critical issues like credit allowance for huge purchase.
Next is the context of Indian Agriculture. Use of expert systems like Pesticide Advisor, Indian
Cotton Insect Pest Management and ESMMDM have proved to be boon for the Indian
Agriculture. These expert systems are targeted for the farmers without much knowledge and
experience on using computers and hence are user friendly and simple platforms for farmers.

EXPERT SYSTEMS 16
They enable the concerned parties to protect their crops by tracking the occurrences of diseases
in the crops. (Chakraborty & Chakrabati, 2008).
3.2 Failure Story of Expert System
Mary Kay Inc. is a privately held American company that offers cosmetics and personal
care products. It was founded in 1963 by Mary Kay Ash. Based on 2015 revenues, DSN Global
100 (2016) listed Mary Kay on the sixth position in its DSN 2016 annual list of top-revenue
generating direct selling companies worldwide. The need for expert system in Mary Kay Inc.
initiated with the flaws in decision making like errors in packaging that created huge costs for the
company. (Vedder, Van Dyke, & Prybutok, 2002). For the cosmetic industry, packaging is
valuable to make its products appealing and useful. Vedder, Van Dyke & Prybutok (2002) have
stated about the usage of expert system in Mary Kay instead of traditional heuristic approach to
create expert system automated package that speeded up the decision making by four weeks and
reported no package design flaws. In spite of this success, Mary Kay did not continue to use
expert system and there are various reasons behind this death of expert system. Vedder, Van
Dyke & Prybutok have also mentioned reasoning for the failure to continuously use expert
system that two crucial aspects. Firstly, no one was trained at the company to maintain the expert
system and hence was not able to adapt to the changing rules of expert system. Furthermore,
bringing expert system in Mary Kay was the decision of the immediate manager solely and after
he left, no one really bothered to give their time and efforts to maintain expert system.
Information technologies like expert system change rapidly and demand constant monitoring and
adaptation. Failure to cope these changing requirements of expert system led to the
discontinuation of expert system in Mary Kay.

EXPERT SYSTEMS 17
CHAPTER 4
STATUS OR POSSIBILITIES OF EXPERT SYSTEM IN NEPAL
Nepal is still in the infancy phase of implementing Expert System. While most of the
literatures found pertain to the wide-spread applications of ESs during 1990s in the west, Nepal’s
1990s has just begun. Nepal has expert systems developed for several areas of geotechnical
engineering. CONE classifies soils and infers the shears strength from cone penetrometer data.
RETW ALL helps in choosing applicable retaining wall types for different conditions. Shallow
Trench assists in excavation for shallow trenches by interpreting a new soil classification system
to plan safety precautions. All these expert systems are designed but are either at the stage of
development or operational prototype. Thus, it would not be an under-statement to say that Nepal
is yet to reap warranted benefits of expert systems and that the possibility of expert systems is
immense in Nepalese context. A further detailed explanation of the possibilities of expert
systems in Nepal has been shown below:
1. Agriculture Expert System
According to FAO, GDP from Agriculture in Nepal has been increasing; statistically, it
increased to Rs. 247691.48 million in 2016/17 from 235330.44 million in 2015/16. Nepal being
an agro-based economy, use of expert systems in agriculture can greatly energize agro-practices.
Expert system can be used for pest control and crop protection. An Agriculture Expert System
can predict disease occurrence in the crops and suggest appropriate crop protection and pest
management strategy, without waiting for a human expert to observe and make suggestions. In
India, ESSMDM expert system has been tested for mango planation in North and North Central
area. The researchers have found, on average, 20-50 kg more fruits per plant because of crop
protection and pest management schemes prescribed by expert system. (Chakraborty &

EXPERT SYSTEMS 18
Chakrabati, 2008). FAO also enlists mango as the major cash crop of Nepal. So, an expert
system like ESMMDM can help our country in cultivating healthier and numbered mangoes for
trade. In addition, such expert systems can also provide live weather updates and forecasts
necessary for cultivation, soil quality information and a plantation strategy based on the same
and video-audio feedback for semi-literate audiences.
2. Tourism Development Expert System
Tourism is one of the most promising and appealing service sectors for Nepal. Being
naturally blessed, the country can take good benefits out of its resources from the pool of visitors
adoring adventures. The use of expert system in tourism can create influence to a considerable
extent to attract more flow of visitors and enhance the services provided. For Nepal, the use of
technologies is limited within some online platform promotions and travel agencies facilitating
online booking and arranging the stay in Nepal while the usage of information technologies like
expert system being rare. Owaied, Al-Hawamdeh, & Al-Okialy (2011) have presented a
framework model to make knowledge based system replace the role of tourist guide in order to
provide services to the visitors like giving information about the places, guiding the routes and
making recommendations about the basic needs. This would lessen the problems existing from
the barriers like linguistic barrier, cultural barrier and other personal requirements leading to
increased probability for enhanced services. Use of expert systems like above can help tourism
sector achieve more progress by providing effective service in efficient and innovative way.
3. Hydro-Power Expert System
Nepal is the second richest country in terms of water resource. Our country has huge
potentiality of developing hydro-energy (especially electricity). Various big hydro projects like
Kali Gandaki (144 MW) and Upper Karnali (900 MW) are in operations. Management of such

EXPERT SYSTEMS 19
hydro-projects calls for well-trained and experienced plant operators that are scarce. Hydro-
power expert system basically manages the complexities that arise as a result of increasing size
of power generating equipments. ESs can simulate expert plant operator’s actions and make
judgments/inferences using modern techniques such as fuzzy logic, neural network, and data
acquisition systems. Expert system can reduce work pressure on plant operators through plant
diagnosis and maintenance, data processing, on-line monitoring, sensors based alarm system,
schedule optimization, and operator training and evaluation. The complex sensors and advanced
monitors can diagnose operational condition of hydro-plants and notify in cases of emergency.
4. Defense, Security and Intelligence Solutions
NIC-Asia Bank’s SWIFT breach incident clearly shows how flawed our security systems
are. Many-a-times, websites of our government bodies are hacked by trespassers. With a defense
and security expert system, the issue of security threats can be well-managed. Such systems can
help organizations and government in countering terrorism, cyber security issues, money-
laundering and frauds, network intrusion and malware attacks, etc. Because management of these
issues requires diversity of information, expert systems like Cogito Intelligence Platform (CIP)
can be a great resort for both tactical and strategic defense and intelligence activities. Such
systems can give immediate content visibility, multiple scenarios as data emerge and probable
action-plan when intruders are detected. Nepal does have an expert system based app called
TaxPert (Nepal Tax Expert System) that works as a solution to government for tax issues. Our
country should invest on developing more tax and intelligence expert systems to check
corruption, terrorism, and illegal trespassing to restricted properties.

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5. Education Expert System
Our teaching-learning practices are still primitive. We hardly use the concepts of MOOC
and other emerging learning systems. Expert systems in education in India have proved to be a
successful implementation of technology to provide students with materials that are neither too
easy nor difficult. Expert system like Computer Aided Instruction (CAI), Intelligent Tutoring
Systems (ITS) use expertise of human instructors to provide individual-based learning to
students depending upon their qualification, cognitive abilities, curriculum prescribed, and
individual strengths and weaknesses. Expert systems will help in the replacement of impersonal
teaching-learning with more customized and personal learning practices.
6. Spiritual Meditation
There has been a mounting inclination towards maintaining spiritual well-being among
people in Nepal. With the increasing concern for nursing a sound health by creating peaceful and
pure mind, people are attending various meditation and yoga courses to attain peace of mind.
Hossain, Akte, & Rahaman (2015) have demonstrated about the use of expert system to assess
the meditation level by considering various human sensing factors like sleeping pattern, heart
rate, pulse difference etc. that are usually complex and uncertain. Use of expert system in
abstract realm like meditation generates better results regarding mediation pattern compared to
traditional way of assessment. This can influence a positive impact on improving the assessment
of meditation level that can groom the entire field.
In addition to above mentioned sectors, expert system can effectively be used in
transportation, banking and insurance, manufacturing, and electoral proceedings. Such systems
will simplify operations and bring down costs to create better performing sectors.

EXPERT SYSTEMS 21
CHAPTER 5
CONCLUSION AND RECOMMENDATION
5.1 Conclusion
Business decisions require human-touch in the sense that decisions made by computers
may not always be feasible. However, human beings have limited processing capabilities and
their judgments are often influenced by personal biases and individual perception. Expert
systems, thus, have evolved to create synergies by combining the processing capabilities of
computers and knowledge base of human beings in making informed judgments. Expert systems
simplify operations, increase efficiency, reduce costs, maximize profits, and design action plans
to grasp existing market opportunities and to create new ones. However, business success is not
always the function of expert system alone. Expert system may be necessary but not sufficient
condition for organizational progress. It is necessary to determine the reasons why expert system
can succeed and why it may fail. Expert systems challenge the status quo and oblige people and
organization to upgrade itself. Many-a-times, such system may not be well-received by people
and the application may fail miserably. Organizations, thus, should focus on creating the
acceptance value of expert system so that the system works as intended.
5.2 Recommendations
The following recommendations have been made to drive successful implementation of
expert system in Nepalese organizations:
1. First of all, organizations should understand that expert systems are necessary but not
sufficient conditions for organizational success. The major reason of failure of such system is
resistance to change from employees. Hence, organizations must manage prior training and

EXPERT SYSTEMS 22
grooming session to make employees acquainted with the new working sphere of expert
system.
2. Nepal must focus on developing superior expert systems for its lucrative sectors like:
Tourism, Agriculture, Hydro-power, and Defense. GON should prioritize these sectors and
call for invitation from developers to develop systems meeting specific requirements. GON
can also encourage private sectors to design expert systems for these sectors. The private
sectors can independently sell their systems, while government can earn revenue through
taxes on such systems.
3. It is high time that we upgrade our impersonal education system using expert systems.
Although graduate schools in Nepal have adopted Moodle like platforms to revolutionize
educational practices, education system is still impersonal. Hence, our country should focus
more on IPS and CAI like expert systems to provide individualized educational experience to
students.
4. Nepal should also focus on designing expert systems for the sectors like Spiritual Yoga and
Herbal medicines where we have core competency. By developing expert system for these
areas, we can increase the value proposition by cutting down costs and increasing efficiency
and quality of service/ products.
5. Marketing companies in Nepal should focus on expert systems to do market research and to
make marketing mix decisions. The outcomes of expert systems are based upon vast pool of
market information. So, the degree of accuracy of such outcomes is high and chances of
meeting customers’ requirements increase. Since business functions are interdisciplinary, it is
necessary to create a multi-dimensional ES that interacts and incorporates all necessary
functional areas to make informed decisions about business issues.

EXPERT SYSTEMS 23
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intelligent agents and expert systems for formulating open innovation strategies for
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Chakraborty, P., & Chakrabati, D. (2008). A Brief Survey of Computerized Expert Systems for
Crop Porduction Being Used in India. Progess in Natural Science, 18(4), 469-473.
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Chau, P. Y. (1991, 09 22). Expert systems (Usage) Decision-making Management. SAM
Advanced Management Journal , 56(4).
Coats, P. (1998). Why expert systems fail. Financial Manage., Autumn, 77-86.
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X
Hayes-Roth, F., & Jacobstein, N. (1994). The state of knowledge-based systems. Commun.
ACM, 37(3), 27-39.

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Hossain, M. S., Akte, S., & Rahaman, S. (2015). 2015 International Conference on
Computational Science and Computational Intelligence (CSCI). CSCI. Las Vegas, NV,
USA: IEEE. doi: 10.1109/CSCI.2015.173
Jayaraman, V., & Srivastava, R. (1996). Expert Systems in Porduction and Operations
Management: Current Applications and Future Prospects. International Journal of
Production and Operations Management, 16(12), 27-44.
Keyes, J. (1989b). Why Expert Systems Fail. AI Expert, November, 50-53.
Khanna, S., Kaushik, A., & Barnela, M. (2010). EXPERT SYSTEMS ADVANCES IN
EDUCATION. National Conference on Computational Instrumentation (pp. 109-112).
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Leonard-Barton, D., & Sviokla, J. (1988, March). Putting Expert Systems to Work. Harvard
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Markić, B., Tomić, D., & Pavlović, I. (2009). AN EXPERT SYSTEM APPROACH IN STOCK
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EXPERT SYSTEMS 25
Siems, J. J. (1985). EDAAS: an expert system at the US Environmental Protection Agency for
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15, 179-199.

EXPERT SYSTEMS 26
APPENDIX 1
BASIC WORKING PRINCIPLE OF EXPERT SYSTEM
Expert Systems are comparatively simpler as they are based on “IF-THEN” rules using forward
chaining and backward chaining method. However, modern day expert systems have inference
engines embedded with various new techniques such as:
 Truth Maintenance
 Hypothetical reasoning’
 Fuzzy Logic and Ontology Classification
The major differences between Forward Chaining and Backward Chaining Method have been
presented in the table below:
Forward Chaining Method Backward Chaining Method
 It is based on the question “What can
happen next?”
 Inference engine follows the chain of
conditions and derivations and finally
deduces the outcome.
 It considers all the facts and rules, and sorts
them before concluding to a solution.
 This strategy is used for conclusion and
result.
 Ex: - prediction of share market status as an
effect of changes in interest rates.
 It is based on the question “Why this
happened”?
 On the basis of what has already happened,
the inference engine tries to find out which
conditions could have happened in past for
this result.
 This strategy used to find reason.
 Ex: -diagnosis of blood cancer in humans

EXPERT SYSTEMS 27
APPENDIX 2
SCHEMATIC REPRESENTATION OF EXPERT SYSTEM
User interface can be computers, cellphones or any other means through which user (not
necessarily expert) may input queries and information to the expert system. Considering the
input from the user, Inference engine draws necessary information from the knowledge base that,
in an expert system, is the replica of human knowledge. The inference engine, then, draws
meaningful conclusion and disseminates the output to the user. The user has now option to
accept the decision of expert system or to make his own kind of decision. Thus, expert systems
only give the most probable and the most favorable solution depending upon the user query and
information in its knowledge base. Decision ultimately depends upon the discretion of user.

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