Introduction

An expert system is a computer application that solves complicated problems that would otherwise require extensive human expertise. To do so, it simulates the human reasoning process by applying specific knowledge and interfaces. Expert systems also use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Books and manual guides have a tremendous amount of knowledge but a human has to read and interpret the knowledge for it to be used.

A system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise (Turban & Aronson, 2001).

A computer program designed to model the problem solving ability of a human expert (Durkin, 1994).

An intelligent computer program that uses knowledge and inference procedures to solve problems that was difficult enough to acquire significant human expertise for their solutions (Feigenbaum).

Expert systems typically have a number of several components. The knowledge base is the component that contains the knowledge obtained from the domain expert. Normally the way of representing knowledge is using rules. The inference engine is the component that manipulates the knowledge found in the knowledge base as needed to arrive at a result or solution. The user interface is the component that allows the user to query the system and receive the results of those queries. Many ES's also have an explanation facility which explains why a question was asked or how a result or solution was obtained.

There are several major application areas of expert system such as agriculture, education, environment, law manufacturing, medicine power systems etc. In this article we will review about agriculture, education, environment and medicine expert system. These four applications widely use among the practitioners due to the maturity of the field by revealing the acceptance of the technology by the commercial sectors.

Expert System in Agriculture

It is no different with other Expert System, the Expert System for Agriculture is same as others knowledge based system, its use the rule based which the experience and knowledge of a human expert is captured in the form of IF-THEN rules and facts which are used to solve problems by answering questions typed at a keyboard attached to a computer on such diversified topics, for example, in pest control, the need to spray, selection of a chemical to spray, mixing and application, optimal machinery management practices, weather damage recovery such as freeze, frost or drought, etc."

The Development of Expert Systems for Agriculture
The early state of developing the Expert Systems are in the 1960s and 1970s were typically written on a mainframe computer in the programming language based on list Processing (LISP). Evolving from university research laboratories, they were limited to the applications developed by these research sites. Most of these expert systems were not intended for commercial use.

They incorporated the specific knowledge of the experts. About the problem area termed "domain knowledge.” Problem-solving heuristics (or "rules of thumb") and inferences capabilities, and an interface mechanism between the user and the system. Some examples of these systems include MACSYMA, developed at the Massachusetts Institute of Technology (MIT), for assisting individuals in solving complex mathematical problems: Standford University’s MYCIN, which diagnosed bacterium and meningitis infections, which is the first diagnostic Expert System ever developed ever and the University of Pittsburgh's INTERNIST/CADUCEUS, which aided internal medicine diagnosis and decision making. These scientists created a general-purpose tool for developing expert systems now called a "shell".

Expert Systems Are Used To Aid
The rises of the agriculture expert system are to help the farmers to do single point decisions, which to have a well planning for before start to do anything on their land. Secondly is to design an irrigation system for their plantation use. Third is to select the most suitable Crop variety or market outlet. Fourth is Diagnosis or identification of the livestock disorder. Fifth is to interpret the set of financial accounts. Sixth is to predict the extreme events such as thunderstorms and frost. And lastly is to suggest a sequence of tactical decisions throughout a production cycle such as plant protection and nutrition decisions, livestock feeding and the like.

An Expert System for a Particular Decision Problem Can Be Used
The expert system can be used as a stand alone advisory system for the specific knowledge domain perhaps with monitoring by a human expert. It also can provide decision support for a high-level human expert. The agriculture expert system also allows a high-level expert to be replaced by a subordinate expert aided by the expert system. The main purposes the rises of the expert system are as a delivery system for extension information, to provide management education for decision makers (farmers), and for dissemination of up-to-date scientific information in a readily accessible and easily understood form, to agricultural researchers, advisers and farmers. By the help of the expert system, the farmers can produce a more high quality product to the citizen.

The Advantages of the Expert System
There are few advantages that the expert system being use in the agriculture field. First, it has the ability to imitate human thought and reasoning. Second, the expert system makes modification of knowledge very convenient. Third, it has the ability of interpretation and transparence makes interaction more user friendly. Fourth, with the machine learning technique knowledge can be acquired automatically and directly from experimental data and real time examples and helps to provide the right information which is timely and actionable. Sixth, it can provide expert level recommendations understandable to users (farmers). And lastly, it has the ability to handle uncertain information.

Some Agricultural Expert systems
Rice-Crop Doctor

National Institute of Agricultural Extension Management (MANAGE) has developed an expert system to diagnose pests and diseases for rice crop and suggest preventive/curative measures. The rice crop doctor illustrates the use of expert-systems broadly in the area of agriculture and more specifically in the area of rice production through development of a prototype, taking into consideration a few major pests and diseases and some deficiency problems limiting rice yield.

The following diseases and pests have been included in the system for identification and suggesting preventive and curative measures. The diseases included are rice blast, brown spots, sheath blight, rice tungro virus, false smut fungi, bacterial leaf blight, sheath rot and zinc deficiency disease. The pests included are stem borers, rice gall midge, brown plant hopper, rice leaf folder, green leaf hopper and Gundhi bug.

Indian Institute of Horticultural Research Institute, Bangalore
The first software for use by the grape cultivators was prepared by the Indian Institute of Horticultural Research Institute, Bangalore. This spontaneous response made them to undertake similar software for providing guidance to mushroom cultivators, which became extremely popular and a large number of growers using it regularly for getting solutions to their problems. The Institute has launched into an effort to give a comprehensive package of practices of about 148 horticulture crops for cultivation in the 4 Southern states of Kerala, Tamilnadu, Karnataka and Andhra Pradesh.

AGREX

Center for Informatics Research and Advancement, Kerala has prepared an Expert System called AGREX to help the Agricultural field personnel give timely and correct advice to the farmers. These Expert Systems find extensive use in the areas of fertilizer application, crop protection, irrigation scheduling, and diagnosis of diseases in paddy and post harvest technology of fruits and vegetables.

Farm Advisory System

Punjab Agricultural University, Ludhiana, has developed the Farm Advisory System to support agri-business management. The conversation between the system and the user is arranged in such a way that the system asks all the questions from user one by one which it needs to give recommendations on the topic of farm Management.

Computer Assisted Agriculture through Distributed Knowledge Based Expert System
There are three levels in which the basic process of agriculture is happening. 1. Low level farmers. 2. Middle level agricultural officers. 3. Higher level research institutions. Each level requires a data depending upon its requirements with interrelationships among them. The farmers interface is designed in such a way that the communication between the expert system and the farmer will be in the farmers own native language. The knowledge base acts as a bridge between farmers and research institutions. The production systems at farmers’ level knowledge base which are not able to find an answer are formed as unanswered dynamic framers and tried for solution by agricultural officers. These frames are transferred to research institutions with the same additional view of points of agricultural officers. Hence research institutions can come to know about the new undiscovered problems that exist at farmers’ level.

Expert Systems for Cotton Crop Management
This Expert System has been developed by the US Department of Agriculture to provide appropriate management recommendations to cotton growers.

CALEX
This is a blackboard based integrated expert decision support system for agricultural management, developed at University of California. CALEX can be used by growers, pest control advisors, consultants and other managers.

VARIEX
This expert system developed at Technical University of Brno, Czechoslovakia enables selection of the best cultivators for different agricultural situations.

Weiping Jin Expert System

There are fertilizing, irrigating, spraying insecticide process and adopting other measures in crop management, which rely on crop state analysis. CMES provides support for crop growth control system (CGCS), i.e., advises growers about optimal population and structure of crop in planting stage and when and what to adopt measures on their crop to keep at optimal state, to avoid Infestation in various stage of growth and development of crop, and finally to obtain the highest crop productivity in harvest stage

LEY Expert Systems
A RF-telemetry based computer-controlled, automated, remote, real-time weather data acquisition and reporting system in Washington State is described. Cooperation among Washington State University the National Weather Service and the U.S. Bureau of Reclamation and several private grower organizations have made this system possible. Data is collected, processed and transmitted to the NWS hourly. These hourly updates of actual conditions are broadcast on the NOAA weather band during the spring frost season to assist fruit growers with frost protection. Real-time weather data are also being used in applications such as irrigation scheduling, crop protection and pest management.

The program is divided into four main modules: frost protection strategies, operational management, forecasting, and instrumentation.. It is being designed to be an addition to other commodity management expert systems that are commercially available or are under development.

Using a unique satellite downlink facility at by R.R. Getz Auburn University, real time meteorological data are being processed on a network of advanced computer workstations. This data feeds a series of computer models that generate site specific predictions of temperature, dew point, wet bulb temperature, and other parameters used to alert Alabama fruit, vegetable, and nursery growers of freezes.

GIS Expert Systems by Naiqian Zhang
The expert system software WHEATWIZ was developed in 1987 as an effective tool to assist Kansas farmers, extension workers, and agri-business personnel in variety selection for hard red winter wheat (Shroyer et al., 1987). The software offered three modes of operation.

The concept of "prescription farming" or precision application of agronomic inputs may dramatically increase the attention a farmer gives to the spatial distribution of nutrients in the soil and methods of fertilizer application. By using a geographic information system (GIS) and a cotton model, GOSSYN, together as a spatial crop simulation, a methodology for investigating the implications of incorporating prescription farming programs for cotton production is investigated. The data contained in the GIS, the result of intensively sampling a cotton field for nitrate information, is used as the primary input to the model. Hydrologic profile information is also mapped using the GIS and used as input to the model. The model is run for each unique combination of inputs. Crop response is mapped back the GIS for spatial analysis. Information pertinent to prescription farming programs which can be extracted from the spatial simulation is discussed. Precision application of agronomic inputs may necessitate increased soil sampling and increased complexity of the technology on tractors and fertilizer applicators. However, this may also prove to increase crop yields, as well as to provide a degree of environmental protection from excessive use of chemicals.

CLIPS Expert System
A prototype alfalfa (Medicago sativa L.) management CLIPS (C Language Integrated Production System) expert system has been developed by Purdue University agricultural scientists. This form of artificial intelligence provides an extension tool, which will enable farmers to very economically use a computer program to reach conclusions concerning profitable alfalfa production that normally would require consultation with a forage expert. To date, the management considerations and the sequence in which they were built into the knowledge base are: soil drainage, soil pH, soil P test, soil K test, and use of alfalfa crop, chemical weed control, and expected longevity of stand, variety recommendation, method and rate of seeding and pure live seed. The knowledge base of these CLIPS expert system can easily be updated, as new information becomes available or can be modified for use in other states or farming regions.

DSS4Ag

The Decision Support System for Agriculture (DSS4Ag) is an expert system being developed by the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. DSS4Ag uses state-of-the-art artificial intelligence and computer science technologies to make spatially variable, site-specific, economically optimum decisions on fertilizer use.

The DSS4Ag has an open architecture that allows for external input and addition of new requirements and integrates its results with existing agricultural systems’ infrastructures. The DSS4Ag reflects a paradigm shift in the information revolution in agriculture that is precision farming. Four test plots showed that the tested fertilizer management methodologies, while applying very different fertilizer rates, produced crops of similar yield and quality. The plots also demonstrated the DSS4Ag’s ability to characterize and identify management zones of common and different productive potential.

The Expert system must be developed in local languages which will help the Farmers to develop their own expertise which in turn will enhance the production and productivity of Agriculture. Expert systems must be available in village booths which act as information resource center for the farmers in the villages.

Expert Systems in Education

In education field, many of the expert system’s application are embedded inside the Intelligent Tutoring System (ITS) by using techniques from adaptive hypertext and hypermedia. Most of the system usually will assist student in their learning by using adaptation techniques to personalize with the environment, prior knowledge of student and student’s ability to learn.

In term of technology used, expert system in education has expanded very consistently from microcomputer to web based (Woodin D.E, 2001) and agent-based expert system (Vivacqua A., and Lieberman H., 2000). By using web-based expert system, it can provide an excellent alterative to private tutoring at anytime from anyplace (Markham H.C, 2001) where Internet is provided. Also, agent based expert system surely will help users by finding materials from the web based on the user’s profile. Supposedly, agent expert system should have capability to diagnose the users and giving the results according to the problems.

Besides technology used, expert system also had a tremendous changes in the applying of methods and techniques. Starting from a simple rule based system; currently expert system techniques had adapted a fuzzy logic (Michael Starek, Mukesh Tomer, Krishna Bhaskar, and Mario Garcia ,2002) and hybrid based technique (Jim Prentzas, Ioannis Hatzilygeroudis, C. Koutsojannis , 2001).

Needs for Expert Systems in Education
According to Markham H.C (2001), expert system are beneficial as a teaching tools because it has equipped with the unique features which allow users to ask question on how, why and what format. When it used in the class environment, surely it will gave many benefit to student as it prepare the answer without referring to the teacher. Beside that, expert system is able to a give reasons towards the given answer. This features is really great as it can make students more understand and confident with the answer.

Ability of expert system to adaptively adjust the training for each particular student on the bases of his/her own pace of learning is another feature that makes expert system more demanding for students. This feature is used in (Zorica Nedic, Vladimir Nedic and Jan Machotka, 2002) for teaching engineering students. It should be able to monitor student’s progress and make a decision about the next step in training.

Application of Expert System in Education
Expert system had been used in several fields of study including computer animation (Victor Yee, 1995), computer science (Heather Christine Markham, 2001), engineering (Zorica Nedic, Vladimir Nedic and Jan Machotka, 2002), language (Expert System in Language Teaching), and business study. For Computer Animation Production, expert system been used as a guide to developer to design 2D and 3D modeling package. Other than that expert system also be used as a tool in teaching mathematic related subject (Micheal Kristopeit,). This paper would present the application of expert system in teaching introductory data structure; follow by application in engineering, technology and earth science.

Expert System for Teaching Introductory data Structure
This expert system had used Internet technology as a medium to access the information. This expert system had been developed by using CLIP as an inference engine, and HTML program as a front page for the system. According to (Markham, 2001), this expert system had provided the excellent alternative to the private tutorial. Since this expert system is developed using Java technology, thus make this system interoperable and independent platform.

Expert System for Engineering
This expert system using fuzzy logic method as an engine to enable this system operates adaptively. This expert system was developed to help first year engineering student gain deep understanding of fundamentals to be able to follow the more advanced topics in the engineering fields. This ITS will help adaptively adjust the training for each particular student on the base on his/her pace of learning. ITS will monitor the student’s progress and have the ability to make decision about the next step in training.

Figure 1 below show the architecture of ITS for teaching engineering student which was embedded expert system inside. This expert system using fuzzy rule based decision making system that would guide the ITS’s behaviour. For each student, this expert system will draw the information regarding student’s performance against the membership function for each topic, difficulty and importance level.



Figure 1: Structure of ITS to teach engineering student. Adapted from Zorica Nedic, Vladimir Nedic and Jan Machotka (2002)

Expert System for Learning Internet
According to (Jim Prentzas, Ioannis Hatzilygeroudis, C. Koutsojannis, 2001), hybrid expert sister lab been developed to assist teacher in learning new technologies such as Internet. They had build web based Intelligent Tutoring System (ITS) for teaching new technologies to high school teacher. Figure 2 below is example of architecture that been developed for this ITS.




Figure 2: Architecture of ITS. Adapted from Jim Prentzas, Ioannis Hatzilygeroudis, C. Koutsojannis(2001)

This architecture had made used of expert system’s knowledge representation formalism based on neurules, a type of hybrid rules integrating symbolic rules with neurocomputing. This neurules will improve the performance of symbolic rules and simultaneously retain naturalness and modularity.

Expert System for Teaching Fault Analysis
Application of expert system also been used by lecturer to teach student on subject relating fault analysis. By using this expert system, lecturer aim to make learning more productive and efficient without increasing the staff number. This ITS was developed by using Leonardo expert system shell, object oriented tool for developing expert system application.



Figure 3: Basic components of Leonardo expert system shell. Adapted from Negnevitsky, M. (1998)

Figure 3 above shown the architecture of Leonardo expert system shell. This architecture was used Negnevitsky, M. (1998) to develop expert system for teaching fault analysis. According to Negnevitsky, M. (1998) this Leonardo tutoring system is a very useful tool for teaching fault analysis in power system. In October 1994, the system was installed in a computer network and it can now be accessed from any computer in the Department of Electrical and Electronic Engineering. It has been found that network delivery of computer-based tutorials is the most cost effective (Negnevitsky, M., 1998).

Expert System For Mineral Identification
This expert system developed to be used for support the teaching of mineral properties at college level and hence to promote effective and meaningful learning of scientific observation in earth science. This system used by the college students, who may or may not have n-depth computer skills. An expert system building tool which can be easily maintained by people from non-computer science background. EXSYS (EXSYS inc. 1994) was used to build this expert system. EXSYS is a commercial expert system building tool that has been in the market for several years. It is easy to use, easy to learn and easy to maintain. EXSYS can explain why and how it reaches a conclusion.

Expert Systems in Environmental Management

The most successful application of Artificial Intelligence (AI) so far is the development of Decision Support System (DSS), particularly expert system, which is a computer program that act as a ‘consultant’ or ‘advisor’ to decision makers (Wash, 1999). Expert system has been a new dimension of human’s view of life where everything seems to be easy and more useful by employing expert system. Thus, the application of expert systems technology in the domain of environmental management is particularly appropriate in order to assist human in their attempt to preserve and disseminate valuable expertise efficiently and at reasonable costs. Nowadays, there have been numbers of expert system application on environmental management domain including those which are still in the development process as well as some newly potential proposed system.

Computer-Aided System For Environmental Compliance Auditing
One of the potential expert system applications in the domain of environmental management is the computer-aided system for environmental compliance auditing which was proposed by Nazar M. Zaki and Mohd Daud from the Faculty of Engineering, University Putra Malaysia. The system was actually a cost effective integrated environmental monitoring system for Environmental Impact Assessment (EIA) project as well as environmental database management system. At that time in Malaysia, 19 different category projects require Environmental Impact Assessment (EIA) reports duly approved by Department of Environment (DOE) before their implementation.

According to the Environmental Quality Order 1987, Malaysia, every development project has some potential environmental impact, thus requires a duly approved Environmental Impact Assessment (EIA) report before implementation of that project. Environmental Impact Assessment guidelines of Department of Environment (DOE) Malaysia recommended that a matrix be used to relate the different project activities at different phases of the project such as exploration, development, operation, and rehabilitation etc. to the physio-chemical, Biological, and human or Socio-economic environment. Once the possible environmental impacts are assessed, the project initiator must identify and indicate the possible mitigation measures to be taken with a purpose of controlling the environmental pollution and keeping the environment safe and healthy. During the assessment, compliance auditing comes into picture to check whether and how far the project is complying with environmental protection and standards while it is under implementation. In fact, an environmental compliance audit is a management tool comprising the systematic, documented, periodic and objective examinations of how well environmental organization management and equipment are performing, with the aim of helping to safeguard the environment (Nazar and Daud, 2001).

From here we can see that compliance auditing plays a vital role in environmental management. Therefore, the integration of Geographic Information Systems (GIS), Expert Systems, and some other statistical packages and database software and microcomputers in the development of the computer-aided system for environmental compliance auditing might help in improving the management efficiency.

Hybrid Expert System, GIS And Simulation Modelling For Environmental And Technological Risk Management
One more examples of expert system in environment domain is the hybrid expert system, GIS and simulation modelling for environmental and technological risk management particularly called RTXPS and developed by K. Fedra and L. Winkelbauer from Environmental Software & Services GmbH. The system was actually an integration of a real-time forward chaining expert system and a backward chaining system as the Decision Support System framework using simulation models and Geographic Information Systems or GIS. RTXPS was based on the results from the international research project called HITERM which was funded under the European ESPRIT technology programme for high-performance computing and networking (HPCN) for decision support.

The HITERM project integrates high-performance computing on parallel machines and workstation clusters with a decision support approach based on a hybrid expert systems approach. To integrate the various information resources in an operational decision support system, a flexible client-server architecture is used (Figure 1), based on TCP/IP and http. The central system, which runs the RTXPS expert system as the overall framework is connected to a number of (conceptual) servers that provide high-performance computing and data acquisition tasks, as well as a number of clients that include mobile clients in the field (Fedra and Winkelbauer, 2002).


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