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Based on the RTXPS framework, the real-time expert system controls communication with the various actors involved in a situation where environmental and technological risk occur, provides guidance and advice based on several data bases including Material Safety Data Sheets for hazardous substances. It will then simulate the risky situation by various simulation models for the simulation of the evolution of an emergency and then predict the environmental impacts on human’s life. To perform this kind of expert task, the system compiles all necessary input information for the models and performs checks for completeness, consistency, and plausibility. Based on the available information and some simple screening, the most appropriate model or set of models is then triggered, interprets the results, and translates that into guidance and advice for the operators to guide the victim of a risky situation. According to Fedra and Winkelbauer, embedded simulation models include a detailed source model for different release types including pool evaporation, atmospheric dispersion using either a multi-puff, multi-layer Eulerian, or a Lagrangian approach based on a 3D diagnostic wind field model, fire and explosion models, and a stochastic soil contamination routine. The ability of the system to perform real-time controlling and logging of data, user inputs and decisions, model results, and communication activities provide an opportunity to use the system for operational management, training purposes, as well as for planning oriented risk assessment tasks.

Application examples are drawn from the domain of technological risk assessment and management, and particular chemical emergencies in fixed installations or transportation accidents based on ongoing case studies in Italy, Switzerland and Portugal (Fedra 1998; Fedra and Weigkricht 1995). From the example given in their documented report of the system’s development, the source model generates information on the total mass evaporated or directly escaping into the atmosphere and thus available for atmospheric dispersion, the mass fraction infiltrating into the soil, and the probabilities for fire and explosion. Based on these results and their respective probability distributions, the models are triggered in sequence with the most likely or dangerous impact scenario simulated first.

Geographic Information Systems (GIS) And Simulation Models For Water Resources Management

Another example of expert system development in the domain of environmental management is the Geographic Information Systems (GIS) and simulation models for Water Resources Management; a case study of the Kelantan River, Malaysia also by K. Fedra in 2002. Functioned as a water management system, it was called WaterWare, a system that manage information based on a range of linked simulation models that utilize data from an embedded GIS, monitoring data including real-time data acquisition, and an expert system. The system used a graphical interface to provide interactive decision support information for water resources planners and policy makers as it is accessible in a local area network from a central server, and alternatively through the Internet for remote clients.

The development of the system was derived from the major problem occur in Kelantan, specifically the Kelantan river which drains the province of Kelantan in north-eastern peninsular Malaysia. A catchment of about 12,000 km2 (upstream of Guillemard bridge) and an altitude difference of more than 2100 m generates an average runoff of about 500 m3/sec, with the variations of the local Monsoon climate. The variability of rainfall with extreme monthly values between 0 and 1750 mm in dry and wet months, respectively, already suggest the main problem: reliability of water resources for the rice paddies that supply about 12 % of national production. Droughts and floods that affect the efficiency of the irrigation system, continuing changes in land use, and the potential of water pollution from intensive agriculture pose a range of problems that require innovative tools for their solution (Fedra, 2002). So this system was then proposed and eventually developed in the meant of managing the water resources properly, in order to prevent those problems derived from the past situations from happening again.

The system used Geographic Information Systems or GIS to capture, analyse, and display spatial data, while the models provide the tools for complex and dynamic analysis. Input for spatially distributes models, as well as their output, can be treated as map overlays and topical maps (Fedra, 1994). Certainly the system needs to have a convenient interface to spatially referenced data, as well as a familiar format of maps to supports the understanding of model results and this particular system have it all. As an additional for complex and dynamic analysis of the system, simulation, optimisation models and of course expert system are used.

Although expert system is not the heart or particularly used in the development of the WaterWare system, but it offers a rule-based expert system for environmental impact assessment (Fedra et al., 1991). A water resources management system is subject to structural changes such as new reservoirs, or policy changes resulting in a modified water allocation pattern. Any such project or policy change will have a range of environmental impacts, positive or negative. This is where expert system is used, screening the level assessment of such projects, and new reservoirs in particular. As the expert system comes with a set of rules for the evaluation, a checklist of potential problems is used together with it, as well as the data coming from the GIS, the object data base, and model results. To produce a classification of all potential problems that may occur for a given project and environment, the inference engine uses a combination of both forward and backward chaining of the well known rule-based expert system.

So, we have reviewed some of the computer program applications which employ the technology of expert systems, the new phenomenon in the information technology world and have invaded the world of environmental management. With so many kind of data and information in various terms for different problems, the implementation of expert system in the domain of environmental management seems to be really useful and benefiting the whole creatures who live in the environment itself. But it is not denied that there are also some constraints prompting in the midst of the implementation of expert system in environmental problems, which include the issues of reliability, effectiveness and so forth. But these issues have been the issues of almost every domain where expert system was adopted in. So, there should be continuous effort and research on the method in optimising the greatness of expert system in various field and domain, where necessary.

Expert Systems in Medicine

It also seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in medicine (Ledley and Lusted, 1959). With intelligent computers able to store and process vast stores of knowledge, the hope was that they would become a perfect'doctors, assisting or surpassing clinicians with tasks like diagnosis.

In reviewing this new field in 1984, Clancey and Shortliffe provided the following definition:

'Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations.'

Much of the difficulty has been the poor way in which they have fitted into clinical practice, either solving problems that were not perceived to be an issue, or imposing changes in the way clinicians worked. What is now being realized is that when they fill an appropriately role, intelligent programs does indeed offer significant benefits. One of the most important tasks now facing developers of AI-based systems is to characterize accurately those aspects of medical practice that are best suited to the introduction of artificial intelligence systems.

Expert or knowledge-based systems are the commonest type of AIM (Artificial Intelligence in Medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules.

Expert systems can be applied in various tasks of medicine domains:

Generating alerts and reminders. In so-called real-time situations, an expert system attached to a monitor can warn of changes in a patient's condition. In less acute circumstances, it might scan laboratory test results or drug orders and send reminders or warnings through an e-mail system.

Diagnostic assistance. When a patient's case is complex, rare or the person making the diagnosis is simply inexperienced, an expert system can help come up with likely diagnoses based on patient data.

Therapy critiquing and planning. Systems can either look for inconsistencies, errors and omissions in an existing treatment plan, or can be used to formulate a treatment based upon a patient's specific condition and accepted treatment guidelines.

Agents for information retrieval. Software 'agents' can be sent to search for and retrieve information, for example on the Internet, which is considered relevant to a particular problem. The agent contains knowledge about its user's preferences and needs, and may also need to have medical knowledge to be able to assess the importance and utility of what it finds.

Image recognition and interpretation. Many medical images can now be automatically interpreted, from plane X-rays through to more complex images like angiograms, CT and MRI scans. This is of value in mass-screenings, for example, when the system can flag potentially abnormal images for detailed human attention.

Currently there are many systems that have made it into clinical use. Many of these are small, but nevertheless make positive contributions to care. In the next sections, we will examine some of the more successful examples of knowledge-based clinical systems, in an effort to understand the reasons behind their success, and the role they can play.

CaDet
CaDet is a computer-based clinical decision support system for Early Cancer Detection. Cancer risk evaluation and early detection are subject to serious limitations mainly related to human factors and to characteristics of the data involved. To help overcome these problems, a computer-based system was designed to provide the physician with a clearer clinical picture and aid in directing patients to appropriate measures.

Clinical and epidemiological data related to early cancer detection and to cancer risk factors was collected from the literature and incorporated in a database, together with heuristic rules for evaluating this data. Individual data obtained from patients through a questionnaire are input into CaDet, a computerized clinical decision support system. A report summarizing patient data and cancer hypotheses, with a scoring system that reflects degrees of alarm, is generated.

The CaDet systems, as well as some preliminary results of the clinical experience accumulated in its use, are described. These preliminary results suggest that the approach may be useful in improving cancer risk assessment and screening in primary care setups.

DXplain
DXplain is an example of one of these clinical decision support systems, developed at the Massachusetts General Hospital (Barnett et al., 1987). It is used to assist in the process of diagnosis, taking a set of clinical findings including signs, symptoms, and laboratory data and then produces a ranked list of diagnoses. It provides justification for each of differential diagnosis, and suggests further investigations. The system contains a data base of crude probabilities for over 4,500 clinical manifestations that are associated with over 2,000 different diseases.

DXplain is in routine use at a number of hospitals and medical schools, mostly for clinical education purposes, but is also available for clinical consultation. It also has a role as an electronic medical textbook. It is able to provide a description of over 2,000 different diseases, emphasizing the signs and symptoms that occur in each disease and provides recent references appropriate for each specific disease.

Decision support systems need not be 'stand alone' but can be deeply integrated into an electronic medical record system. Indeed, such integration reduces the barriers to using such a system, by crafting them more closely into clinical working processes, rather than expecting workers to create new processes to use them.

Germwatcher
Germwatcher has been developed to assist the Infection Control Departments of Barnes and Jewish Hospitals (teaching hospitals affiliated with the university) with their infection control activities. These activities include surveillance of microbiology cultures data.

Hospital-acquired (nosocomial) infections represent a significant cause of prolonged inpatient days and additional hospital charges. Using a rulebase consisting of a combination of the NNIS criteria and local hospital infection control policy, GermWatcher scans the culture data, identifying which cultures represent nosocomial infections. These infections are then reported to the CDC.

HELP
The HELP (Health Evaluation through Logical Processes) System is a complete knowledge based hospital information system. It supports not only the routine application of an HIS including ADT, order entry/charge capture, pharmacy, radiology, nursing documentation, ICU monitoring, but also supports a robust decision support function.

The HELP system is an example of this type of knowledge-based hospital information system, which began operation in 1980 (Kuperman et al., 1990; Kuperman et al., 1991). It not only supports the routine applications of a hospital information system (HIS) including management of admissions and discharges and order entry, but also provides a decision support function.

The decision support system has been actively incorporated into the functions of the routine HIS applications. Decision support provides clinicians with alerts and reminders, data interpretation and patient diagnosis facilities, patient management suggestions and clinical protocols. Activation of the decision support is provided within the applications but can also be triggered automatically as clinical data is entered into the patient's computerized medical record.

PEIRS
PEIRS (Pathology Expert Interpretative Reporting System) appends interpretative comments to chemical pathology reports (Edwards et al., 1993).

The knowledge acquisition strategy is the Ripple Down Rules method, which has allowed a pathologist to build over 2300 rules without knowledge engineering or programming support. New rules are added in minutes, and maintenance tasks are a trivial extension to the pathologist's routine duties. PEIRS commented on about 100 reports/day. Domains covered include thyroid function tests, arterial blood gases, glucose tolerance tests, hCG, catecholamines and a range of other hormones. PIERS was implemented in the St Vincent's Hospital, Sydney, but is now out of use while a new hospital information system is settling in. Once this is stable, PIERS will need to be interfaced into the system.

Puff
The Puff system diagnoses the results of pulmonary function tests. Puff went into production at Pacific Presbyterian Medical Center in San Francisco in 1977. Several implementations and many thousands of cases later, it is still in routine use. The PUFF basic knowledge base was incorporated into the commercial "Pulmonary Consult" product. Several hundred copies have been sold and are in use around the world. The PUFF system for automatic interpretation of pulmonary function tests has been sold in its commercial form to hundreds of sites world-wide (Snow et al., 1988). PUFF went into production at Pacific Presbyterian Medical Center in San Francisco in 1977, making it one of the very earliest medical expert systems in use. Many thousands of cases later, it is still in routine use.

SETH
The aim of SETH is to give specific advice concerning the treatment and monitoring of drug poisoning. Currently, the data base contains the 1153 most toxic or most frequently ingested French drugs from 78 different toxicological classes.

The SETH expert system simulates expert reasoning, taking into account for each toxicological class, delay, clinical symptoms and ingested dose. It generates accurate monitoring and treatment advice, addressing also drug interactions and drug exceptions.

Between April 1992 and October 1994, 2099 SETH analyzed cases inputted by residents. Since that time three phases of evaluation have been performed. It was concluded that an expert system in clinical toxicology is a valuable tool in the daily practice of a Poison Control Center.

As seen from considering of existing ES's, many of medicine ES's are for the assistance to the physicians in making diagnosing. These ES's may shorten the time to make the correct diagnosis and may reduce the number of diagnostic errors. At the same time, physicians may obtain the information on the symptoms of each of the diseases and pathologic syndromes contained therein.

These circumstances are very important for the countries with large number of population where the number of physicians respecting to 1000 person is limited. It is necessary to take into consideration designing and using of medicine ES's. Thus, researchers have to do their investigations directly on this area.

Conclusion

Expert system in agriculture, education, environmental management and medicine had been through a tremendous phases from simple expert system to the complex multipurpose systems. Hybrid expert system and together with fuzzy expert system can be seen as a new techniques that be used by researchers lately. Implementation of expert system in such fields is greatly influenced by techniques and methods from adaptive hypertext and hypermedia. Features of personalization, user modeling and ability of adaptive towards environment will become great challenges to settle. It can be used as a guideline to promote an expert system in various functions.

In recent years, ES's have been used together with artificial neural networks, fuzzy logic, genetic algorithms and other methods of Artificial Intelligence. These methods allow taking into account their advantages in the designed system and, therefore, new designed systems are more powerful instruments to facilitate various tasks that require instant, accurate and reliable results.

References

Aikins JS, Kunz JC, Shortliffe EH, Fallat RJ., “PUFF: an expert system for interpretation of pulmonary function data.”, Comput Biomed Res. 1983 Jun;16(3):199-208.

Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain. An evolving diagnostic decision-support system. JAMA. 1987 Jul 3;258(1):67-74.

Basri. H, “An expert system for planning landfill restoration”, Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, Water Science and Technology Vol. 37, No. 8, pp 211–217, 1998.
Brusilovsky P. and Gorskaya-Belova T.B. (1992) The Environment for Physical Geography Teaching. Computers and Education, 1992, 18, (1-3), p.85-88. [PDF]

Brusilovsky, P. (1996) Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6 (2-3), pp. 87-129. [PDF]

Brusilovsky, P. (2001) Adaptive hypermedia. User Modeling and User Adapted Interaction, Ten Year Anniversary Issue (Alfred Kobsa, ed.) 11 (1/2), 87-110 [PDF].

Charles K Mann, Stephen R.Ruth, “Expert System in Developing Countries Practice and promise”, 1992.

Compton P, Edwards G, Srinivasan A, Malor R, Preston P, Kang B, Lazarus, L. Ripple, “down rules: turning knowledge acquisition into knowledge maintenance. Artificial Intelligence in Medicine”, 1992;4(6):463-475

Compton P., “A philosophical basis for knowledge acquisition. Knowledge Acquisition”, 1990;2:241-257.

Edwards G, Compton P, Malor R, Srinivasan A, Lazarus L. PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology reports. Pathology 1993;25:27-34

Expert System for Decision Support in Drug Therapy, PHARM-2, http://views.vcu.edu/views/fap/pharm.html

Expert Systems in Medicine, http://amplatz.uokhsc.edu/acc95-expert-systems.html.

Fedra. K, “GIS and simulation models for Water Resources Management: A case study of the Kelantan River, Malaysia”, GIS Development, August 2002.

Fedra, K. and Winkelbauer, L., “A hybrid expert system, GIS and simulation modeling for environmental and technological risk management”, Environmental Software & Services GmbH, 2002.

Feldman MJ, Barnett GO. An approach to evaluating the accuracy of DXplain. Comput Methods Programs Biomed. 1991 Aug;35(4):261-6.

Foltin L.C., The Future of Expert Systems, National Public Accountant 39 (7) July 1994, pp.28-31.

Gardner RM, Pryor TA, Warner HR. The HELP hospital information system: update 1998. Int J Med Inf. 1999 Jun;54(3):169-82.

Harmon P. And Sawyer B., Creating Expert Systems for Business and Industry, John Wiley and Sons: New York, 1990.

Haug PJ, Rocha BH, Evans RS., “Decision support in medicine: lessons from the HELP system.”, Int J Med Inf. 2003 Mar;69(2-3):273-84.

Jay Liebowitz (1989) Expert systems technology for training applications, Educational Technology Volume 29 , Issue 7 Pages: 43 – 45

Jay Liebowitz, Janet S. Zeide (1987) EVIDENT: an expert system prototype for helping the law student learn admissibility of evidence under the federal rules, Computers & Education Volume 11 , Issue 2 Pages: 113 - 120

Jim Prentzas, Ioannis Hatzilygeroudis, C. Koutsojannis (2001) AWeb-Based ITS Controlled by a Hybrid Expert System, Proceedings of IEEE International Conference on Advance Learning Techniques (ICALT'01)

Kahn MG, Steib SA, Dunagan WC, Fraser VJ. Monitoring expert system performance using continuous user feedback. J Am Med Inform Assoc. 1996 May-Jun;3(3):216-23

Kahn MG, Steib SA, Spitznagel EL, Claiborne DW, Fraser VJ. Improvement in user performance following development and routine use of an expert system. Medinfo. 1995;8 Pt 2:1064-7.

Kunz, J.C., R.J. Fallat, D.H. McClung, et. al., "Automated interpretation of pulmonary function test results". Proceedings of Computers in Critical Care and Pulmonary Medicine, IEEE Press, 1979.

Kuperman GJ, Gardner RM, Pryor TA, The HELP System, Springer-Verlag, New York, 1991.

Levine R.L., Drang D.E. and Edelson B, A Comprehensive Guide to AI and Expert Systems, McGraw-Hill, 1986.

Markham H.C, (2001) An internet-based expert system for teaching introductory data structures, Proceedings of the seventh annual consortium for computing in small colleges central plains conference on The journal of computing in small colleges, Pages: 155 – 165

Michael Starek, Mukesh Tomer, Krishna Bhaskar, and Mario Garcia (2002) An Expert System For Mineral Identification, Journal of Computing Sciences in Colleges, Volume 17 , Issue 5 Pages: 193 - 197

Min, F.B.M., (1985); Een expertsysteem gebaseerd op kansberekening; een poging tot ontmythologisering; een 'didactisch voorbeeld'. Computers op school, jaarg. 3, no. 2, (dec. 1985), page 12-19. Retrieved from Rik Min - Expert system ARTS

Nathan M. Reiss and James C. Hofmann (1997) TEACHMET: An Expert System for Teaching Weather Forecastin, Journal of Atmospheric and Oceanic Technology: Vol. 5, No. 2, pp. 368–374. retrieved from AMS Online Journals - TEACHMET: An Expert System for Teaching Weather Forecasting

Nazar M. Zaki and Mohd Daud, “Development of a Computer-Aided System for Environmental Compliance Auditing”, Faculty of Engineering, University Putra Malaysia, Malaysia, Journal of Theoretics, Inc. 2001.

Negnevitsky, M.( 1998) A knowledge based tutoring system for teaching fault analysis, Power Systems, IEEE Transactions on Volume 13, Issue 1, Feb. 1998 Page(s):40 - 45

ONCO-HELP, http://ford.pc-labor.uni-bremen.de/zhao/oncohelp.html

Regers W. et all. Computer Aided Medical Diagnosis; Literature Review, in Proc. 1.Conf. on Artificial Intelligence Applications-IEEE Computer Society, 1984, pp.178-186.

Renate Lippert (1990) Teaching problem solving in mathematics and science with expert systems, Journal of Artificial Intelligence in Education Volume 1, Issue 3 Spring 1990 Pages: 27 - 40

Richard E. Plant, Nicholas D. Stave, “Knowledge based systems in Agriculture”, McGraw-Hill, 1991.

R. L. Hoskinson, J. R. Hess, R. K. Fink, “A Decision Support System for Optimum Use of Fertilizers”, 1992.

Rule-Based ES's in Medicine, http://alpha.cbmi.upmc.edu/courses/fall97/Sep25/ index.htm

Saatchi, M.R.; Ayienga, E.M.; Travis, J.R.; Rippon, F.; (1998) An expert system developed to assist digital electronics teaching, Engineering Science and Education Journal Volume 7, Issue 2, April 1998 Page(s):81 - 87

SETHis an ES for the management on acute drug poisoning, Expert system in clinical toxicology

Snow, M.G., Fallat, R.J., Tyler, W.R., Hsu, S.P., "Pulmonary Consult: Concept to Application of an Expert System", Journal of Clinical Engineering 13:3, pp. 201- 205, 1988.

Victor Ye, (1995) Expert Systems in Computer Animation Production Environments (ESCAPE), Proceedings of the 1st Conference on Computers in Art & Design Education. University of Brighton, 18-21 April 1995, retrieved from http://web.ukonline.co.uk/victor/pub...95/CADE95.html

Vivacqua A., and Lieberman H. (2000) Agents to assist in finding help, Proceedings of the SIGCHI conference on Human factors in computing systems Conference on Human Factors in Computing Systems, Pages: 65 - 72 The Hague :The Netherlands

Weber, G. and Brusilovsky, P. (2001) ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial Intelligence in Education 12 (4), Special Issue on Adaptive and Intelligent Web-based Educational Systems, 351-384. [PDF]

Woodin D.E (2001) Design and Implementation of GungaWeb : An Application of Classical Expert System Technology to the Production of Web-based Commercial Systems, Proceedings of the 8th international conference on Artificial intelligence and law Pages: 104 - 108

XDIS - a simple diagnostic expert system,http://views.vcu.edu/views/fap/xdis.html

Y. F. Chan, H. K. Ma, F. T. Chan, H. Y. Chen, T. Y. Chen (1995) Teaching family Planning with expert system, Computers & Education Volume 24 , Issue 4 Pages: 293 – 298

Zorica Nedic, Vladimir Nedic and Jan Machotka (2002) Intelligent Tutoring System for teaching 1st year engineering, World Transactions on Engineering and Technology Education, Vol.1, No.2, 2002

۱۲-۱۵-۱۳۸۸, ۰۷:۲۱ بعد از ظهر	#2 (لینک دائم)
Astaraki Administrator تاريخ عضويت: خرداد ۱۳۸۷ محل سكونت: تهران-کرج! پست ها: 3,465 تشكرها: 754 16,337 تشكر در 3,127 پست My Mood:	Based on the RTXPS framework, the real-time expert system controls communication with the various actors involved in a situation where environmental and technological risk occur, provides guidance and advice based on several data bases including Material Safety Data Sheets for hazardous substances. It will then simulate the risky situation by various simulation models for the simulation of the evolution of an emergency and then predict the environmental impacts on human’s life. To perform this kind of expert task, the system compiles all necessary input information for the models and performs checks for completeness, consistency, and plausibility. Based on the available information and some simple screening, the most appropriate model or set of models is then triggered, interprets the results, and translates that into guidance and advice for the operators to guide the victim of a risky situation. According to Fedra and Winkelbauer, embedded simulation models include a detailed source model for different release types including pool evaporation, atmospheric dispersion using either a multi-puff, multi-layer Eulerian, or a Lagrangian approach based on a 3D diagnostic wind field model, fire and explosion models, and a stochastic soil contamination routine. The ability of the system to perform real-time controlling and logging of data, user inputs and decisions, model results, and communication activities provide an opportunity to use the system for operational management, training purposes, as well as for planning oriented risk assessment tasks. Application examples are drawn from the domain of technological risk assessment and management, and particular chemical emergencies in fixed installations or transportation accidents based on ongoing case studies in Italy, Switzerland and Portugal (Fedra 1998; Fedra and Weigkricht 1995). From the example given in their documented report of the system’s development, the source model generates information on the total mass evaporated or directly escaping into the atmosphere and thus available for atmospheric dispersion, the mass fraction infiltrating into the soil, and the probabilities for fire and explosion. Based on these results and their respective probability distributions, the models are triggered in sequence with the most likely or dangerous impact scenario simulated first. Geographic Information Systems (GIS) And Simulation Models For Water Resources Management Another example of expert system development in the domain of environmental management is the Geographic Information Systems (GIS) and simulation models for Water Resources Management; a case study of the Kelantan River, Malaysia also by K. Fedra in 2002. Functioned as a water management system, it was called WaterWare, a system that manage information based on a range of linked simulation models that utilize data from an embedded GIS, monitoring data including real-time data acquisition, and an expert system. The system used a graphical interface to provide interactive decision support information for water resources planners and policy makers as it is accessible in a local area network from a central server, and alternatively through the Internet for remote clients. The development of the system was derived from the major problem occur in Kelantan, specifically the Kelantan river which drains the province of Kelantan in north-eastern peninsular Malaysia. A catchment of about 12,000 km2 (upstream of Guillemard bridge) and an altitude difference of more than 2100 m generates an average runoff of about 500 m3/sec, with the variations of the local Monsoon climate. The variability of rainfall with extreme monthly values between 0 and 1750 mm in dry and wet months, respectively, already suggest the main problem: reliability of water resources for the rice paddies that supply about 12 % of national production. Droughts and floods that affect the efficiency of the irrigation system, continuing changes in land use, and the potential of water pollution from intensive agriculture pose a range of problems that require innovative tools for their solution (Fedra, 2002). So this system was then proposed and eventually developed in the meant of managing the water resources properly, in order to prevent those problems derived from the past situations from happening again. The system used Geographic Information Systems or GIS to capture, analyse, and display spatial data, while the models provide the tools for complex and dynamic analysis. Input for spatially distributes models, as well as their output, can be treated as map overlays and topical maps (Fedra, 1994). Certainly the system needs to have a convenient interface to spatially referenced data, as well as a familiar format of maps to supports the understanding of model results and this particular system have it all. As an additional for complex and dynamic analysis of the system, simulation, optimisation models and of course expert system are used. Although expert system is not the heart or particularly used in the development of the WaterWare system, but it offers a rule-based expert system for environmental impact assessment (Fedra et al., 1991). A water resources management system is subject to structural changes such as new reservoirs, or policy changes resulting in a modified water allocation pattern. Any such project or policy change will have a range of environmental impacts, positive or negative. This is where expert system is used, screening the level assessment of such projects, and new reservoirs in particular. As the expert system comes with a set of rules for the evaluation, a checklist of potential problems is used together with it, as well as the data coming from the GIS, the object data base, and model results. To produce a classification of all potential problems that may occur for a given project and environment, the inference engine uses a combination of both forward and backward chaining of the well known rule-based expert system. So, we have reviewed some of the computer program applications which employ the technology of expert systems, the new phenomenon in the information technology world and have invaded the world of environmental management. With so many kind of data and information in various terms for different problems, the implementation of expert system in the domain of environmental management seems to be really useful and benefiting the whole creatures who live in the environment itself. But it is not denied that there are also some constraints prompting in the midst of the implementation of expert system in environmental problems, which include the issues of reliability, effectiveness and so forth. But these issues have been the issues of almost every domain where expert system was adopted in. So, there should be continuous effort and research on the method in optimising the greatness of expert system in various field and domain, where necessary. Expert Systems in Medicine It also seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in medicine (Ledley and Lusted, 1959). With intelligent computers able to store and process vast stores of knowledge, the hope was that they would become a perfect'doctors, assisting or surpassing clinicians with tasks like diagnosis. In reviewing this new field in 1984, Clancey and Shortliffe provided the following definition: 'Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations.' Much of the difficulty has been the poor way in which they have fitted into clinical practice, either solving problems that were not perceived to be an issue, or imposing changes in the way clinicians worked. What is now being realized is that when they fill an appropriately role, intelligent programs does indeed offer significant benefits. One of the most important tasks now facing developers of AI-based systems is to characterize accurately those aspects of medical practice that are best suited to the introduction of artificial intelligence systems. Expert or knowledge-based systems are the commonest type of AIM (Artificial Intelligence in Medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Expert systems can be applied in various tasks of medicine domains: Generating alerts and reminders. In so-called real-time situations, an expert system attached to a monitor can warn of changes in a patient's condition. In less acute circumstances, it might scan laboratory test results or drug orders and send reminders or warnings through an e-mail system. Diagnostic assistance. When a patient's case is complex, rare or the person making the diagnosis is simply inexperienced, an expert system can help come up with likely diagnoses based on patient data. Therapy critiquing and planning. Systems can either look for inconsistencies, errors and omissions in an existing treatment plan, or can be used to formulate a treatment based upon a patient's specific condition and accepted treatment guidelines. Agents for information retrieval. Software 'agents' can be sent to search for and retrieve information, for example on the Internet, which is considered relevant to a particular problem. The agent contains knowledge about its user's preferences and needs, and may also need to have medical knowledge to be able to assess the importance and utility of what it finds. Image recognition and interpretation. Many medical images can now be automatically interpreted, from plane X-rays through to more complex images like angiograms, CT and MRI scans. This is of value in mass-screenings, for example, when the system can flag potentially abnormal images for detailed human attention. Currently there are many systems that have made it into clinical use. Many of these are small, but nevertheless make positive contributions to care. In the next sections, we will examine some of the more successful examples of knowledge-based clinical systems, in an effort to understand the reasons behind their success, and the role they can play. CaDet CaDet is a computer-based clinical decision support system for Early Cancer Detection. Cancer risk evaluation and early detection are subject to serious limitations mainly related to human factors and to characteristics of the data involved. To help overcome these problems, a computer-based system was designed to provide the physician with a clearer clinical picture and aid in directing patients to appropriate measures. Clinical and epidemiological data related to early cancer detection and to cancer risk factors was collected from the literature and incorporated in a database, together with heuristic rules for evaluating this data. Individual data obtained from patients through a questionnaire are input into CaDet, a computerized clinical decision support system. A report summarizing patient data and cancer hypotheses, with a scoring system that reflects degrees of alarm, is generated. The CaDet systems, as well as some preliminary results of the clinical experience accumulated in its use, are described. These preliminary results suggest that the approach may be useful in improving cancer risk assessment and screening in primary care setups. DXplain DXplain is an example of one of these clinical decision support systems, developed at the Massachusetts General Hospital (Barnett et al., 1987). It is used to assist in the process of diagnosis, taking a set of clinical findings including signs, symptoms, and laboratory data and then produces a ranked list of diagnoses. It provides justification for each of differential diagnosis, and suggests further investigations. The system contains a data base of crude probabilities for over 4,500 clinical manifestations that are associated with over 2,000 different diseases. DXplain is in routine use at a number of hospitals and medical schools, mostly for clinical education purposes, but is also available for clinical consultation. It also has a role as an electronic medical textbook. It is able to provide a description of over 2,000 different diseases, emphasizing the signs and symptoms that occur in each disease and provides recent references appropriate for each specific disease. Decision support systems need not be 'stand alone' but can be deeply integrated into an electronic medical record system. Indeed, such integration reduces the barriers to using such a system, by crafting them more closely into clinical working processes, rather than expecting workers to create new processes to use them. Germwatcher Germwatcher has been developed to assist the Infection Control Departments of Barnes and Jewish Hospitals (teaching hospitals affiliated with the university) with their infection control activities. These activities include surveillance of microbiology cultures data. Hospital-acquired (nosocomial) infections represent a significant cause of prolonged inpatient days and additional hospital charges. Using a rulebase consisting of a combination of the NNIS criteria and local hospital infection control policy, GermWatcher scans the culture data, identifying which cultures represent nosocomial infections. These infections are then reported to the CDC. HELP The HELP (Health Evaluation through Logical Processes) System is a complete knowledge based hospital information system. It supports not only the routine application of an HIS including ADT, order entry/charge capture, pharmacy, radiology, nursing documentation, ICU monitoring, but also supports a robust decision support function. The HELP system is an example of this type of knowledge-based hospital information system, which began operation in 1980 (Kuperman et al., 1990; Kuperman et al., 1991). It not only supports the routine applications of a hospital information system (HIS) including management of admissions and discharges and order entry, but also provides a decision support function. The decision support system has been actively incorporated into the functions of the routine HIS applications. Decision support provides clinicians with alerts and reminders, data interpretation and patient diagnosis facilities, patient management suggestions and clinical protocols. Activation of the decision support is provided within the applications but can also be triggered automatically as clinical data is entered into the patient's computerized medical record. PEIRS PEIRS (Pathology Expert Interpretative Reporting System) appends interpretative comments to chemical pathology reports (Edwards et al., 1993). The knowledge acquisition strategy is the Ripple Down Rules method, which has allowed a pathologist to build over 2300 rules without knowledge engineering or programming support. New rules are added in minutes, and maintenance tasks are a trivial extension to the pathologist's routine duties. PEIRS commented on about 100 reports/day. Domains covered include thyroid function tests, arterial blood gases, glucose tolerance tests, hCG, catecholamines and a range of other hormones. PIERS was implemented in the St Vincent's Hospital, Sydney, but is now out of use while a new hospital information system is settling in. Once this is stable, PIERS will need to be interfaced into the system. Puff The Puff system diagnoses the results of pulmonary function tests. Puff went into production at Pacific Presbyterian Medical Center in San Francisco in 1977. Several implementations and many thousands of cases later, it is still in routine use. The PUFF basic knowledge base was incorporated into the commercial "Pulmonary Consult" product. Several hundred copies have been sold and are in use around the world. The PUFF system for automatic interpretation of pulmonary function tests has been sold in its commercial form to hundreds of sites world-wide (Snow et al., 1988). PUFF went into production at Pacific Presbyterian Medical Center in San Francisco in 1977, making it one of the very earliest medical expert systems in use. Many thousands of cases later, it is still in routine use. SETH The aim of SETH is to give specific advice concerning the treatment and monitoring of drug poisoning. Currently, the data base contains the 1153 most toxic or most frequently ingested French drugs from 78 different toxicological classes. The SETH expert system simulates expert reasoning, taking into account for each toxicological class, delay, clinical symptoms and ingested dose. It generates accurate monitoring and treatment advice, addressing also drug interactions and drug exceptions. Between April 1992 and October 1994, 2099 SETH analyzed cases inputted by residents. Since that time three phases of evaluation have been performed. It was concluded that an expert system in clinical toxicology is a valuable tool in the daily practice of a Poison Control Center. As seen from considering of existing ES's, many of medicine ES's are for the assistance to the physicians in making diagnosing. These ES's may shorten the time to make the correct diagnosis and may reduce the number of diagnostic errors. At the same time, physicians may obtain the information on the symptoms of each of the diseases and pathologic syndromes contained therein. These circumstances are very important for the countries with large number of population where the number of physicians respecting to 1000 person is limited. It is necessary to take into consideration designing and using of medicine ES's. Thus, researchers have to do their investigations directly on this area. Conclusion Expert system in agriculture, education, environmental management and medicine had been through a tremendous phases from simple expert system to the complex multipurpose systems. Hybrid expert system and together with fuzzy expert system can be seen as a new techniques that be used by researchers lately. Implementation of expert system in such fields is greatly influenced by techniques and methods from adaptive hypertext and hypermedia. Features of personalization, user modeling and ability of adaptive towards environment will become great challenges to settle. It can be used as a guideline to promote an expert system in various functions. In recent years, ES's have been used together with artificial neural networks, fuzzy logic, genetic algorithms and other methods of Artificial Intelligence. These methods allow taking into account their advantages in the designed system and, therefore, new designed systems are more powerful instruments to facilitate various tasks that require instant, accurate and reliable results. References Aikins JS, Kunz JC, Shortliffe EH, Fallat RJ., “PUFF: an expert system for interpretation of pulmonary function data.”, Comput Biomed Res. 1983 Jun;16(3):199-208. Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain. An evolving diagnostic decision-support system. JAMA. 1987 Jul 3;258(1):67-74. Basri. H, “An expert system for planning landfill restoration”, Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, Water Science and Technology Vol. 37, No. 8, pp 211–217, 1998. Brusilovsky P. and Gorskaya-Belova T.B. (1992) The Environment for Physical Geography Teaching. Computers and Education, 1992, 18, (1-3), p.85-88. [PDF] Brusilovsky, P. (1996) Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6 (2-3), pp. 87-129. [PDF] Brusilovsky, P. (2001) Adaptive hypermedia. User Modeling and User Adapted Interaction, Ten Year Anniversary Issue (Alfred Kobsa, ed.) 11 (1/2), 87-110 [PDF]. Charles K Mann, Stephen R.Ruth, “Expert System in Developing Countries Practice and promise”, 1992. Compton P, Edwards G, Srinivasan A, Malor R, Preston P, Kang B, Lazarus, L. Ripple, “down rules: turning knowledge acquisition into knowledge maintenance. Artificial Intelligence in Medicine”, 1992;4(6):463-475 Compton P., “A philosophical basis for knowledge acquisition. Knowledge Acquisition”, 1990;2:241-257. Edwards G, Compton P, Malor R, Srinivasan A, Lazarus L. PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology reports. Pathology 1993;25:27-34 Expert System for Decision Support in Drug Therapy, PHARM-2, http://views.vcu.edu/views/fap/pharm.html Expert Systems in Medicine, http://amplatz.uokhsc.edu/acc95-expert-systems.html. Fedra. K, “GIS and simulation models for Water Resources Management: A case study of the Kelantan River, Malaysia”, GIS Development, August 2002. Fedra, K. and Winkelbauer, L., “A hybrid expert system, GIS and simulation modeling for environmental and technological risk management”, Environmental Software & Services GmbH, 2002. Feldman MJ, Barnett GO. An approach to evaluating the accuracy of DXplain. Comput Methods Programs Biomed. 1991 Aug;35(4):261-6. Foltin L.C., The Future of Expert Systems, National Public Accountant 39 (7) July 1994, pp.28-31. Gardner RM, Pryor TA, Warner HR. The HELP hospital information system: update 1998. Int J Med Inf. 1999 Jun;54(3):169-82. Harmon P. And Sawyer B., Creating Expert Systems for Business and Industry, John Wiley and Sons: New York, 1990. Haug PJ, Rocha BH, Evans RS., “Decision support in medicine: lessons from the HELP system.”, Int J Med Inf. 2003 Mar;69(2-3):273-84. Jay Liebowitz (1989) Expert systems technology for training applications, Educational Technology Volume 29 , Issue 7 Pages: 43 – 45 Jay Liebowitz, Janet S. Zeide (1987) EVIDENT: an expert system prototype for helping the law student learn admissibility of evidence under the federal rules, Computers & Education Volume 11 , Issue 2 Pages: 113 - 120 Jim Prentzas, Ioannis Hatzilygeroudis, C. Koutsojannis (2001) AWeb-Based ITS Controlled by a Hybrid Expert System, Proceedings of IEEE International Conference on Advance Learning Techniques (ICALT'01) Kahn MG, Steib SA, Dunagan WC, Fraser VJ. Monitoring expert system performance using continuous user feedback. J Am Med Inform Assoc. 1996 May-Jun;3(3):216-23 Kahn MG, Steib SA, Spitznagel EL, Claiborne DW, Fraser VJ. Improvement in user performance following development and routine use of an expert system. Medinfo. 1995;8 Pt 2:1064-7. Kunz, J.C., R.J. Fallat, D.H. McClung, et. al., "Automated interpretation of pulmonary function test results". Proceedings of Computers in Critical Care and Pulmonary Medicine, IEEE Press, 1979. Kuperman GJ, Gardner RM, Pryor TA, The HELP System, Springer-Verlag, New York, 1991. Levine R.L., Drang D.E. and Edelson B, A Comprehensive Guide to AI and Expert Systems, McGraw-Hill, 1986. Markham H.C, (2001) An internet-based expert system for teaching introductory data structures, Proceedings of the seventh annual consortium for computing in small colleges central plains conference on The journal of computing in small colleges, Pages: 155 – 165 Michael Starek, Mukesh Tomer, Krishna Bhaskar, and Mario Garcia (2002) An Expert System For Mineral Identification, Journal of Computing Sciences in Colleges, Volume 17 , Issue 5 Pages: 193 - 197 Min, F.B.M., (1985); Een expertsysteem gebaseerd op kansberekening; een poging tot ontmythologisering; een 'didactisch voorbeeld'. Computers op school, jaarg. 3, no. 2, (dec. 1985), page 12-19. Retrieved from Rik Min - Expert system ARTS Nathan M. Reiss and James C. Hofmann (1997) TEACHMET: An Expert System for Teaching Weather Forecastin, Journal of Atmospheric and Oceanic Technology: Vol. 5, No. 2, pp. 368–374. retrieved from AMS Online Journals - TEACHMET: An Expert System for Teaching Weather Forecasting Nazar M. Zaki and Mohd Daud, “Development of a Computer-Aided System for Environmental Compliance Auditing”, Faculty of Engineering, University Putra Malaysia, Malaysia, Journal of Theoretics, Inc. 2001. Negnevitsky, M.( 1998) A knowledge based tutoring system for teaching fault analysis, Power Systems, IEEE Transactions on Volume 13, Issue 1, Feb. 1998 Page(s):40 - 45 ONCO-HELP, http://ford.pc-labor.uni-bremen.de/zhao/oncohelp.html Regers W. et all. Computer Aided Medical Diagnosis; Literature Review, in Proc. 1.Conf. on Artificial Intelligence Applications-IEEE Computer Society, 1984, pp.178-186. Renate Lippert (1990) Teaching problem solving in mathematics and science with expert systems, Journal of Artificial Intelligence in Education Volume 1, Issue 3 Spring 1990 Pages: 27 - 40 Richard E. Plant, Nicholas D. Stave, “Knowledge based systems in Agriculture”, McGraw-Hill, 1991. R. L. Hoskinson, J. R. Hess, R. K. Fink, “A Decision Support System for Optimum Use of Fertilizers”, 1992. Rule-Based ES's in Medicine, http://alpha.cbmi.upmc.edu/courses/fall97/Sep25/ index.htm Saatchi, M.R.; Ayienga, E.M.; Travis, J.R.; Rippon, F.; (1998) An expert system developed to assist digital electronics teaching, Engineering Science and Education Journal Volume 7, Issue 2, April 1998 Page(s):81 - 87 SETHis an ES for the management on acute drug poisoning, Expert system in clinical toxicology Snow, M.G., Fallat, R.J., Tyler, W.R., Hsu, S.P., "Pulmonary Consult: Concept to Application of an Expert System", Journal of Clinical Engineering 13:3, pp. 201- 205, 1988. Victor Ye, (1995) Expert Systems in Computer Animation Production Environments (ESCAPE), Proceedings of the 1st Conference on Computers in Art & Design Education. University of Brighton, 18-21 April 1995, retrieved from http://web.ukonline.co.uk/victor/pub...95/CADE95.html Vivacqua A., and Lieberman H. (2000) Agents to assist in finding help, Proceedings of the SIGCHI conference on Human factors in computing systems Conference on Human Factors in Computing Systems, Pages: 65 - 72 The Hague :The Netherlands Weber, G. and Brusilovsky, P. (2001) ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial Intelligence in Education 12 (4), Special Issue on Adaptive and Intelligent Web-based Educational Systems, 351-384. [PDF] Woodin D.E (2001) Design and Implementation of GungaWeb : An Application of Classical Expert System Technology to the Production of Web-based Commercial Systems, Proceedings of the 8th international conference on Artificial intelligence and law Pages: 104 - 108 XDIS - a simple diagnostic expert system,http://views.vcu.edu/views/fap/xdis.html Y. F. Chan, H. K. Ma, F. T. Chan, H. Y. Chen, T. Y. Chen (1995) Teaching family Planning with expert system, Computers & Education Volume 24 , Issue 4 Pages: 293 – 298 Zorica Nedic, Vladimir Nedic and Jan Machotka (2002) Intelligent Tutoring System for teaching 1st year engineering, World Transactions on Engineering and Technology Education, Vol.1, No.2, 2002 __________________ تعرفه ارزان تبلیغات در اولین و بزرگترین سایت هوش مصنوعی ایران ! با بیش از صد و چهل هزار کاربر!