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...d in this Expert System is based on the Centers for Disease Control and Prevention “Sexually Transmitted Diseases Treatment Guidelines – 2006”. This Expert System is web-based and asks a series of questions to the user. There could be 100 questions but the Expert System usually asks about 20. A challenge in these Expert Systems is Conflict Resolution. If two or more rules in the Interference engine gave a high output, which one will you fire. Few techniques were attempted to solve this problem [9]. One of them was the Means End Analysis (MEA). It was first introduced in General Problem Solver [10]. This search process over the problem space aims to combine aspects both forward and backward reasoning. This approach focused problem-solving on the actual differences between the current state and that goal or calculate the difference between each available option and the goal state. A disadvantage of this is if used in an Expert System is the definition of a goal state. The rules were that goal state that was defined will be fired without consideration to the unfired rules which still have a chance to be the Possible Cause (PC). This paper will present a new Conflict Resolution Model for E...

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...a set of Symptoms. In return, the user will still have a coarse time in doing diagnostic procedures. A solution to this problem is to apply a Conflict Resolution model. This paper presents a Conflict Resolution model using Logic Scoring of Preference (LSP) to be used in an Expert System for Information System Communication Networks. This can aide the user in diagnostic procedures and reduce trial and error. Keywords: Conflict Resolution, Data Management, Expert Systems, Information Systems. I. INTRODUCTION Artificial intelligence is maybe the most important technical contributor in the future [1]. Systems that can make jobs or processes faster are advantageous. An example of an Artificial Based System is an Expert System. This type of system simulates the judgment and behaviour of a human or an organization that has expert knowledge and experience in a particular field or intends to embody the ability and knowledge of an expert in a specific domain [2]. In this era of fast growing Technology and Communication systems, it is very important for corporations and industries to develop Support Division environment technologies to facilitate and diagnose its own Information System Communica...

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...agnostic procedures and reduce trial and error. Keywords: Conflict Resolution, Data Management, Expert Systems, Information Systems. I. INTRODUCTION Artificial intelligence is maybe the most important technical contributor in the future [1]. Systems that can make jobs or processes faster are advantageous. An example of an Artificial Based System is an Expert System. This type of system simulates the judgment and behaviour of a human or an organization that has expert knowledge and experience in a particular field or intends to embody the ability and knowledge of an expert in a specific domain [2]. In this era of fast growing Technology and Communication systems, it is very important for corporations and industries to develop Support Division environment technologies to facilitate and diagnose its own Information System Communication Network. The integration of a Helpdesk Contact Center in the organizations structure is of vital importance to a large corporation. Employees of the company are the customers of the division. They facilitate the technical concerns of the employees and diagnose them at the soonest possible time to minimize the delay in operations. For this research Informat...

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...s. For this research Information System Communication Networks refers to the internal technical backbone of an organization solving problems that relate to Information and Communications Technology (ICT), computers and their networking, hardware, firmware, and software applications. The Conflict Resolution Model that was developed will be applied in an Information System Communications Network division of a telecommunications company. II. EXAMPLE OF DEVELOPED EXPERT SYSTEMS One of the earliest developed Expert Systems which were developed in the early 1970 at Stanford University was the MYCIN [3]. It was the Doctoral Dissertation of Dr. Edward Shortliffe and was written in LISP. This Expert System was programmed to identify the bacteria that can cause severe infections and to recommend antibiotics. This expert system uses an Interference Engine that is composed of six hundred rules. The system will query the physician running the program with a long series of text questions. The result will be a list of possible bacteria that is responsible for the disease. The system will also give a recommended method of treatment. A research conducted by Stanford Medical School found that MYCIN giv...

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...e of 65% which was beneficial to most physicians who ISSN (Print) : 2320 – 3765 ISSN (Online): 2278 – 8875 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 8, August 2013 Copyright to IJAREEIE www.ijareeie.com 3835 are not specialists in that field. This Expert System was never used in practice not because of its weakness in performance but due to its ethical and legal issues on the use of Expert Systems in Medical Diagnosis. Another example of developed Expert Systems is the Caduceus [4]. It was an Expert System developed by Harry Pople in the University of Pittsburg. It started in the early 1970’s. It took a long time to finish and was based on his interviews with Dr. Jack Meyers, one of the top internal medicine diagnosticians. Their intent was to improve the MYCIN Expert System. Instead of focusing on internal medicine, this Expert System can possibly diagnose 1000 diseases. Another Expert System that was created is the MEXSES [5]. This program is a prototype for environmental screening. This software draws a number of Environmental Impact Assessment Techniques in an attem...

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...e to its ethical and legal issues on the use of Expert Systems in Medical Diagnosis. Another example of developed Expert Systems is the Caduceus [4]. It was an Expert System developed by Harry Pople in the University of Pittsburg. It started in the early 1970’s. It took a long time to finish and was based on his interviews with Dr. Jack Meyers, one of the top internal medicine diagnosticians. Their intent was to improve the MYCIN Expert System. Instead of focusing on internal medicine, this Expert System can possibly diagnose 1000 diseases. Another Expert System that was created is the MEXSES [5]. This program is a prototype for environmental screening. This software draws a number of Environmental Impact Assessment Techniques in an attempt to combine the most appropriate elements into one intelligent, comprehensive and knowledge-driven. This Expert System manages a database of project assessments and a geographical database of maps and geo-referenced data. This system provides potential environmental impacts in an early stage. The Expert System draws on broad knowledge and databases on project characteristics based on a hierarchical classification scheme and generic profiles. The sys...

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...abase of project assessments and a geographical database of maps and geo-referenced data. This system provides potential environmental impacts in an early stage. The Expert System draws on broad knowledge and databases on project characteristics based on a hierarchical classification scheme and generic profiles. The system’s knowledge base is limited to a few examples of checklists and rules for one problem class. An additional example of a developed Expert System is IMPACT. It is an Expert System used for environmental analysis screening tool that was developed by the US Department of Energy [6]. This software uses a simplified approach to assess the different kinds of impacts like proximity effects, groundwater effects and water pollution. The software gives its results by giving a tabular written report. By year 2001, NASA’s Artificial Intelligence Group Information Systems Technology Section created the Spacecraft Health Inference Engine (SHINE). This system was designed to operate in a non-simulated environment and to be utilized using C and C++ programming languages [7]. Its knowledge base systems include automatic task planning, diagnosis and other applications which require di...

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...Expert System used for environmental analysis screening tool that was developed by the US Department of Energy [6]. This software uses a simplified approach to assess the different kinds of impacts like proximity effects, groundwater effects and water pollution. The software gives its results by giving a tabular written report. By year 2001, NASA’s Artificial Intelligence Group Information Systems Technology Section created the Spacecraft Health Inference Engine (SHINE). This system was designed to operate in a non-simulated environment and to be utilized using C and C++ programming languages [7]. Its knowledge base systems include automatic task planning, diagnosis and other applications which require different software modules based on artificial intelligence concepts and advanced programming techniques. This knowledge base can also be customized to meet the user’s own problemsolving representations and techniques. A publicly available Expert System was developed in 2006 by the Expert Health Data Programming called the STD Wizard [8]. It is used for determining which vaccinations, screening tests and evaluations are recommended which are related to sexually transmitted diseases. The...

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...aluations are recommended which are related to sexually transmitted diseases. The information included in this Expert System is based on the Centers for Disease Control and Prevention “Sexually Transmitted Diseases Treatment Guidelines – 2006”. This Expert System is web-based and asks a series of questions to the user. There could be 100 questions but the Expert System usually asks about 20. A challenge in these Expert Systems is Conflict Resolution. If two or more rules in the Interference engine gave a high output, which one will you fire. Few techniques were attempted to solve this problem [9]. One of them was the Means End Analysis (MEA). It was first introduced in General Problem Solver [10]. This search process over the problem space aims to combine aspects both forward and backward reasoning. This approach focused problem-solving on the actual differences between the current state and that goal or calculate the difference between each available option and the goal state. A disadvantage of this is if used in an Expert System is the definition of a goal state. The rules were that goal state that was defined will be fired without consideration to the unfired rules which still have...

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...his is the first time that it was used in an Expert System. It was used to give a weight to the ones an Expert System has suggested to what the most probable cause. This model was used on the rules an Expert System. The algorithm on how to incorporate it from website ranking to Quandary weighting for expert systems was originally developed by the author Different input parameters may give different probable causes of problems. As in real applications, given different symptoms, a human expert may have different suggestions to a problem and give percent sureness to might have caused the problem [11]. The Conflict Resolution Model algorithm will give the weight. This algorithm can be used in formulating the relationships of the symptoms of the problems, in which symptoms have sub symptoms with different credence relationships. Table I Symbols and Parameters of the ANDOR Function [12] Operation Symbol d Disjunction D 1.0000 Strong QD(+) D++ 0.9375 Strong QD D+ 0.8750 Strong QD(-) D+- 0.8125 Medium QD DA 0.7500 Weak QD(+) D-+ 0.6875 Weak QD D- 0.6250 Square Mean SQU 0.6232 Weak QD(-) D-- 0.5625 Arithmetic Mean A 0.5000 Weak QC(-) C-- 0.4375 Weak QC C- 0.3750 Geometric Mean GEO 0.3333 Weak Q...