International Journal of Scientific Engineering and Science Volume 2, Issue 5, pp. 19-24, 2018. ISSN (Online): 2456-7361 19 http://ijses.com/ All rights reserved Stored-Knowledge Based Troubleshooting and Diagnosing System Rotimi-Williams Bello 1 , Firstman Noah Otobo 2 1, 2 Department of Mathematical Sciences, University of Africa, Toru-Orua, Bayelsa State, Nigeria Email address: sirbrw@yahoo.com Abstract—Recognizing and detecting system problems by any human intelligent is a complicated process which demands high technical-know- how just as simple problem could take hours or even days to solve. Expert systems use human knowledge often stored as rules within the computer to solve problems that generally would entail human intelligence. This paper proposes a stored-knowledge based troubleshooting and diagnosing system that mimic human intelligence in solving system problems. The system is composed of a user interface, a knowledge-base, an inference engine, and a stored-knowledge based system interface including an intelligent agent that assists in the knowledge acquisition process. The stored-knowledge based system is meant to automate troubleshooting and diagnosis of system problems that ordinarily would have been carried out manually by human intelligence, which is laborious, costly, and time consuming. The developed system used backward chaining approach to infer the rules while the forward chaining approach is left as future work. Keywords— Troubleshooting; Intelligent; Expert system; Rule-base; Inference engine. I. INTRODUCTION Troubleshooting is a strategy by which a technician observes the telltale signals of a malfunctioning system and follows a series of predefined steps to isolate and correct the problem in minimal time. This entails following an algorithmic processes. The series of predefined steps of isolating and fixing the problem can be cumbersome and high in cost. In computer domain for example, the technical-know-how professionals were limited in number this, has resulted in the need to propose systems that can mimic human intelligence in providing solutions to the system problems. Knowledge-based system is one component of an artificial intelligence which is working like human expert but it cannot replace human expert. It supports human experts in making decision, that is, it acts in all respect of human counterpart. Sometimes interchangeably used as intelligent system and is computer software which makes decision like a human expert [1], [2]. The advantage of knowledge-based systems as expert systems over conventional systems is that their core algorithm is not encapsulated in the programming code but stored as knowledge in an independent database called knowledge-base. Due to this reason, knowledge-based systems are not required to be reprogrammed and recompiled as the domain knowledge changes from time to time or from expert to expert knowledge. In real situation, expert systems are applied in different decision making process including medical activities, fault diagnosis, industrial process controlling, climate forecasting, manufacturing failure analysis, decision support, and decision making [3], [4]. According to Pomykalski, Truszkowski and Brown [5], an expert system is a computer program that is designed to imitate the decision-making ability of a decision maker in a particular narrow field of expert knowledge or skill. The specific task of an expert system is to be an alternative source of decision-making ability for organization to use instead of relying on the expert knowledge or skill of few people or just one person. The focus in the development of expert system is to acquire and represent the knowledge and experience of a person who has been identified as possessing the special skill or mastery [5]. The primary intent of expert system technology is to realize the integration of human expert knowledge into computer process. This integration allows humans to be freed from performing the more routine activities that might be associated with a computer-base system. This agreed with Kaushik et al. [6], who acknowledged the ability of artificial intelligence for creating machines that can engage on behaviors that humans consider intelligent. Expert systems have caused revolution in the way we think about work, skill and their possibilities for computerization. Expert system addresses real needs [7]. Knowledge is of central importance to expert system. Data, facts and information are terms used with the meaning of knowledge. The process of building an expert system is commonly known as knowledge engineering. Knowledge engineering implies acquisition of knowledge from a human or other source and coding it into the knowledge base of the expert system [8]. According to Jones and Barrett [9], Expert systems are not suited for all types of problems. Initially, many developers actively sought problems amenable to expert system solution or try to solve all problems encountered using expert system. Expert systems are verified specifically. In the designing process of stored knowledge-based system a lot of knowledge based reasoning mechanisms are there. The well- known reasoning approaches are ontology based reasoning, case based reasoning and rule based reasoning. For the purpose of this research work, a rule-based reasoning approach was discussed as follows: one of the approaches used in knowledge based reasoning technique is rule based reasoning (RBR) approach which is a system whose knowledge representation involves a set of conditions [10]. Symbol dependent rules are the most known reasoning methods and this popularity is mainly due to their naturalness, which facilitates comprehension of the represented knowledge. The conditional statements of the reasoning rules are linked with each other by using logical operators to generate logical functionalities. When sufficient conditions of a rule are satisfied, then the conclusion is derived and the rule is said to be fired. Rule based reasoning was dominantly