International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 1– No.7, March 2012 – www.ijais.org 11 Web based Fuzzy Expert System and Its Applications – a Survey Maitri Patel Assistant Professor, Smt. Chandaben M. Patel Institute of Computer Applications CHARUSAT Changa, India Paresh Virparia Associate Professor, G.H.Patel Department of Computer Science & Technology S.P.University, V.V.Nagar, India Dharmendra Patel Assistant Professor, Smt. Chandaben M. Patel Institute of Computer Applications CHARUSAT Changa, India ABSTRACT The extensive use of the Internet for data collection, information and knowledge has become a popular activity. Expert system, which provide consultation along with reasoning are more beneficial when made available on the World Wide Web. Expert systems are basically of two types (i) Conventional and (ii) Fuzzy logic based systems. Conventional expert systems are mainly symbolic reasoning engines and very complex in nature while fuzzy expert systems are oriented towards numerical processing which handles even uncertain or imprecise information. The paper presents a comprehensive literature review of recent work in the area of expert systems; precisely web based fuzzy expert systems over the last two decades. This paper discusses application areas of web based fuzzy expert system extensively. General Terms Artificial Intelligence, Expert System Keywords Web based Expert System, Fuzzy Logic, Symbolic Reasoning, Conventional Expert Systems. 1. INTRODUCTION Artificial Intelligence is a computer science domain involving the study and development of computer systems that demonstrate some form of intelligence: systems that learn new concepts and tasks, systems that can reason and draw useful conclusions about the world around us, systems that can understand a natural language or perceive and comprehend a visual scene, and systems that perform other types of feats that require human types of intelligence. Irrefutably the AI systems address thought processing and reasoning or the behaviour. Developing functional computer systems that are proficient in executing tasks which require high levels of intelligence is the main objective of AI. It is not essential for the programs emulate human senses and thought processes. The programs may exceed the human abilities if the tasks are performed in a well-organized and particular manner. The important thing is that the systems should be capable of performing intelligent tasks effectively and efficiently. A better understanding of AI can be achieved by looking at the component areas of study. 1.1 Task Domains of AI AI can be applied for the problem solving in wide variety of task domains as in [5]. For instance, AI can be used in analyzing physical objects and their relationships as well as reasoning about actions and their effects. Such problems can be categorized as commonsense reasoning. Perceptual tasks which include vision and speech are difficult because they involve analog signals, the signals are typically very noisy and usually a large number of things must be perceived at once. AI can also be used in engineering design, scientific discovery, medical diagnosis and financial planning. Such specialized tasks require carefully acquired expertise. The Figure 1 depicts some of the task domains of artificial intelligence. This paper mainly focuses on the expert tasks of artificial intelligence. The second section describes the concept of expert systems in details. The third section denotes the application areas of web based fuzzy expert system. The fourth and final section gives the conclusion of this paper. 2. EXPERT SYSTEMS The problem areas where AI is now flourishing most as a practical discipline are primarily the domains that require specialized expertise without the assistance of commonsense knowledge. A large number of programs called expert systems are utilized in day-to-day operation throughout almost all the areas – may it be industry or government. Each of these systems attempts to solve part, or whole of the practical, significant problem that previously required scarce human expertise. It has been proved effectively that the expert systems can solve problems in various domains where human expertise is required. Few such domains are law, chemistry, biology, engineering, manufacturing, aerospace, military operations, finance, banking, meteorology, geology and geophysics. An expert system can be defined as a set of programs that use the human expertise as knowledge which is stored in an encoded form and may manipulate it to solve problems in a specialized domain. An expert system’s knowledge must be coded and stored in the form which the system can use in its reasoning processes performed by the inference engine. The main sources of expert knowledge are the experts themselves and other sources - such as texts, journals, articles and databases. To obtain this type of knowledge, a lot of training and experience in specialized is required. After obtaining the expert knowledge, it should be encoded and stored in the knowledge base. Thereafter, it should be tested and enhanced continuously throughout the system life.