Abstract--Drought forecasting and prediction is a complicated process due to the complexity and scalability of the environmental parameters involved. Hence, it required a high level of expertise to predict. In this paper, we describe the research and development of a rule-based drought early warning expert systems (RB-DEWES) for forecasting drought using local indigenous knowledge obtained from domain experts. The system generates inference by using rule set and provides drought advisory information with attributed certainty factor (CF) based on the user's input. The system is believed to be the first expert system for drought forecasting to use local indigenous knowledge on drought. The architecture and components such as knowledge base, JESS inference engine and model base of the system and their functions are presented. The intricate complexity of drought has always been a stumbling block for drought forecasting and prediction systems . This is mostly due to the web of environmental events (such as climate variability) that directly/indirectly triggers this environmental phenomenon. There are six broad categories of drought: meteorological, climatological, atmospheric, agricultural, hydrologic and water drought . Nevertheless, irrespective of the category of drought, there is a consensus amongst scientist that drought is a disastrous condition of lack of moisture caused by a deficit in precipitation in a certain geographical region over some time period .
This section focuses on "Expert System" in Artificial Intelligence. These Multiple Choice Questions (mcq) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations. Explanation: Expert System introduced by the researchers at Stanford University, Computer Science Department. Explanation: Expanding is not Capabilities of Expert Systems. Explanation: The components of ES include: Knowledge Base, Inference Engine, User Interface.
Expert systems in Artificial Intelligence are a prominent domain for research in AI. It was initially introduced by researchers at the Stanford University, and were developed to solve complex problems in a particular domain. The following topics will be covered through this blog on Expert Systems in Artificial Intelligence. An Expert system is a domain in which Artificial Intelligence stimulates the behavior and judgement of a human or an organisation containing experts. It acquires relevant knowledge from its knowledge base, and interprets it as per the user's problem. The data in the knowledge base is essentially added by humans who are experts in a particular domain.
Internet and expert systems have offered new ways of sharing and distributing knowledge, but there is a lack of researches in the area of web based expert systems. This paper introduces a development of a web-based expert system for the regulations of civil service in the Kingdom of Saudi Arabia named as RCSES. It is the first time to develop such system (application of civil service regulations) as well the development of it using web based approach. The proposed system considers 17 regulations of the civil service system. The different phases of developing the RCSES system are presented, as knowledge acquiring and selection, ontology and knowledge representations using XML format. XML Rule-based knowledge sources and the inference mechanisms were implemented using ASP.net technique. An interactive tool for entering the ontology and knowledge base, and the inferencing was built. It gives the ability to use, modify, update, and extend the existing knowledge base in an easy way. The knowledge was validated by experts in the domain of civil service regulations, and the proposed RCSES was tested, verified, and validated by different technical users and the developers staff. The RCSES system is compared with other related web based expert systems, that comparison proved the goodness, usability, and high performance of RCSES.
Artificial Intelligence Department, Computer Resenrch Laboratory, Tektronix, 1, Post Office Box 500, Beaverton, Oregon 97077 Getting started on a new knowledge engineering project is a difficult and challenging task, even for those who have done it before. For those who haven't, the task can often prove impossible. One reason is that the requirementsoriented methods and intuitions learned in the development of other types of software do not carry over well to the knowledge engineering task. Another reason is that methodologies for developing expert systems by extracting, representing, and manipulating an expert's knowledge have been slow in coming. At Tektronix, we have been using a step-by-step approach to prototyping expert systems for over two years now.