Expert system technology

The expert system is a computer program which attempts to reproduce the problem-solving behavior of an expert, who is able to view problems from a broad perspective and arrive at conclusions rapidly, using intuition, shortcuts, and analogies to previous situations. Expert systems are a departure from the usual artificial intelligence approach to problem solving. Researchers have traditionally tried to develop general modes of human intelligence that could be applied to many different situations. Expert systems, on the other hand, tend to rely on large quantities of domain specific knowledge, much of it heuristic. The reasoning component of the system is relatively simple and straightforward. For this reason, expert systems are often called knowledge based systems. The report expands on the foregoing. Section 1 discusses the architecture of a typical expert system. Section 2 deals with the characteristics that make a problem a suitable candidate for expert system solution. Section 3 surveys current technology, describing some of the software aids available for expert system development. Section 4 discusses the limitations of the latter. The concluding section makes predictions of future trends

Autonomous power expert system

The Autonomous Power Expert (APEX) system was designed to monitor and diagnose fault conditions that occur within the Space Station Freedom Electrical Power System (SSF/EPS) Testbed. APEX is designed to interface with SSF/EPS testbed power management controllers to provide enhanced autonomous operation and control capability. The APEX architecture consists of three components: (1) a rule-based expert system, (2) a testbed data acquisition interface, and (3) a power scheduler interface. Fault detection, fault isolation, justification of probable causes, recommended actions, and incipient fault analysis are the main functions of the expert system component. The data acquisition component requests and receives pertinent parametric values from the EPS testbed and asserts the values into a knowledge base. Power load profile information is obtained from a remote scheduler through the power scheduler interface component. The current APEX design and development work is discussed. Operation and use of APEX by way of the user interface screens is also covered

Autonomous power expert system

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling and dynamic replanning

Expert System Application for Diagnosis of Engine Damage of 4- Clutch Motor

In scientific writing, the author discusses the development of Expert System,which is one field of artificial intelligence techniques in the computer field. Herethe author uses the method of production functions that included rules inpredicate calculus as a way of knowledge representation is applied in TurboProlog programming language, where the application is used to look for damagethat may occur in 4-stroke motorcycle engine in accordance with existing traits.Turbo Prolog programming language is a form of declarative programminglanguage developed in the early 1970s by Alain Colmerauer from the Universityof Marseilles in France.Expert Systems with Applications made to Diagnose Damage Motor 4-strokeengine is expected to develop existing theory base significantly, so that thelearning is more easily understood with the success of this Expert Systemapplications and can help users who do not know the motor in detail about themotor 4 - not to get consideration before taking further action if there is damageto their motor

Revised Irrigation Design Expert System for Grapes

The purpose of irrigation scheduling is to determine the exact amount of water to apply to the field and the exact timing for application. The amount of water applied is determined by using a criterion to determine irrigation need and a strategy to prescribe how much water to apply in any situation

Irrigation Design Expert System for Mang

The purpose of irrigation scheduling is to determine the exact amount of water to pply to the field and the exact timing for application. The amount of water applied is determined by using a criterion to determine irrigation need and a strategy to prescribe how much water to apply in any situation

Expert system for campus environment indexing in wireless sensor network

Wireless sensor network can deliver environment data in campus area as CO, NO2, HC, particulate matter, temperature, humidity, and luminous intensity to provide accurate realtime data. This realtime environment data is used for environment indexing accurately, and then can be developed in an expert system. This expert system collects input data from the sensor. This expert system will help giving the accurate input for campus authority to state and evaluate campus developing policy continuously. This expert system uses forward chaining method, PHP programming language and MySQL database

Expert System Development Methodology (ESDM)

The Expert System Development Methodology (ESDM) provides an approach to developing expert system software. Because of the uncertainty associated with this process, an element of risk is involved. ESDM is designed to address the issue of risk and to acquire the information needed for this purpose in an evolutionary manner. ESDM presents a life cycle in which a prototype evolves through five stages of development. Each stage consists of five steps, leading to a prototype for that stage. Development may proceed to a conventional development methodology (CDM) at any time if enough has been learned about the problem to write requirements. ESDM produces requirements so that a product may be built with a CDM. ESDM is considered preliminary because is has not yet been applied to actual projects. It has been retrospectively evaluated by comparing the methods used in two ongoing expert system development projects that did not explicitly choose to use this methodology but which provided useful insights into actual expert system development practices and problems

Fertilization Design Expert for Grapes

The purpose of this system is to present a generic fertilization design for crops. This system have been applied on grape

Expert system application education project

Artificial intelligence (AI) technology, and in particular expert systems, has shown potential applicability in many areas of operation at the Kennedy Space Center (KSC). In an era of limited resources, the early identification of good expert system applications, and their segregation from inappropriate ones can result in a more efficient use of available NASA resources. On the other hand, the education of students in a highly technical area such as AI requires an extensive hands-on effort. The nature of expert systems is such that proper sample applications for the educational process are difficult to find. A pilot project between NASA-KSC and the University of Central Florida which was designed to simultaneously address the needs of both institutions at a minimum cost. This project, referred to as Expert Systems Prototype Training Project (ESPTP), provided NASA with relatively inexpensive development of initial prototype versions of certain applications. University students likewise benefit by having expertise on a non-trivial problem accessible to them at no cost. Such expertise is indispensible in a hands-on training approach to developing expert systems