Selection of expert system shells for traffic signal design

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

In this paper we analyse the explanation facilities of several expert systems using a framework based on factors relevant to the structure and content of a generated explanation. We compare this with the lack of such facilities in expert system shells, and lay out criteria for an explanation facility within an expert system shell for education. We then describe their realisation in the explores (explanation oriented expert system) shell, where domain-independent templates allow questions to be asked about the domain of any knowledge base being consulted. In response, the shell generates domain-based explanations, with further information available through follow-up questions related to the original question, the generated explanation, and the available knowledge. This facility allows a user to explore and learn about a knowledge domain, and has implications for the use of expert system shells in education.

An expert database system is an expert system which is developed on databases using DBMS technology to manage facts and rules. Frame-based expert systems are widely used as the knowledge representation for such expert systems with large knowledge bases. Many systems have the ability to connect to external databases. Facts stored in databases can be loaded into expert system's knowledge base and inference is performed by the inference engine of the expert system. In many cases, such external facts are required several times for each inference. Thus, a lot of communication traffic takes place. This research work present the design and implementation of a frame-based object-relational database system which has a tight coupling between the expert system and the external knowledge base. The external knowledge base also use frame as its knowledge representation. More over, it has its own inference engine so that inference can be perform on the knowledge base side and the results, not only simple facts, are sent back to the expert system for further inference. A medical consultation system is used as an illustrated example an Oracle lOg object relational DBMS is used as the database platform.

An expert system for selecting the methods for retrofitting fatigue cracking in steel bridges is developed. The expert system involves a knowledge base with new knowledge representations and a new inference engine. The knowledge is based on 90 cases of fatigue cracking in existing steel bridges, and a knowledge base is constructed with knowledge being represented as production relations. All the relations in the knowledge base are assumed to have a certainty factor. The new inference engine was developed in the C language so that it has the ability to learn by using the knowledge‐based network model. The inference engine is able to modify relations with a certainty factor. Therefore, the knowledge‐based network model can grow into an optimum knowledge‐based network. The present system is evaluated for the actual fatigue cracking in three steel bridges. It is found that the system is able to infer reasonable methods for retrofitting cracked members.

The use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the "gold standard" of day 21 progesterone. The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.

An integrated expert decision support system for agricultural management

A Relational Database Interface for Design Expert Systems

Nowadays, expert systems being used in various fields have received a great deal of attention. Auditing is one such field, along with determining the audit opinion type. An expert system consists of a knowledge database and an inference engine. The objective of this research is to make an expert system that will be of help to auditors in predicting and determining the different types of audit reports. The expert system receives data or knowledge from financial reports and determines the types of audit opinions by using an artificial neural network and a decision tree as an inference engine. An expert system should be able to explain the solution, but presenting the reason for the results obtained with a neural network is difficult. This study attempts to provide a method that will present simple and understandable reasons for the results obtained with neural networks.

REX: Robotics expert systems building tool

The purpose of this study was to develop an Expert System (ES) to evaluate Nuclear Power Plant (NPP) operators. The study included a review of the Artificial Intelligence (AI) literature relevant to the NPP and human resource management, knowledge acquisition, knowledge representation, knowledge encoding, and Inference engine. An expert system development tool FuzzyCLIPS6.1 was used to develop a fuzzy rule-based expert system called NPPOEX. NPP operator records obtained from a Chinese NPP were entered into NPPOEX and the system produces a crisp value for Evaluation in the range [0,1], a certainty factor and brief explanations for the result.

A web-based multi-models expert system called DCDDS is presented in this paper, which developed for diagnosis of dairy cow diseases through the symptoms submitted by users on web. As it is accepted that the inference engine and the relevant knowledge representation are the crucial part of diagnosis expert system, which limits its application and popularization in animal disease diagnosis. To break the limit and raise accuracy, this paper compares and appraises the existed systems and presents a solution that contains three models—Case-based reasoning (CBR), Subjective Bayesian theory and D-S evidential theory. Accordingly a knowledge representation method which can support the three different models is also designed. Up to the complicacy of the group of symptoms users acquired, they can choose which of the three models should be adopted to meet the best resolve. The performance of the proposed system was evaluated by an application to the field of dairy cow disease diagnosis using a real example of dairy cow diseases. The result indicates that the new methods have improved the inference procedures of the expert systems, and have showed that the new architecture has some advantage over the conventional architectures of expert systems on both efficiency and accuracy.

Expert systems are built to solve a wide range of problems in domains such as medicine, science, engineering, manufacturing, education, and many other fields. A Rule-Based Expert System has the following components: the knowledge base, the inference engine, and a user interface that communicates with users. A Decision Tree is a hierarchical data structure consisting of nodes, which store information or knowledge, and edges, which connect the nodes. In this paper, we discuss that the inference or reasoning process of Expert System can be easier to identify by a decision tree structure through a forward reasoning inference or a backward reasoning inference. Forward reasoning inference is data-driven; its reasons for facts to find out what solutions (or conclusions) follow from the facts. However, backward reasoning inference is goal-driven, which needs to find facts that support the hypothesis. A small animal classification example will be discussed. For learning expert system programming effectively, we introduce two software tools and discuss their advantages. Firstly, CLIPS (C Language Integrated Production System) is discussed. CLIPS was designed using C programming language at the NASA/Johnson Space Center with the specific purpose of providing high portability, low cost, and easy integration with external systems. Secondly, an interactive logic programming tool, Visirule is discussed; VisiRule was offered by LPA, a small, private UK company specializing in practical Artificial Intelligence. Finally, we propose a Rubric for Outcome Assessments for learning expert system programming with decision Trees for anyone who is interested in practical expert system programming.

Rule-based systems are relatively easier to explain to end users because rules can be expressed as if/else conditions. Indeed, at times there are multiple if/else conditions. Expert systems are deterministic in nature and very accurate, because they are driven by well-established rules and conditions. The need for explainability arises when multiple rules either conflict or are combined in such a way that it is difficult to interpret. In the field of artificial intelligence, an expert system is a program designed to simulate a real decision making process. Expert systems are designed to solve complex problems by simple reasoning and are represented as if/else/then conditions. Expert systems are designed to act like an inference engine. The role of an inference engine is to make decisions. Sometimes expert systems are simple, like a decision tree, and sometimes expert systems are very complex. Complex expert systems require a knowledge base. Sometimes these knowledge bases are an ontology, which is a form of a body of knowledge, through which a particular rule can be framed and inferencing can be done.

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) is responsible for the safe operation and mission success of manned space flights. The MITRE Corporation, working with MOD, is developing requirements for the Operations Management System (OMS) to automate many aspects of flight control for the Space Station onboard systems. To help develop these requirements, the Integrated Status Assessment (ISA) expert system prototype was built to perform Station-wide failure diagnosis. This paper describes the OMS, how the ISA prototype was built, and how the prototype is being used to help define the OMS.Currently the Space Shuttle is managed on the ground using a hierarchy of control personnel. A flight director responsible for the overall Shuttle mission, a number of front room flight controllers are each responsible for a different system, and many back room controllers support various front room controllers. This system has been used since the early days of space flight and is very manpower intensive. A similar hierarchy is seen in an automated system for the Space Station. The back room controllers can be augmented with smart components and sensors on the Station and the front room controllers can have their jobs aided with expert systems. At the highest level is the OMS which works at the flight director level getting summary information from the various systems in the Station.The OMS will include onboard automation, ground based automation, onboard manual operations, and ground based operations. One of the functions of the OMS is to perform a Station-wide status assessment and failure diagnosis of the Station. This Station-wide view is very important because all of the systems interact with each other and a failure in one system will have impacts on the other systems.It became evident early in discussion with to the MOD flight controllers that prototypes would be very helpful in the process of gathering requirements for the OMS. The prototypes were used to gather additional requirements, to expose people to new ideas and technologies, and study the feasibility of these technologies for systems management.Because the task of assessing the status of space vehicles is a complex job that requires “expert” knowledge to find heuristic solutions to problems, an expert system approach was chosen to prototype the ISA system. The initial domain of the expert system was the KU band portion of the communications and tracking system (one of the onboard “core” Space Station Systems).The ISA prototype expert system consists of a knowledge base, an inference engine, and a user interface. The ISA system was designed as a hybrid expert system using different methodologies: object-oriented programming, rule-based programming using both shallow and deep reasoning, and qualitative modeling.The ISA prototype has been very successful in its goals. ISA has demonstrated new technology and ideas to the NASA operations community, helped define requirements for the OMS, and shown the feasibility of using hierarchical expert systems to monitor complex systems. The ISA prototype, along with other OMS prototypes, are being integrated in a test bed environment with other space station prototypes to form the Space Station Information System (SSIS) End-to-end Test Capability (ETC) which will have components all over the world. The results of the ETC will influence the Space Station design and be a test environment for software and hardware for years to come.

So far, many knowledge representation methods have been discussed. Each method has its own advantages and disadvantages, so it is not possible to introduce a specific method as the best knowledge representation technique. We introduce a unique knowledge base, E/sub x/-OAV KB. As the verification and validation (V&V) of knowledge in any AI systems is important, we propose a new user friendly tool, TKT-OAV, to transform the knowledge representation method of any expert system such as semantic network or frame to the E/sub x/-OAV KB. By using this tool, we could also draw and edit the semantic network or frame and construct the knowledge base. This tool uses the standard algorithms that have been designed by C. Dadkhah and A. Ahmad (2004) for transforming the knowledges. The transformed knowledges are shown in three forms: OAV triple, OAV hierarchy and OAV table. Finally the knowledge base which its knowledges are represented in OAV triple is created. Any expert system shell can use this KB for reasoning and the verification and validation tools like OAV/spl I.bar/VVT expert, could also verify and validate this knowledge base.