Deep versus compiled knowledge approaches to diagnostic problem-solving

Edamame grade is determined based on edamame quality parameters with several parameters that are quite a lot. The process of deciding edamame grade is relatively complicated and challenging. To determine the edamame grade, an experienced expert in the field is needed. With limited grading experts, the design of expert systems for grading edamame can help users or companies to determine edamame grade. This study aims to design an expert system for determining edamame grade, which can determine the quality of edamame accurately. The Design of an expert system in this research uses the forward chaining method. The stages of designing an expert system include problem identification, knowledge acquisition, building a data flow diagram, compile the database, building a decision tree diagram, and developing a user interface. The results showed that the expert system has been verified and validated well. Verification test results show that all the menu buttons on the expert system application are functioning correctly. Then, the validation results also show that the edamame quality output produced by the expert system is compatible with the expert solution, meaning that the expert system's output data is by following with the results of the decision given by the expert. In testing the validation of determining the quality is appropriate. The results of the verification and validation of the edamame grading expert system show that the expert system has produced satisfactory outcomes. Expert system output is compatible with the solution provided by the expert.

This paper describes how a new method for answer justification in abductive diagnostic expert systems, presented in a preceding companion paper (Part I), can be supported in a domain-independent fashion. Both the issues of explaining why a disorder is included in a differential diagnosis and why it is ranked the way it is relative to its "competitors" are addressed. This approach to answer justification is then compared to previous work on answer justification in medical expert systems.

Preliminary research on applying a novel control scheme to a medical expert system is presented. The solution of most diagnosis problems requires reasoning at multiple abstraction levels. Adaptive abstraction (AA) is a control scheme that enables an expert system to switch automatically to an appropriate level of abstraction to solve the task at hand. The overall knowledge organization, the control mechanisms, and the knowledge base update method of the AA scheme are described along with examples in automated gait analysis. The advantages of the AA scheme are mentioned. Several theoretical issues concerning the design of expert systems are raised, including: determination of a knowledge base's granularity, the downward inclusion property, which is needed for the AA scheme to work, and the necessity of common-sense knowledge for expert systems to overcome brittleness. >

In Memoriam: Dr. Clive L. Dym (1942–2016)

SASHA is a model-driven application shell which generates expert systems for fault isolation and repair in complex electronic systems. SASHA incorporates an expert system shell and a knowledge acquisition tool into a unique mechanism that elicits data directly from the domain expert and outputs a rule-based diagnostic expert system.SASHA employs causal-model and rule-based representations. It uses a causal model to identify the structure, behavior and relation of a domain object. It uses rules to direct the diagnostic problem solver. SASHA's initial knowledge base consists of a domain independent generalized model of the system under analysis. This underlying model drives knowledge acquisition and the consequent generation of an expert system. SASHA introduces a new concept of Pattern Rules, which are a generalized skeleton of the diagnostic problem solver. During diagnosis, when applied to the domain specific Structural Knowledge base, Pattern Rules dynamically instantiate the actual domain rules. Based on the Pattern Rules notion, three types of rule base organization are proposed and analyzed: Fully Patternized Organization (FPO), Partly Patternized Organization (PPO) and Customized Domain-specific Organization (CDO).

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

The importance of clinical problem solving, i.e., clinical diagnostic reasoning, particularly in the CCU, is essential and may be lifesaving. All too often, the diagnostic (Dx) approach consists of reflex assumptions, e.g., lung infiltrates must be due to pneumonia, then empiric therapy for the presumed Dx follows. Even if the infiltrates represent pneumonia, the appropriate diagnostic reasoning response should be to develop a clinical syndromic diagnosis. This narrowed differential diagnosis permits a focused diagnostic workup rather than a non-clue directed shot gun testing. Non-focused testing is not only unnecessary and wasteful, as it is unhelpful, but may be harmful to the patient. Excessive unrelated testing misdirects the workup to focusing on incidental laboratory or imaging findings. Abnormal tests, unrelated to the patient’s present CCU problem, generates more testing. Further testing takes time and often results in potentially deleterious delays in initiating optimal treatment. Such non-directed or misdirected occurrence on CCU rounds. A clue focused diagnostic workup requires a thoughtful diagnostic approach based on clinical experience. In this introductory chapter, the process of clinical diagnostic reasoning is explained and illustrated via two common clinical scenarios, i.e., acute bacterial meningitis and severe commonly-acquired pneumonia. The key elements of clinical diagnostic reasoning are presented in tabular form.

This work focuses on the construction of an expert system shell for airborne equipment design. Traditional expert systems are constructed using a single monolithic software program for a specific application. Since the airborne equipment design is critical & complex, it demands the use of expert system technology. Since present aircrafts take in different equipments for different purposes, it is not feasible to think in terms of independently developed monolithic expert system program for each equipment. To overcome these problems, we have designed and developed a complex competent system i.e., a generic component based expert system for airborne equipment design. The main advantage of the component-based approach is that, the knowledge base is not hard coded and these components can be coupled with any domain-specific knowledge bases. The expert system with its expertise knowledge is made to guide the designer by providing design guidelines & testing procedures for the desired equipment. The expert system also audits the design and provides guidelines to modify & to improve the design.

Morphometry of the lymphoid cells was performed with a computerized image analyzer. Database formation was performed automatically using our own new software. The initial database of calculated mean values, of constructed histograms, of regression, and of discriminant analysis was transformed into a set of morphometric features. Eight morphometric features were selected for characterizing two separate populations of the lymphoid cells: one from the lymph node tissue with a malignant tumor and the other without any tumor (control). The eight features ensured the least overlap between the two groups of lymphoid cells. The eight features formed the basis of the decision criteria for identifying a patient with a malignant pathology. A weighting coefficient was calculated for each feature. It was ascertained that the presence of the regional papillary thyroid cancer metastases causes significant changes in the relationship between small (diameter </= 7.5 mum) and large (>7.5 mum) lymphoid cells of the cervical lymph nodes. The morphometric changes were assessed by our diagnostic index. The expert diagnostic system was developed on the basis of a set of quantitative features and their boundary values. This system highlights the morphometric changes in the population of the cervical lymph nodes lymphoid cell with metastases versus control. Clinical trials have shown that the expert diagnostic system's conclusions coincided with the histological diagnosis in all the cases. The expert diagnostic system, image processing, database formation, assessment of lymphoid cells morphometric features formed the basis of the rapid method of diagnosing regional metastases of papillary thyroid cancer. The diagnosis was performed in automatic mode. Taking into account that about 30-50% of aspiration smears contain only blood cells (absence of specialized cells such as thyrocytes), the use of our method should greatly improve the cytologic diagnosis of regional metastases of papillary thyroid cancer.

The article discusses the problem of forming the knowledge awareness in the course of teaching the basics of artificial intelligence to the future and working teachers. Based on the analysis of scientific sources and generalization of pedagogical experience, the authors state that the low level of knowledge awareness in the field of AI among future teachers usually shows up during the design of expert systems and in the process of developing intelligent programs. The description of the developed methodology that ensures the formation of teachers' knowledge awareness in the AI is presented in the article. The authors emphasize that special attention should be paid to the informal assimilation of theoretical material in the process of forming the knowledge awareness. The article presents the examples of lectures on the following topics: "Introduction to AI", "The models of knowledge representation in AI systems", "Expert systems" where interactive surveys of various types and content are implemented and collective work on the virtual whiteboard is carried out. Pedagogical experiment confirmed the effectiveness of the developed methodology which has a number of advantages over traditional training. Among them we can state visualization of theoretical material which helps a lot to solve the problem of formal learning. Also, the ability for the teacher to take into consideration the student’s point of view shown via interactive surveys and react on them simultaneously. Finally, students get an opportunity to act as participants of the educational process and meanwhile to conduct their self-control and self-assessment during learning the theory.

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.

An expert system framework for machine fault diagnosis : Murali Krishnamurthi and Don T. Phillips. Comput. ind. Engng22(1), 67 (1992)

Limitations on the representation of knowledge, and hence the inference methods, naturally limit the domains of the first-generation expert systems. In response to these limitations, fuzzy expert systems or the second-generation of expert systems provide two essential and unique advantages in the design, development, and implementation of expert systems: (1) fuzzy knowledge representation and (2) fuzzy inference methods. It is suggested that second-generation expert systems be designed and implemented with the expressive powers of representation and improved inference methods based on fuzzy logic. A number of research topics and application areas are identified. >

RheumaTutor. A case-based rheumatological training program. Stefan Schewe. ($50, DM 98, students $34.00, DM 68). Wiesbaden: Ullstein Medical Verlag, 1999. ISBN 3-86126-934-1 .

Abstract: This paper describes a system of shallow and deep knowledge acquisition and representation for diagnostic expert systems. The acquisition system is integrated into a diagnostic expert system shell. Shallow knowledge is represented in a failure model as a set of cause‐effect relations among the possible faults, while deep knowledge is represented in three deep models: a functional, a deep causal and a taxonomic model. The acquisition and the representation of all the models are fully integrated. The deep knowledge is used by the final expert system in order to provide the user with deep explanations of the cause‐effect relations of the failure model.