Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

A New Approach In Learning For Intelligent Multi Agent Systems

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.

To design a High-Performance Building (HPB), a performance goal should be clearly set from very early design phases, and then a decision path of what performance measures have been chosen in the past stages and shall be chosen in a later stage should be visible. In particular, for small- and mid-sized HPBs that are constructed with a smaller budget, if applicable performance measures are subjective to change, supplementary design costs can increase due to intermittent performance evaluations. To help this situation, we are developing a design expert system for small- and mid-sized buildings that pursues a balance between economic value and energy performance. The economy rule base suggests the most economic building volumetry and form in view of the site context, while the energy rule base suggests a series of energy-sensitive design variables and their options. Based on these rule bases, the expert system presents multiple design decision paths. The design decision support model of the inference engine helps stakeholders choose a preferred design path out of multiple paths, compare the paths, trace back the paths, and effectively revoke past decisions. An actual small retail and office construction project was chosen as a test case to compare the utility and robustness of the pilot system against the conventional design practice. In case of a rather risky design change scenario, the decision-making using the pilot expert system outperforms the conventional practice in terms of selecting designs with a good balance between economic value and energy performance. In addition, it was easier for users of the pilot system to forecast risks upon critical design changes and, in turn, to identify reasonable alternatives.

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

This paper describes recent experience with the development and performance of OVERDRIVE 1.2. a central component of PARADIGM (the Pavement Rehabilitation Analysis and Design Mentor). PARADIGM is a microcomputer-based tool to integrate a set of knowledge-based expert systems (KBES) and algorithmic models for pavement rehabilitation decision-making, particularly at the local level. Because the analysis and design of pavement rehabilitation strategies relies so heavily on experts, and many of the tasks involved are both complex and ill-defined, conventional computer tools are of limited use in this domain. A basic premise of this research is that KBES can play a significant role in addressing these problems. OVERDRIVE is the Overlay Design Heuristic Adviser, and is a research prototype KBES to assist local engineers evaluate the structural adequacy of existing flexible pavement and design the structural thickness of an asphalt concrete overlay. In this paper the general domain, the domain tasks addressed by the current version of OVERDRIVE, knowledge acquisition, knowledge representation and inference, and hardware and software are described. A case-study application is presented using actual data. This application permits an initial assessment to be made of the comparative performance of OVERDRIVE versus alternative overlay design methods.

Chapter 1 briefly introduces the concept of cognitive emulation, and outlines its current status. Chapter 2 reviews psychological research on human expert thinking. First, the study of expert thinking is placed in the context of modern cognitive psychology. Next, the principal methods and techniques employed by psychologists examining expert cognition are examined. The remainder of the chapter is given over to a review of the published literature on the nature and development of human expertise. Chapter 3 reviews the main arguments for and against cognitive emulation in expert system design. The tentative conclusion reached is that a significant degree of emulation is inevitable, but that a pure, unselective strategy of emulation is neither realistic nor desirable. Chapter 4 examines the prospects for cognitive emulation from a more pragmatic angle. Several factors are identified that represent constraints on the usefulness of a cognitive approach. However, a second set of factors is identified which should facilitate an emulation strategy - especially in the longer term. Some guidance is given on when to seriously consider adopting an emulation strategy. Chapter 5 presents a critical survey of expert system research that has already addressed the emulation issue. Six basic approaches to cognitive emulation are distinguished and evaluated. This helps draw out in more detail the implications of an emulation strategy for knowledge acquisition, knowledge representation and system architecture. The chapter concludes by discussing the issues that arise when different approaches to emulation are combined. Some guidance is offered on how this might be achieved. Chapter 6 summarizes the main themes and issues to have emerged, the design advice contained in the thesis, and the original contributions made by the thesis.

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. >

The design, engineering, manufacture, and maintenance of high performance and high complexity systems have become one of the most important issues in the industrial world today. Facilitating these engineering activities depends on our ability to assemble and effectively deliver the life-cycle engineering knowledge. This paper deals with different problems of design using concurrent engineering, and with the application of information and knowledge management, with acquisition, representation, retrieval and delivery of design knowledge to the designers to reach co-operative knowledge processing. Car body shapes will be given as examples to shape based and feature based knowledge. Finally the support for interacting / integrated designer systems will be discussed.

Building Expert Systems: A Structured Bibliography

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.

Knowledge acquisition and representation for the tolerance design of injection-molded parts

Expert system for automobile fault diagnosis is an intelligent system, and its key technologies are knowledge acquisition, knowledge representation and inference strategy. Based on large collection of papers which on abroad or on home, some resolved methods have been presented in this paper, including improving of traditional method such as automotive acquisition of fault rules, combined knowledge representation and inference method diversification, applying of new theory or new technology such as case-base expert system, fuzzy-base expert system, neural network-base expert system and action-base expert system and so on. And then put forward to that intellectualization, cyberization and integration are the future development direction of automobile fault diagnosis expert system.

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.

Deep versus compiled knowledge approaches to diagnostic problem-solving

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. >