Knowlege-based expert systems and their application(tutorial session

Schedule-based material requirements planning (SBMRP), which has been suggested as an integration of scheduling and MRP methods, is described and its deficiencies are identified. Knowledge acquisition through computer experimentation (KACE) was used to develop a knowledge-based expert system (KBES) for SBMRP. Comparison of the KBES with conventional forward- and backward-method SBMRP, using total cost as a criterion showed the KBES to be superior for scheduling jobs consisting of up to 20 parts. Sensitivity analysis was conducted on the important parameters such as total available time over total processing time (TA/TP), processing time for each operation, and number of parts. The KBES exhibited the best performance regardless of the processing time. As the level of TA/TP increased, the KBES's performance improved.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, the authors contend that the area of real estate pricing is often learned from an inefficient trial and error process. To address this problem, a knowledge-based expert system (REPRES) was developed by the authors. The knowledge base and inference engine logic of REPRES are described in the paper. Use of this expert system, in which the user is directed to the price of a given home using either a market or cost approach to pricing, should result in improved quality of real estate pricing, facilitate accumulation of further knowledge about the real estate pricing process, enhance communication between users of REPRES and customers, and assist in training individuals who are unfamiliar with the logic and knowledge base of real estate pricing. Validation results indicate REPRES has accomplished these goals. The authors conclude with a discussion of the implications of such an expert system to real estate pricing and real estate modeling research.

The importance of operators' knowledge in the production of high-quality water and the need for consistent high-level expertise have fueled propositions for the application of computer technology, including knowledge-based expert systems (KBES), as a means of enhancing drinking water treatment operations. With this in mind, the authors have developed CHLOROS, a knowledge-based system which integrates both expert system and hypertext technologies in a WindowsTM environment. Designed as an off-line tool, the system does not directly control operations related to drinking water disinfection, but rather has been intended to assist treatment plant operations personnel. It is believed that CHLOROS will serve as a practical tool for water utilities because it creates a consistent decision-making environment, captures and preserves the expertise of human experts, and reduces the amount of manual effort and time that may be expended by searching through manuals and literature for information that is required to support the choice of control parameters.Key words: knowledge-based systems, expert system, hypertext system, disinfection, chlorination, chloramination.

The need and desire to transform computers from machines that perform laborious, tedious and repetitive calculations to systems that can perform more intelligent tasks akin to human beings has been the single goal of the artificial intelligence (Al) community. With recent advances in the field of artificial intelligence this long awaited dream is becoming a reality. This success is due to what has become known as the knowledge-based expert systems technique. A characteristic of these systems is massive amount of knowledge in a specific domain which enables them to imitate the human problem solving process. Knowledge-based expert systems are able to perform tasks which are labeled as intelligent tasks when performed by human beings. More specifically, they are able to interpret data, monitor a system, diagnose a condition, plan an action, consult, reason, and explain their reasoning. These characteristics are the motivation for transforming expert systems from a few academic projects in the 1970’s to the explosive commercial market in the 1980’s. The importance of knowledge-based expert systems can be measured in several ways: the widespread application of these systems to various domains, a few in the 1970’s compared to 100s of applications in the 1980’s; the number of startup companies marketing knowledge-based expert systems and tools related to the development of these systems; the number of dollars spent by various governments and companies to achieve superiority and state of the art status in this field [Hayes-Roth 84].KeywordsExpert SystemVLSI DesignHuman DesignerProduction MemoryVLSI ChipThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 advisory system for the ISO 9001 quality system

In present study, a hierarchical knowledge-based expert system, named HYDROPERT, to predict binary azeotrope formation in hydrocarbon mixtures was created and investigated. Specific knowledge included in azeotropy on hydrocarbon mixtures and the implementation of the expert system are described along some of the several components of expert system applications: knowledge representation strategy and levels of knowledge abstraction, inference machine, user interface, explanation facilities. The knowledge base is hierarchically structured with the multiple levels of domain-specific knowledge such as the azeotropic data bank as the lowest level, component-specific compiled heuristic rules as the second level, group-oriented compiled heuristic rules as the third level, and generic class-oriented model-based heuristic rules as the highest level. The predictive capabilities and generality of the expert system can be highly enhanced through the integration of different kinds of domain knowledge into the hierarchical structure. The expert system predicting the binary azeotrope formation in hydrocarbon mixtures may be a useful tool for many chemical engineering activities, especially such as process synthesis and design.

Knowledge-based Systems and Expert Systems, in particular, are expensive to build and difficult to validate and debug because of their complexity and dynamism. Therefore, it is not easy for knowledge engineer and domain expert to identify the gaps and mistakes in knowledge base. Unit testing is unable to cover validation process at all stages, in many cases manual thorough review of decision process is needed. In this paper we spot main approaches to validation and verification issue and describe a component that helps to debug a knowledge base by visualising execution of rules that derive a particular result. This component is developed for Knowledge-based Systems built on Drools Platform1 and we demonstrate application of this component in a knowledge-based engineering system for structural optical design.

Knowledge-based expert systems: user interface implications

Use of a domain model to drive an interactive knowledge-editing tool

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.

An expert system is designed for determining substation locations and feeder configuration of a distribution system. By incorporating the heuristic rules currently followed by distribution engineers into the knowledge base, the expert system can benefit from system planners' experience in its problem-solving process. To minimize the feeder losses of the distribution plan, the location-allocation method is proposed for determining substation locations. The expert system is implemented using the artificial language PROLOG and is designed to be used interactively. The expert system is applied to the planning of a distribution system in Taiwan. Test results indicate that it is efficient and user friendly.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Knowledge-based (expert) systems replicate the thinking processes that are involved in strategic planning. As such, they not only assist in making strategic planning decisions, but they also serve as useful learning tools for those studying strategic planning. By examining the knowledge bases of these systems, readers can analyze in detail how planners think. In addition, by using the systems, readers can observe how these processes are put to work in making specific decisions. The discussion of knowledge-based systems is in four parts: commercially available systems; proprietary company systems; research; and future integrated systems tools.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The authors of this paper spent over three decades on average working with, for and on business decision takers, typically top executives. We have been working with them in different parts of their organisations, for them in a variety of advisory roles, and on them in various research projects trying to understand the process of decision making and decision taking. In this paper we reflect on our consultancy and research experience involving decision takers in order to conceptualize a distinction between two types of decisions with regards to the way of supporting them. Therefore, purpose of the study could be described as an attempt of a minimalist decision typology (involving only two types) where the two decision types are achieved by their characteristics and then we argue that two different approaches to decision support can be useful for the two types of decisions. This paper qualifies as a conceptual one informed by underlying empirical studies. These empirical underpinnings comprise of three types: (1) designed as qualitative empirical research projects, involving participant observations, interviews, focus groups, and knowledge modelling using knowledge-based expert systems, (2) consultancy carried out through executive coaching and knowledge engineering, and (3) a mix of the previous two in action research projects. We reflect on these experiences in this paper drawing speculative conclusions using retrospective sensemaking – therefore the resulting model is a conceptual one and we offer it for debate and potential conceptual for forthcoming research projects for the academic audience interested in decision making. Additionally, the model can also be useful for practicing executives, in deciding what kind of decision support to seek for the particular decision at hand, as well as for consultants to figure out what type of decision support to offer. The super-large decisions that may redefine the organisation we call Icons (with capital ‘I’), referring to the notion of Iconic. These decisions require exceptionally high degree of competence. The second group consists of those insignificant decisions that do not require any substantial degree of competence; we call these as icons (with lower case ‘i’) referring to the icons that we need to click or touch in order to run an app on our computers, tablets, smartphones or wearables. It is perhaps obvious that Icons need very different types of decision support than icons. To get a better understanding of this, we need to look at the dominant knowledge type used in the decision situations. We distinguish three types of knowledge: facts, skills, and intuition. Facts are not of primary focus in most decision situations and these are pre-processed and provided by ERP (Enterprise Resource Planning) systems. Skills play a significant role in the professional lives of practicing managers, but they actually need one single skill: communication. Good managers are typically good communicators. Therefore, the focus in the case of both Icons and icons is intuition. In Icons the dominant knowledge is focal intuition, which we call hunch. The dominant knowledge in icons is subsidiary intuition, which we call explanation. Thus, Icons call for a coach who can help decision takers clarify their expectations, while icons need knowledge-based expert systems which can help develop organisational routines.

Expert systems in wind engineering