Communication Tools for Human-Computer Knowledge Transfer

ABSTRACT:The diverse knowledge levels among first-year mechanical engineering students lead to significant disparities in individual learning. Intelligent tutoring systems (ITS) offer a solution by providing tailored digital one-to-one instruction, bridging knowledge gaps, and equalizing learning outcomes. This thesis develops an ITS for design theory based on a knowledge-based engineering system, presenting an innovative model that integrates key features of ITS and knowledge-based systems. Implemented in a specialized environment, the system’s application and validation demonstrate its ability to meet context-sensitive design teaching requirements and provide adaptive tutoring.

This chapter aims to indicate some of the potential benefits and dangers inherent in the technology of Knowledge-Based Systems (KBSs) and to suggest courses of action that would both minimize risk and promote social benefit in future developments. KBSs offer cheap and ready access to knowledge, efficient management and organization of information, conversion of information into usable knowledge to guide human action, knowledge and advice relevant to the context in which it is needed. There are, however, potential dangers if a user is unfamiliar with the assumptions underlying KBS structures of reasoning and representing knowledge: the danger of a program becoming incomprehensible or unreliable in an emergency: the possibility of placing too much faith in the abilities of KBSs and thereby losing the capacity for critical evaluation of its judgments: the inevitable uncertainty or inexactitude programmed into KBSs: the economic implications at an uneven distribution of new technology: and the likelihood of misinterpretation or technical failure when autonomous. systems are automatically activating machinery (e. g. military/defence systems). The chapter argues for the regulation of KBS construction, particularly for those systems designed for public or “non-expert” use. Suggestions include programming into each system a clear qualification of answers and requests for data, setting up a workable Code of Conduct for AI practitioners, and constant supervision of the assumptions and activities of each computerized system. Since KBSs work by consulting an internal model of the world (supplied to them by a human programmer), their decisions must be challenged or ignored when necessary and always treated with an appropriate degree of scepticism

Artificial intelligence in pharmaceutical product formulation: knowledge-based and expert systems

To solve the problem of advanced digital manufacturing technology in the practical application, a knowledge engineering technology was introduced into the computer numerical control (CNC) programming. The knowledge acquisition, knowledge representation and reasoning used in CNC programming were researched. The CNC programming system functional architecture of impeller parts based on knowledge based engineering (KBE) was constructed. The structural model of the general knowledge-based system (KBS) was also constructed. The KBS of CNC programming system was established through synthesizing database technology and knowledge base theory. And in the context of corporate needs, based on the knowledge-driven manufacturing platform (i.e. UG CAD/CAM), VC++6.0 and UG/Open, the KBS and UG CAD/CAM were integrated seamlessly and the intelligent CNC programming KBE system for the impeller parts was developed by integrating KBE and UG CAD/CAM system. A method to establish standard process templates was proposed, so as to develop the intelligent CNC programming system in which CNC machining process and process parameters were standardized by using this KBE system. For the impeller parts processing, the method applied in the development of the prototype system is proven to be viable, feasible and practical.

Building an influence diagram in a knowledge-based decision system

Discussion

Due to recent advances in knowledge-based systems technology it is suggested that students in a systems development course be given exposure to the concept of designing and implementing knowledge-based systems. The purpose of this paper is to describe the objective, scope, methods and procedures of knowledge-based projects. The first half of the paper introduces the knowledge-based approach and describes the structure and components of such a system. The second half of the paper deals with course design procedures. The scope, topics, project characteristics and experiences pertaining to such a project are covered here. A practical Knowledge-based Systems Development Life Cycle (KSDLC) is also proposed for use in such a course. It is a suitable methodology as it resembles the traditional Systems Development Life Cycle.

Dutch research on knowledge-based instructional systems: Introduction to the special issue

Knowledge-Based and Intelligent Information and Engineering Systems

Knowledge-Based and Intelligent Information and Engineering Systems

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.

This paper describes the application of model-based technology in the area of failure mode effects analysis (FMEA). FMEA involves the repetitive application of engineering expertise, and so would seem to be a promising target for automation through knowledge-based systems (KBS) technology. However, in order to decide what would be the effect of a failure in a sub-system, it is necessary to reason about the structure of the sub-system being investigated and to be able to represent and reason about different facets of the way in which the sub-system works. The difficulty of automating this analysis of failure effects for new designs has meant that, in general, automation of the FMEA task has concentrated on only the clerical aspects of FMEA—helping the human expert to keep track of which possible failures have been analysed. The work described in this paper automates the analysis phase of the FMEA process. This should enable the development of computerized aids for the FMEA engineer which will eliminate much of the tediousness of the task.

Inducing general functions from specific training examples is a central problem in the machine learning. Using sets of If-then rules is the most expressive and readable manner. To find If-then rules, many induction algorithms such as ID3, AQ, CN2 and their variants, were proposed. Sequential covering is the kernel technique of them. To avoid testing all possible selectors, Entropy gain is used to select the best attribute in ID3. Constraint of the size of star was introduced in AQ and beam search was adopted in CN2. These methods speed up their induction algorithms but many good selectors are filtered out. In this work, we introduce a new induction algorithm that is based on enumeration of all possible selectors. Contrary to the previous works, we use pruning power to reduce irrelative selectors. But we can guarantee that no good selectors are filtered out. Comparing with other techniques, the experiment results demonstrate that the rules produced by our induction algorithm have high consistency and simplicity.

Knowledge-Based Intelligent Information and Engineering Systems

This paper explores the integration of a Knowledge-Based Engineering System (KBES) for design heuristics to enhance sustainable engineering education. The primary goal is to equip students with the skills needed to develop sustainable products while comprehending the complexities and multifaceted nature of sustainability. As sustainability is highly context-dependent, the importance of utilizing context-specific knowledge in product development is emphasized. A key question addressed in this study is how digital tools can effectively provide and facilitate the sharing of knowledge critical for sustainability-oriented design. The paper also examines how to build competency in sustainability using design heuristics, which are easy to understand and apply, offering students a practical approach to sustainable problem-solving. By integrating KBE systems with these heuristics, this paper proposes a framework for engineering education that fosters a deeper understanding and actionable skills for developing sustainable products. This approach aims to empower future engineers to create solutions that are not only innovative but also responsible and adaptive to the complex demands of sustainability.