Automated fuzzy knowledge acquisition with connectionist adaptation

This paper presents an automated knowledge acquisition architecture for the truck docking problem. The architecture consists of a neural network block, a fuzzy rule generation block and a genetic optimisation block. The neural network block is used to quickly and adaptively learn from trials the driving knowledge. The fuzzy rule generation block then extracts the driving knowledge to form a knowledge rule base. The driving knowledge rule base is further optimised in the genetic optimisation block using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.     

Strategic knowledge acquisition: a case study of learning through prototyping

To understand useful strategies for guiding the design process, we analyze the relationship between the use of prototypes and design knowledge acquisition. Prototyping in five design projects at a science museum is analyzed and compared. We also extract situations in which design knowledge is acquired from prototyping.     

A framework for intelligent design of manufacturing cells

One of the major thrusts of agile/lean/responsive manufacturing strategies of the twentyfirst century is to introduce advanced information technology into manufacturing. This paper presents a framework for robust manufacturing system design with the integration of simulation, neural networks and knowledge-based expert system tools. An operation/ cost-driven cell design methodology was applied to concurrently consider cell physical design and the complexity of cell control functions. Simulation was exercised to estimate performance measures based on input parameters and given cell configurations. A rulebased expert system was employed to store the acquired expert knowledge regarding the relation between cell control complexities, cost of cell controls, performance measures and cell configuration. Neural networks were applied to predict the cell design configuration and corresponding complexities of cell control functions. Training of neural networks was performed with both forward and backward methods by using the same pair of data sets. Hence, trained neural networks will be able to predict either input or output parameters. This innovative new design methodology was illustrated via a successful implementation exercise resulting in actually acquiring an automated cell at industrial settings. The experience learned from this exercise indicates that the proposed design methodology works well as an effective decision support system for cell designers and the management in determining appropriate cell configuration and cell control functions at the design stage.     

Automated knowledge acquisition for the surface-mount PWB assembly domain

The development of knowledge-based (or expert) systems for the surface-mount printed wiring board (PWB) assembly domain requires the understanding and regulation of several complex tasks. While the knowledge base in an expert system serves as a storehouse of knowledge primitives, its design and development is a bottleneck in the expert system development life-cycle. Therefore the development of an automated knowledge acquisition (KA) facility (or KA tool) would facilitate the implementation of expert systems for any domain. This paper describes an automated KA tool that helps to elicit and store information in domain-specific knowledge bases for surface-mount PWB assembly. A salient feature of this research is the acquisition of uncertain information.     

GDKAT: A goal-driven knowledge acquisition tool for knowledge base development

While knowledge-based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in-depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain-dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal-driven knowledge acquisition tool (GDKAT) that helps elicit and store experts' declarative and procedural knowledge in knowledge bases for a user-defined domain. The designed tool is implemented using the object-oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.     

mwKAT: A meta-tool-generated knowledge acquisition tool for multimedia workstations development

mwKAT is an interactive knowledge acquisition tool for acquiring domain knowledge about multimedia components. It constructs knowledge bases for a consulting system that produces the design specification for a multimedia workstation according to the user requirements.mwKAT is generated from and executed inGAS, a primitives-based generic knowledge acquisition meta-tool. It contains three acquisition primitives, namely, parameter proposing, constraint proposing, and fix proposing to construct an intermediate knowledge base represented by a dependency model. These primitives identify necessary domain knowledge and guide users to propose significant components, constraints, and fix methods into the dependency model.mwKAT also invokes knowledge verification and validation primitives to verify the completeness, consistency, compilability, and correctness of the intermediate knowledge base.     

The training of knowledge structures for manufacturing tasks: an empirical study

Abstract

Knowledge structure refers to the manner in which a human organizes knowledge with a given domain. Research has identified knowledge structure as a determinant of the human ability to perform cognitive-oriented tasks. Yet uncertainty still exists about how to improve an individual's cognitive task performance through the controlled utilization of the individual's knowledge structure. The purpose of this study is to investigate whether the development of individual's knowledge structure in a particular domain can be manipulated through training. The experiment utilized the manufacturing domain of plastic extrusion machine operation. Sixteen subjects, having no previous knowledge of the domain, were randomly assigned to one of two experimental groups. Each of the experimental groups corresponded to a distinct training condition. Over a three-day period, both training groups received the same instructional content; however, the sequence in which the training material was presented differed. One group initially received the abstract, conceptual relationships between domain concepts, followed by more detailed relationships associated with the lower level aspects of the domain. The other group received the training material in the reverse order; i.e. the lower level information followed by the abstract. Prior to and concluding the training sessions, each individual's knowledge structure was assessed along two dimensions, hierarchical levels and multiple relations, through a computer-based measurement technique.entitled KSAT. The group which received the abstract relationships first showed significant improvement following training along both dimensions of knowledge structure. No significant changes in the knowledge structure dimensions were found for the group which received the lower level relationships first. This study suggests that an individual's knowledge structure can be manipulated through training, with a significant effect being attributed to the training sequence of abstract material followed by the more detailed material.     

Interweaving knowledge extracting,organizing and evaluating: A concrete design for preventing logic and structure bugs while interviewing experts

The process of constructing expert systems (ESs), programs that approximate how domain experts solve problems in their specialized fields, is not at all as systematic, efficient, and verifiable as it should be. A reason is that no rigorous error-prevention interviewing method exists for structuring and testing ESs while building them. Often domain experts do implicitly ask of themselves analytical questions such as ‘Is that claim of mine always true?’ Another kind of expert — one specializing in logic analysis — explicitates, collects, and systematizes the fund of generic questions, such as ‘Are these sub-goals sufficient steps to the pre-established goal-category?’ There is a great need to make a method of interviewing, interlaced with testing and organizing, available to all domain experts and ES programmers via an interactive program. This program, which can generically be called a LAP (Logic Aids Program), plays the role of a domain-independent logic-assistant.     

An approach to user knowledge acquisition in product design

As the world increasingly moves towards a knowledge-based economy, user requirements become an important factor for enterprises to drive product collaborative design evolution. To map user requirements to the product model, user requirements are generally extracted into knowledge that can be used for design decisions. However, because users are interest-driven participants and not professional design engineers, the effect of user knowledge acquisition is not ideal. There are significant challenges for rapid knowledge acquisition with dynamic user requirements. This paper presents an approach to user knowledge acquisition in the product design process, which obtains the tangible requirements of users under the premise that users are adequate for participation. In this approach, the typical information flow is divided into four stages: submission, interaction, knowledge discovery, and model evolution. In the submission stage, natural language processing technology is used to transform text form solutions into data, so that computer technology can be applied to manage large-scale user requirements. In the interaction stage, users are helped to improve their solutions by the iterative recommendation process. In the knowledge discovery stage, after less concerned partial solutions are removed and vacant items are predicted to be supplemented, the final collection of user design information is obtained. Finally, based on rough set theory, design knowledge can be extracted to support the decision of the product model. The washing machine design project is used as a case study to explain the implementation of the proposed approach.     

Demand-driven knowledge acquisition method for enhancing domain ontology integrity

Knowledge, as the most important resource for the knowledge economy in the 21st century, is fundamental to enterprise competitive strength. Therefore, how to effectively integrate internal and external knowledge and provide correct knowledge to the right users in a timely fashion have become key success factors in business operation.     

An intelligent approach of knowledge searching within Internet-based distributive knowledge integrated environment

Used to integrate all kinds of knowledge coming from various locations, domains and disciplines, a framework of the Internet-based distributive knowledge integrated system (DKIS) is proposed. Since knowledge repositories are the key components of the DKIS, the efficiency and reliability of the knowledge searching affects the running performance of the DKIS system. We stress methods of efficiently retrieving knowledge from DKIS repositories. Based on artificial neural networks, fuzzy logic and rule reasoning, a novel approach to knowledge searching over the Internet is presented. The issue of knowledge layering is also discussed. Using the rolling bearing as an example, a prototype of knowledge searching is realized. The main aim of this study is to identify an efficient and reliable method of knowledge retrieval for the DKIS within an Internet environment so as to support its perfect operation.     

Knowledge acquisition and knowledge representation with class: the object-oriented paradigm

Knowledge acquisition and knowledge representation are the fundamental building blocks of knowledge-based systems (KBSs). How to efficiently elicit knowledge from experts and transform this elicited knowledge into a machine usable format is a significant and time consuming problem for KBS developers. Object-orientation provides several solutions to persistent knowledge acquisition and knowledge representation problems including transportability, knowledge reuse, and knowledge growth. An automated graphical knowledge acquisition tool is presented, based upon object-oriented principles. The object-oriented graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for the automated KA tool provides a representation independent methodology that can easily be mapped into any other object-oriented expert system or other object-oriented intelligent tools.     

An interactive tool for knowledge base refinement

We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.     

Constraint acquisition for Entity-Relationship models

We establish search algorithms from the area of propositional logic as invaluable tools for the semantic knowledge acquisition in the conceptual database design phase. The acquisition of such domain knowledge is crucial for the quality of the target database.Integrity constraints are conditions that capture the semantics of the application domain under consideration. They restrict the databases to those that are considered meaningful to the application at hand. In practice, the decision of specifying a constraint is very important and extremely challenging.We show how techniques from propositional logic can be utilised to offer decision support for specifying Boolean and multivalued dependencies between properties of entities and relationships in conceptual databases. In particular, we use a search version of SAT-solvers to semi-automatically generate sample databases for this class of dependencies in Entity-Relationship models. The sample databases enable design participants to judge, justify, convey and test their understanding of the semantics of the future database. Indeed, the decision by the participants to specify a dependency explicitly is reduced to their decision whether there is some sample database that they can accept as a future database instance.     

High Performance Knowledge Bases: four approaches to knowledge acquisition, representation and reasoning for workaround planning

As part of the DARPA-sponsored High Performance Knowledge Bases program, four organisations were set the challenge of solving a selection of knowledge-based planning problems in a particular domain, and then modifying their systems quickly to solve further problems in the same domain. The aim of the exercise was to test the claim that, with the latest AI technology, large knowledge bases can be built quickly and efficiently. The domain chosen was ‘workarounds’; that is, planning how a convoy of military vehicles can ‘work around’ (i.e. circumvent or overcome) obstacles in their path, such as blown bridges or minefields.

This paper describes the four approaches that were applied to solve this problem. These approaches differed in their approach to knowledge acquisition, in their ontology, and in their reasoning. All four approaches are described and compared against each other. The paper concludes by reporting the results of an evaluation that was carried out by the HPKB program to determine the capability of each of these approaches.     

Reconceptualizing knowledge networks for enterprise systems implementation: incorporating domain expertise of knowledge sources and knowledge flow intensity

Research on knowledge acquisition through informal social networks during enterprise system implementation has not accounted for the domain expertise of knowledge sources or the quality of knowledge flows. By using data collected from an enterprise resource planning system implementation, this paper reconceptualizes knowledge networks into subnetworks on the basis of the domain expertise of end users and analyzes knowledge acquisition patterns between subnetworks across workgroups having varying performance outcomes. Expertise-based knowledge patterns and their intensities had significant implications for performance outcomes, reiterating their role in the learning process and emphasizing the need to incorporate them into knowledge networking models.     

MRM: A matrix representation and mapping approach for knowledge acquisition

Knowledge acquisition plays a critical role in constructing a knowledge-based system (KBS). It is the most time-consuming phase and has been recognized as the bottleneck of KBS development. This paper presents a matrix representation and mapping (MRM) approach to facilitate the effectiveness of knowledge acquisition in building a KBS. The proposed MRM approach, which is based on matrix representation and mapping operations, comprises six consecutive steps for generating rules. The procedure in each step is elaborated. A case study on primarily diagnosing an automotive system is employed to illustrate how the MRM approach works.     

Restructuring decision tables for elucidation of knowledge

Decision tables are widely used in many knowledge-based and decision support systems. They allow relatively complex logical relationships to be represented in an easily understood form and processed efficiently. This paper describes second-order decision tables (decision tables that contain rows whose components have sets of atomic values) and their role in knowledge engineering to: (1) support efficient management and enhance comprehensibility of tabular knowledge acquired by knowledge engineers, and (2) automatically generate knowledge from a tabular set of examples. We show how second-order decision tables can be used to restructure acquired tabular knowledge into a condensed but logically equivalent second-order table. We then present the results of experiments with such restructuring. Next, we describe SORCER, a learning system that induces second-order decision tables from a given database. We compare SORCER with IDTM, a system that induces standard decision tables, and a state-of-the-art decision tree learner, C4.5. Results show that in spite of its simple induction methods, on the average over the data sets studied, SORCER has the lowest error rate.     

Trace-driven knowledge acquisition (TDKA) for rule-based real time scheduling systems

Expert scheduling systems, which develop the schedule automatically on a real time basis, are able to respond to the changes of product demand in Flexible Manufacturing Systems (FMS). While developing an expert scheduling system, the most time-consuming and difficult step is knowledge acquisition, the process that elicits the knowledge from experts and transfers it into the knowledge base. A trace-driven knowledge acquisition (TDKA) method is proposed to extract the expertise from the schedules produced by expert schedulers. Three phases are involved in the TDKA process: data collection, data analysis, and rule evaluation. In data collection, the expert schedulers are identified and decisions made during the scheduling process are recorded as a trace. In data analysis, a set of scheduling rules is developed based on the trace. The rules are then evaluated in the last phase. If the resulting rules do not perform as well as the expert schedulers, the process returns to phase two and refines the rules. The whole process stops whenever the resulting rules perform at least as well as the expert schedulers. A circuit board production line is used to demonstrate the feasibility of the TDKA methodology. The scheduling rules perform much better than the expert schedulers from whom the rules are extracted.