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.     

Visual knowledge specification for conceptual design: Definition and tool support

Current CAD tools are not able to support the conceptual design phase, and none of them provides a consistency analysis for sketches produced by architects. This phase is fundamental and crucial for the whole design and construction process of a building. To give architects a better support, we developed a CAD tool for conceptual design and a knowledge specification tool. The knowledge is specific to one class of buildings and it can be reused. Based on a dynamic and domain-specific knowledge ontology, different types of design rules formalize this knowledge in a graph-based form. An expressive visual language provides a user-friendly, human readable representation. Finally, a consistency analysis tool enables conceptual designs to be checked against this formal conceptual knowledge.In this article, we concentrate on the knowledge specification part. For that, we introduce the concepts and usage of a novel visual language and describe its semantics. To demonstrate the usability of our approach, two graph-based visual tools for knowledge specification and conceptual design are explained.     

Automatic knowledge acquisition tool for irrigation and fertilization expert systems

Knowledge acquisition is commonly regarded as a major obstacle and bottleneck in the process of designing and implementing knowledge base system. Our approach to overcome this problem is to build a task specific knowledge acquisition tool for solving the irrigation and fertilization scheduling problems. The tool was tested using set of cases that was designed to measure the tool flexibility, usability, accuracy, and exception handling.     

D-KAT: a deep knowledge acquisition tool

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.     

Inference in the SOOKAT object-oriented knowledge acquisition tool

The SOOKAT (structured object-oriented knowledge acquisition tool) knowledge acquisition (KA) tool, supporting the SeSKA (seamless structured knowledge acquisition) methodology, integrates phases of KA through seamless transformations between object-oriented (OO) models.The integration of constructing a knowledge base (KB) can be extended beyond the KA process by performing inferences in instantiations of models constructed during the KA process.The models, constructed during the KA process, form a framework for performing inferences in instantiations of the models.Inferences performed in instantiations of OO models are guided by control objects (CO). Messages are sent between COs and components of the inference structure. A specific CO, possibly using subordinate COs, can be specified for each inference strategy.There exists a mutual CO for forward and backward chaining that can also be used when reasoning according to protocols. In addition, COs for problem-solving methods (PSMs), such as cover-and-differentiate or propose-and-revise, can be used.Mechanisms for importing PSMs over the Internet, as well as for generating specific COs for PSMs, are under development.     

Kave: a tool for knowledge acquisition to support artificial ventilation

A decision support system for artificial ventilation is being developed. One of the fundamental goals for this system is the application of the system when a domain expert is not present. Such a system requires a rich knowledge base. The knowledge acquisition process is often considered to be the bottleneck in acquiring such a complete knowledge base. Since no single available method, for example interviewing domain experts, is sufficient for removing this bottleneck, we have chosen a combination of different methods. The different backgrounds of knowledge engineers and domain experts could cause communication restrictions and difficulties between them, e.g. they might not understand each others knowledge domain and this will affect formulation of the knowledge. To solve this problem we needed a tool which supports both the knowledge engineer and the domain expert already from the initial phase of developing the knowledge base. We have developed a knowledge acquisition system called KAVE to elicit knowledge from domain experts and storing it in the knowledge base. KAVE is based on a domain specific conceptual model which is a result of cooperation between knowledge engineers and domain experts during identification, design and structuring of knowledge for this domain. KAVE includes a patient simulator to help validate knowledge in the knowledge base and a knowledge editor to facilitate refinement and maintenance of the knowledge base.     

A novel self-optimizing approach for knowledge acquisition

The attribute reduction and rule generation (the attribute value reduction) are two main processes for knowledge acquisition. A self-optimizing approach based on a difference comparison table for knowledge acquisition aimed at the above processes was proposed. In the attribute reduction process, the conventional logic computation was transferred to a matrix computation along with some added thoughts on the evolution computation used to construct the self-adaptive optimizing algorithm. In addition, some sub-algorithms and proofs were presented in detail. In the rule generation process, the orderly attribute value reduction algorithm (OAVRA), which simplified the complexity of rule knowledge, was presented. The approach provided an effective and efficient method for knowledge acquisition that was supported by the experimentation.     

Strategies for knowledge acquisition

This paper describes knowledge acquisition strategies developed in the course of handcrafting a diagnostic system and reports on their consequent implementation in MORE, an automated knowledge acquisition system. We describe MORE in some detail, focusing on its representation of domain knowledge, rule generation capabilities, and interviewing techniques. MORE's approach is shown to embody methods which may prove fruitful to the development of knowledge acquisition systems in other domains.     

A cooperative tool for facilitating knowledge management

Nowadays, we can find systems and environments supporting processes of ontology building. However, these processes have not been specified enough yet. In this work, a real environment for integrating ontologies supplied by a predetermined set of (experts) users, who might be distributed through a communication network and working cooperatively in the integration process, is introduced. In this environment, the (expert) user can check for the ontology that is being produced, so he/she is able to refine his/her private ontology. Furthermore, the experts who take part of the ontology construction process are allowed to use their own terminology even for requesting information about the global-derived ontology until a specific instant after the integration.     

A neural network-based methodology for machining knowledge acquisition

The paper presents results of a study on collecting machining strategies for machining assistants and process planning. These efforts are being conducted at the NMSU-Integrated Manufacturing Systems Laboratory (IMSL). Goals of the project aim at improving and advancing the solicitation, documentation, and automation of machining knowledge/data acquisition, and integration with CAD/CAM/CAE systems. This paper emphasizes the knowledge acquisition phase of the study utilizing artificial neural networks.     

A vague-rough set approach for uncertain knowledge acquisition

By combining both vague sets and rough sets in fuzzy data processing, we propose a vague-rough set approach for extracting knowledge under uncertain environments. We compute all attribute reductions using the vague-rough lower approximation distribution, concepts of attribute reduction and the discernibility matrix in a vague decision information system (VDIS). Research results for extracting decision rules from the VDIS show the proposed approaches extend the corresponding method in classical rough set theory and provide a new avenue to uncertain vague knowledge acquisition.     

Integrating knowledge acquisition and language acquisition

Very large knowledge bases constitute an important step for artificial intelligence and will have significant effects on the field of natural language processing. This paper describes LUKE, a tool that allows a knowledge base builder to create an English language interface by associating words and phrases with knowledge base entities. The philosophy behind LUKE is that knowledge about language is built up at the same time as knowledge about the world. LUKE assumes no linguistic expertise on the part of the user—that expertise is built directly into the tool itself. LUKE draws its power from a large set of heuristics about how words are typically used to describe the world.This research was supported in part by the National Science Foundation under contract IRI-8858085.     

Design considerations for knowledge acquisition

Expert systems are commercially becoming more prominent and available as the breadth of applications continues to expand, eventually they could very well become the most integral part of an organization's normal operations. Knowledge acquisition is often found to be the major problem in the development of expert systems. This paper places in order the framework for proper knowledge acquisition to ensure that knowledge which is the key for any expert system is acquired effectively from an expert.     

A statistical approach to knowledge acquisition for expert systems

Classical expert systems are rule based, depending on predicates expressed over attributes and their values. In the process of building expert systems, the attributes and constants used to interpret their values need to be specified. Standard techniques for doing this are drawn from psychology, for instance, interviewing and protocol analysis. This paper describes a statistical approach to deriving interpreting constants for given attributes. It is also possible to suggest the need for attributes beyond those given.The approach for selecting an interpreting constant is demonstrated by an example. The data to be fitted are first generated by selecting a representative collection of instances of the narrow decision addressed by a rule, then making a judgement for each instance, and defining an initial set of potentially explanatory attributes. A decision rule graph plots the judgements made against pairs of attributes. It reveals rules and key instances directly. It also shows when no rule is possible, thus suggesting the need for additional attributes. A study of a collection of seven rule based models shows that the attributes defined during the fitting process improved the fit of the final models to the judgements by twenty percent over models built with only the initial attributes.     

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.     

Quantitative knowledge acquisition for expert systems

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. Whilte it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a straightforward task. This paper presents a statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. This paper describes the systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data. The development method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set. ANOVA results show that statistically different position accuracies are obtained when different navigation aids are used, the number of navigation aids is changed, the trajectory is varied, or the performance history is altered. By indicating that these four factors significantly affect the decision metric, an appropriate parameter framework was designed, and a simulation example base was created. The example base contained over 900 training examples from nearly 300 simulations. The ID3 algorithm was then applied to the example base, yielding classification “rules” in the form of decision trees. The NSM expert system consists of seventeen decision trees that predict the performance of a specified integrated navigation sensor configuration. The performance of these decision trees was assessed on two arbitrary trajectories, and the performance results are presented using a predictive metric. The test trajectories used to evaluate the system's performance show that the NSM Expert adapts to new situations and provides reasonable estimates of sensor configuration performance.     

A frame-based knowledge software tool for developing interactive robots

A knowledge-based software tool for developing interactive robot applications, called SPAK, has been developed. The “world” of interest is represented in a SPAK knowledge base by using a frame knowledge technique. This technique is chosen because it can represent the world meaningfully and naturally. Relationships among frames, which represent things in the world, and actions to be taken when certain things occur can be specified. In action, SPAK perceives changes in the environment, updates the knowledge base if needed, and generates output actions according to the knowledge contents. To support robotic applications, extensions to the conventional frame model are proposed. Various robotic applications can run cooperatively on top of SPAK. Each can easily make use of the knowledge available, and share its knowledge with others. A SPAK knowledge editor allows simple and intuitive development and modification of robot applications. To demonstrate these benefits, a prototype system and a sample robot application are developed. A multiagent technique is employed to combine various robotic components, both hardware and software, together. A sample dialogue manager for managing interactions with humans runs as an application on SPAK.