Knowledge Acquisition Facilities Research Articles

Modularized Cutting Tool Selection Expert System

This paper is to present a rule-based cutting tool selecting expert system which has knowledge modules and rule bases. Besides, according to different process targets, the selection progress will apply corresponding constraints and rule modules. The logic of tool selection follows a decision-making procedure as an experienced engineers. The strategy of system is to guide the user through several standard steps: information input; feature recognition; selection of machining method; selection of tool material and type; calculation of process parameter and solving cutting problem. This system also has a modularized structure which allows adding new functions and new modules to expand knowledge base and data base. Modules involves in this system are composed of the user interface, knowledge acquisition facility, explanation facility, the knowledge base module, the inference engine and the database module.

University experts knowledge acquisition: Investigating knowledge engineering system approach

Since knowledge plays a vital role in all acts of contemporary organizations, one of the main purposes of expert systems and artificial intelligence applications is to capture the expert’s knowledge and make it accessible to everyone. This study attempts to investigate a Knowledge Engineering System (KES) model, which proposes a set of elements and processes that facilitates this function. Data are gathered via questionnaires administered to academics staff in selected universities in order to spot their attitudes towards KES elements and determine the appropriate components and operations for the proposed KES, as well as to consider the extent to which all its factors are accepted by the respondents. The findings reveal that the respondents possess various levels of information and experience in certain aspects of the proposed KES, and adopt various practices which help significantly in the system implementation. Key words: Knowledge acquisition facility (KAF), academic expert, informationtechnology system components, knowledge engineering (KE), knowledge-base.

A New Environment for Developing Fuzzy Expert Systems

This work presents a grid-based fuzzy expert system environment, which employs a repertory grid, fuzzy theory and case-based reasoning. The proposed environment provides friendly user interfaces, abundant data types, intelligent knowledge acquisition facilities and an efficient inference engine. To help users make precise decisions, three factors (i.e., feasibility, reliability and certainty) are used during inference. Moreover, several tools are provided to help experts define membership functions and the knowledge of multimedia data types. Furthermore, the proposed approach is evaluated by implementing an expert system for the diagnosis of animal diseases and by performing several tests.

Representing geotechnical properties in a knowledge-based system

A knowledge-based system for the estimation of geotechnical design parameters is described. The form of representation and the way in which this has been implemented using an object-oriented knowledge-based system development environment (ProKappa) are explained. Three knowledge bases for Ground Types, Ground Parameters and `Typical' Values have been established as object hierarchies. The knowledge bases hold ranges of typical values for geotechnical parameters for a wide variety of ground types. Different qualitative properties of the ground are also represented. A user interface allows identification of the soil or rock type for which properties are to be estimated and this is matched at the appropriate level of detail within the knowledge bases. Estimation can be done both in terms of the ground type and qualitative properties such as plasticity, consistency, mineralogy, particle shape, etc. The knowledge representation scheme adopted allows combinations of qualitative properties to be considered. A knowledge acquisition facility has also been provided to allow users to add their own knowledge to the system. Users can add new parameters, extend the number of qualitative properties considered and even establish new ground types.

An interactive environment for the model-based design of analog circuits

A powerful environment for the design of analog circuits is presented. Additionally to its interactive facilities some novel features will be highlighted, including the underlaying model based approach itself. Due to integrated knowledge acquisition facilities the user is enabled to put his own expertise into the system.

Initial results from the Intelligent Monitoring System

AbstractThe Intelligent Monitoring System (IMS) integrates advanced technologies in a knowledge-based distributed system that automates most of the seismic data interpretation process. Results from IMS during its first 8 weeks of operation (1 October through 25 November 1989) are analyzed to evaluate its performance. During this test period, the IMS processed essentially all data recorded by the NORESS and ARCESS high-frequency arrays in Norway. The emphasis was on detection and location of regional events within 2,000 km of these arrays. All events were reviewed and corrected if necessary by a skilled analyst. The final IMS Bulletin for the period includes 1,580 regional events (∼280 events/day). Approximately 55 per cent were smaller than MLg 1, with the largest just over MLg 3.Comparison of IMS locations in southern Finland and northwestern USSR (800 to 900 km from both arrays) with event locations from the University of Helsinki's local network bulletin are used to assess the detection and location capabilities of the system. Two or more phases (minimum needed to locate) were detected for 96 per cent of the events with magnitude greater than 2.5. The median separation between the IMS and Helsinki locations for all common events was 23.5 km. A consistent bias in arrival-time and azimuth residuals was observed for events in small geographic areas, indicating that refined travel-time models and path corrections could further improve location accuracy.The knowledge base in this first version of IMS was based on analysis of NORESS data, and many of the errors in interpretation corrected by the analysts can be attributed to differences encountered when this knowledge is used to interpret ARCESS data. Nevertheless, nearly 60 per cent of the events appearing in the final bulletin are automatic solutions approved without change or moved (by analyst corrections) less than 25 km from the automatic locations. The IMS had the most difficulty interpreting the overlapping signals generated by closely spaced explosions commonly detonated at mines in the Kola Peninsula and northern Sweden. Using the knowledge acquisition facilities included in the system, the deficiencies responsible for these and other errors are isolated, leading to development of new knowledge to be incorporated in the next version of the IMS knowledge base.

MED1: an intelligent computer program for thoracic pain diagnosis.

MED1 is a fully implemented, medical expert system providing assistance in the diagnosis of patients complaining of chest pain. Its reasoning strategy combines efficient mechanisms for hypothesis generation and hypothesis evaluation in a model simulalting the basic features of the hypothesize-and-test approach found to be applied by diagnosing physicians. The knowledge acquisition facility of the program is comfortable enough to allow the expert physician to alter the knowledge base without understanding the basic code (LISP) of the program.