Intelligent knowledge acquisition system

IKAS (Intelligent Knowledge Acquisition System) is an expert system building shell with a machine-assisted concept checker for easy integrated rule-entry. Unlike most expert system shells currently available, IKAS not only performs rule syntax checking, but also maintains semantic integration of the knowledge base by using a Concept Base. The Concept Base is incrementally grown during the cycle of expert system application development and safeguards the whole domain from conceptually conflicting rules by finding possible inconsistencies or duplications of concepts and giving recommendations for possible solutions. Also automatic generalized concept forming using the Inductive Extension Generalization method gives IKAS a capability to discover new inductive hypotheses from the existing knowledge base. IKAS can be an advancement for the current expert system technology in terms of complex knowledge representation and utilization

Multisensor Data Fusion Implementation for a Sensor Based Fertilizer Application System

"Mapping systems" (“mapping approach”), real-time sensor-actuator systems ("sensor approach") or the combination of both (“Real-time approach with map overlay”) determine the process control in mobile application systems for spatially variable fertilization. Within the integrated research project “Information Systems Precision Farming Duernast” (IKB Duernast) the implementation of the “Real-time approach with map overlay” was done for intensive nitrogen fertilization. The bottom line of this sophisticated approach is a comprehensive situation assessment, a typical multisensor data fusion task. Based on a functional and procedural modelling of the multisensor data fusion and decision making process, it could be pointed out that an expert system is an adequate fusion paradigm and algorithm. Therefore, a software simulation with an expert system as core element was implemented to fuse on-line sensor technology measurements (REIP), maps (yield, EM38, environmental constraints, draft force) and user inputs in order to derive an application set point in real-time. The development of an expert system can be viewed as a structured transformation in five levels from the “specification level”, the “task level”, the “problem solving level” and the “knowledge base level” to the “tool level”. In the “tool level” the hybrid expert system shell JESS (Java Expert System Shell) was selected for implementation due to the results of preceding levels. Knowledge acquisition was done within another IKB-subproject by the means of data mining. Typical and maximal times of 10 ms and 60 ms for one fusion cycle were measured running this application on a 32-bit processor hardware (Intel Pentium III Mobile, 1 GHz)

Hospitality unit diagnosis: an expert system approach

Formal methods of management problem-solving have been extensively researched. However, these concepts are incomplete in that they assume a problem has been correctly identified before initiating the problem-solving process. In reality management may not realise that a problem exists or may identify an incorrect problem. As a result, considerable time and effort may be wasted correcting symptoms rather than the true problem. This research describes the development of a computerised system to support problem identification. The system focuses specifically on the area of hospitality management, encompassing causes and symptoms of prominent problems in the hospitality industry. The system is based on knowledge rather than data. Research has shown that Expert Systems allow reasoning with knowledge. As a result, Expert Systems were selected as an appropriate technology for this application. Development is undertaken from the perspective of a hotel manager, using appropriate software development tools. The required knowledge is generally obtained from either expert interviews or textbook analysis. Gaining commitment from sufficient industry experts proved too difficult to allow the use of the former method, and therefore the latter method was utilised. However, knowledge acquired in this manner is limited in both quality and quantity. In addition, essential experience based judgmental knowledge is not available from this source. To counteract this, the personal knowledge of the author, a qualified hotel manager, was used. When developing an Expert System, knowledge acquisition and representation are of paramount importance. In this research, these issues are problematic due to the broad interdisciplinary nature and scope of hospitality management. To counteract this problem, some structure was required. Finance, Marketing, Personnel, Control, and Operations were selected as important functions within the hospitality business and therefore were represented within the system for diagnosis. A modular approach was used with modules being developed for each functional area. An initial top level module performs a general diagnosis, and then separate subordinate modules diagnose the functional areas. This research established that the knowledge required for incorporation into such a system is not available. The possibility of acquiring this knowledge is beyond the bounds of this research. However, sufficient marketing knowledge was sourced to facilitate the development of the Expert System structure. This structure demonstrates the application of the technology to the task and could subsequently be used when more knowledge is elicited. The research findings show that the development of a modular diagnostic system is possible using an Expert System Shell. The major limiting factor encountered is the total lack of the relevant knowledge. As a result, further research is recommended to establish the factors influencing diagnosis in the hospitality industry

A reusable knowledge acquisition shell: KASH

KASH (Knowledge Acquisition SHell) is proposed to assist a knowledge engineer by providing a set of utilities for constructing knowledge acquisition sessions based on interviewing techniques. The information elicited from domain experts during the sessions is guided by a question dependency graph (QDG). The QDG defined by the knowledge engineer, consists of a series of control questions about the domain that are used to organize the knowledge of an expert. The content information supplies by the expert, in response to the questions, is represented in the form of a concept map. These maps can be constructed in a top-down or bottom-up manner by the QDG and used by KASH to generate the rules for a large class of expert system domains. Additionally, the concept maps can support the representation of temporal knowledge. The high degree of reusability encountered in the QDG and concept maps can vastly reduce the development times and costs associated with producing intelligent decision aids, training programs, and process control functions

Knowledge acquisition for case-based reasoning systems

Case-based reasoning (CBR) is a simple idea: solve new problems by adapting old solutions to similar problems. The CBR approach offers several potential advantages over rule-based reasoning: rules are not combined blindly in a search for solutions, solutions can be explained in terms of concrete examples, and performance can improve automatically as new problems are solved and added to the case library. Moving CBR for the university research environment to the real world requires smooth interfaces for getting knowledge from experts. Described are the basic elements of an interface for acquiring three basic bodies of knowledge that any case-based reasoner requires: the case library of problems and their solutions, the analysis rules that flesh out input problem specifications so that relevant cases can be retrieved, and the adaptation rules that adjust old solutions to fit new problems

The Feasibility of Using Expert Systems in the Management of Human Resources

The purpose of this paper is to introduce a decision aid that is being used increasingly in the business world, the expert system, and to begin to examine its potential for human resource management. First, the expert system technology is reviewed, with a special emphasis on the players, those involved in developing and using the system, and the parts, the three main components of a system. This is followed by an analysis of the costs and benefits and the advantages and disadvantages that have been ascribed to expert systems. We conclude this initial research endeavor by presenting some preliminary findings which suggest that employees are willing to cooperate with expert systems, even those that require personal information, and that they see some benefits to using expert systems as decision aids

A knowledge based system for linking information to support decision making in construction

This work describes the development of a project model centred on the information and knowledge generated and used by managers. It describes a knowledge-based system designed for this purpose. A knowledge acquisition exercise was undertaken to determine the tasks of project managers and the information necessary for and used by these tasks. This information was organised into a knowledge base for use by an expert system. The form of the knowledge lent itself to organisation into a link network. The structure of the knowledge-based system, which was developed, is outlined and its use described. Conclusions are drawn as to the applicability of the model and the final system. The work undertaken shows that it is feasible to benefit from the field of artificial intelligence to develop a project manager assistant computer program that utilises the benefit of information and its link