ACE: An Expert System Supporting Analysis and Management Decision Making

ACE, a system for Automated Cable Expertise, is a Knowledge-Based Expert System designed to provide trouble-shooting reports and management analyses for telephone cable maintenance in a timely manner. Many design decisions faced during the construction of ACE were guided by recent successes in expert systems technology, most notably R1/XCON, the Digital Equipment Corporation Vax configuration program. The most significant departure from "standard" expert systems architectures is ACE's use of a conventional data base management system as its primary source of information. Its primary sources of knowledge are the expert users of the database system, and primers on maintenance analysis strategies. The coupling of "knowledge-base" and "data-base" demonstrates in a forceful way the manner in which an expert system can significantly enhance the throughput and quality of data processing environments supporting business management. However, further difficult problems must be solved before the expert system approach becomes a standard technique in the data processing industry

Design and Development of the Architecture and Framework of a Knowledge-Based Expert System for Environmental Impact Assessment

The development of the architecture and framework of a knowledge-based expert system (ES) named "JESEIA" for environmental impact assessment (EIA) was developed using the C Language Integrated Production System (CLIPS) that incorporates relevant expert knowledge on EIA and integrates a computational tool to support the preparation of an EIA study. The research was based on the conceptualization and development of the architecture and framework of a knowledge-based expert system that demonstrates the feasibility of integrating the following aspects: Expert knowledge-based system approach, Object-oriented techniques and rules structuring as knowledge modeling paradigm, database management system as a repository connection between domain knowledge sources and the expert system kernel, and finally EIA as a significant knowledge domain and incremental approach as a development model. This work describes the functional framework of combining shared knowledge from various experts as knowledge sources through the implementation of a blackboard system approach that organizes the solution elements and determines which information has the highest certainty to contribute to the inference solution. The rules, in the rule base, were developed according to the environmental component classification characteristics with attributes in an object-oriented technique. The developed system considers the robustness, expandability and modularity throughout its development process. The raw knowledge and database were kept in a supportive data base developed in the system for further reference or updating through the developed expert system as a built-in functionality as well as through a connection to an external data base environment through an open database connectivity mechanism

Expert system development for probabilistic load simulation

A knowledge based system LDEXPT using the intelligent data base paradigm was developed for the Composite Load Spectra (CLS) project to simulate the probabilistic loads of a space propulsion system. The knowledge base approach provides a systematic framework of organizing the load information and facilitates the coupling of the numerical processing and symbolic (information) processing. It provides an incremental development environment for building generic probabilistic load models and book keeping the associated load information. A large volume of load data is stored in the data base and can be retrieved and updated by a built-in data base management system. The data base system standardizes the data storage and retrieval procedures. It helps maintain data integrity and avoid data redundancy. The intelligent data base paradigm provides ways to build expert system rules for shallow and deep reasoning and thus provides expert knowledge to help users to obtain the required probabilistic load spectra

An architecture for heuristic control of real-time processes

Abstract Process management combines complementary approaches of heuristic reasoning and analytical process control. Management of a continuous process requires monitoring the environment and the controlled system, assessing the ongoing situation, developing and revising planned actions, and controlling the execution of the actions. For knowledge-intensive domains, process management entails the potentially time-stressed cooperation among a variety of expert systems. By redesigning a blackboard control architecture in an object-oriented framework, researchers obtain an approach to process management that considerably extends blackboard control mechanisms and overcomes limitations of blackboard systems

An intelligent alternative approach to the efficient network management

Due to the increasing complexity and heterogeneity of networks and services, many efforts have been made to develop intelligent techniques for management. Network intelligent management is a key technology for operating large heterogeneous data transmission networks. This paper presents a proposal for an architecture that integrates management object specifications and the knowledge of expert systems. We present a new approach named Integrated Expert Management, for learning objects based on expert management rules and describe the design and implementation of an integrated intelligent management platform based on OSI and Internet management models. The main contributions of our approach is the integration of both expert system and managed models, so we can make use of them to construct more flexible intelligent management network. The prototype SONAP (Software for Network Assistant and Performance) is accuracy-aware since it can control and manage a network. We have tested our system on real data to the fault diagnostic in a telecommunication system of a power utility. The results validate the model and show a significant improvement with respect to the number of rules and the error rate in others systems

LPWM expert: An expert system for water management during land preparation in a paddy estate in Malaysia.

Seberang Perak paddy estate, Malaysia, which practices intensive mechanized farming still uses the traditional approach in decision-making. Water management during land preparation, the critical process to be completed within scheduled duration, needs better and quick management decisions for many alternative scenarios. A method proposed to encapsulate specific knowledge available with domain experts and generated through modeling to an expert system (Land Preparation Water Management (LPWM) Expert) is outlined. The LPWM expert consists a database, a model base, a knowledge base and a user interface. Knowledge was gathered through discussions and interviews with domain experts. Collected quantitative data were used in modeling canal flows and water balance to extract knowledge for different possible scenarios. Knowledge base represent extracted knowledge as rules. All the rules in IF-THEN structure and syntax are verified with the help of wxCLIPS debugging capability. The results generated by the LPWM expert are validated with the domain experts. The expert system proposes decisions for many combinations of scenarios considering all the possible variations in rain, irrigation water supply, secondary blocks, sub-estates, cropping seasons and cropping intensity. The LPWM expert is user friendly and efficient where the outputs are supported with graphics

Risk management of groundwater pollution: a knowledge-based approach

Risk assessment and risk management now underpin environmental protection in the UK. Risk assessment provides for a structured and systematic analysis of a problem, and is an objective tool to inform risk management decisions. In particular, risk assessment can assist in the prioritisation of management activities to direct resources more effectively to significant risks. However, the application of risk assessment remains ad hoc and often focused on quantified approaches. The problem of how to integrate the results of a risk assessment into decisionmaking processes remains. The objective of this research was to assess whether a knowledgebased approach could be usefully applied to risk management decisions associated with the protection of groundwater. The use of a knowledge-based system offers considerable potential to support regulatory decision-making relating to environmental risks. Such systems utilise expert knowledge to solve specific problems as an expert would but without requiring specialist or skilled users. This research describes the development of a prototype decision-support system to assist non-specialist regulatory personnel, in the prioritisation of risks and management activities relating to groundwater threats from hydrocarbon point-sources. The research focused on the knowledge acquisition process using semi-structured interviews, concept sorting and risk rating to identify the type of information required by the expert in their decision-making processes and also to distinguish any differences of approach between experts and 'non-experts'. A conceptual model was developed that represented expert decision-making and problem solving. This model was used to develop the prototype decision-support system which was subsequently evaluated by experts and users, resulting in system refinements. A positive response to the usability and utility of the system was received from both expert and user groups, suggesting a knowledge-based approach can be usefully applied to risk management decisions associated with the protection of groundwater

NETMAT: A knowledge-based grid system analysis tool

The increasing expansion of electric power systems renders the power system operator's task increasingly complex. The integration into energy management systems of further analytical algorithms implies that more data has to be analysed by the control engineer. For these reasons and many others, more sophisticated tools are required by power engineers to ease the pressure under which they perform their task. The advent of knowledge-based systems has led to a new approach to the problem. The combination of expert systems and numerical algorithms can be advantageously exploited to assist the power system engineer in operating the system. This paper presents the development of a knowledge-based tool for grid system analysis. The tool, NETMAT (NETwork Modelling AssistanT) , is to be used to analyse the impact of grid system maintenance and modification procedures and of new generating plants on power utilities, and in particular on their ability to generate and sell electricity. NE TMAT consists of a number of numerical applications interfaced to an expert system shell through specific problem domain knowledge bases. Results are presented based on the use of the IEEE-30 busbar network as a test network

SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology

In this era of knowledge economy in which knowledge have become the most precious resource, surveys have shown that e-Learning has been on the increasing trend in various organizations including, among others, education and corporate. The use of e-Learning is not only aim to acquire knowledge but also to maintain competitiveness and advantages for individuals or organizations. However, the early promise of e-Learning has yet to be fully realized, as it has been no more than a handout being published online, coupled with simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by Web 2.0 technology still hardly overcome common problem such as information overload and poor content aggregation in a highly increasing number of learning objects in an e-Learning Management System (LMS) environment. The aim of this research study is to exploit the Semantic Web (SW) and Knowledge Management (KM) technology; the two emerging and promising technology to enhance the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is the backbone of SW and KM is introduced for managing knowledge especially from learning object and developing automated question answering system (Aquas) with expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to develop the Ontology in this research work. The potential of SW and KM technology is identified in this research finding which will benefit e-Learning developer to develop e-Learning system especially with social constructivist pedagogical approach from the point of view of KM framework and SW environment. The (semi-) automatic ontological knowledge base construction system (SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically whilst the Aquas with expert locator has facilitated knowledge retrieval that encourages knowledge sharing in e-Learning environment. The experiment conducted has shown that the SAOKBCS can extract concept that is the main component of Ontology from text learning object with precision of 86.67%, thus saving the expert time and effort to build Ontology manually. Additionally the experiment on Aquas has shown that more than 80% of users are satisfied with answers provided by the system. The expert locator framework can also improve the performance of Aquas in the future usage. Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS), semi-automatic ontological knowledge base construction system (SAOKBCS), automated question answering system (Aquas), Ontology, expert locator

A Knowledge Management and Decision Support Model for Enterprises

We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty