Factors influencing the Development and Adoption of Knowledge Based Decision Support Systems for Small,Owner-Operated Rural Business

This paper provides an analysis of existing literature and practical problems associated with the adoption of a developed knowledge-based decision support system (KBDSS) within small rural businesses. The rural small businesses selected for this study were individual farms within the Australian dairy industry and the developed KBDSS was called DairyPro. The object was to determine the factors that could help with future KBDSS development and improve adoption rates. These factors were tested against DairyPro to determine their effectiveness. This analysis indicates that system developers need to have a good working knowledge of the target industry and to understand the types of decisions that are made by managers in order to develop systems that will be used. A review of the literature also suggests that adoption rates can be influenced by cultural, political, educational and age factors as well as individual characteristics of information technology itself. Small business managers needed more ownership in the process of KBDSS development. The author suggests that the factors affecting KBDSS adoption by dairy farmers can be equally applicable to other small, owner-operated rural businesses. This approach advocates the use of domain experts to provide estimates of expected production levels rather than that of the traditional approach of using the results from mathematical or simulation models to make these estimates.     

An Embedded Microprocessor for Intelligent Control

The conventional approach for the implementation of the knowledge base of a planning agent, on an intelligent embedded system, is solely of software nature. It requires the existence of a compiler that transforms the initial declarative logic program, specifying the knowledge base, to its equivalent procedural one, to be programmed to the embedded systems microprocessor. This practice increases the complexity of the final implementation (the declarative to sequential transformation adds a great amount of software code for simulating the declarative execution) and reduces the overall systems performance (logic derivations require the use of a stack and a great number of jump instructions for their evaluation). The design of specialized hardware implementations, which are only capable of supporting logic programs, in an effort to resolve the aforementioned problems, introduces limitations in their use in applications where logic programs need to be intertwined with traditional procedural ones in a desired application. In this paper, we exploit HW/SW codesign methods to present a microprocessor, capable of supporting hybrid applications using both programming approaches. We take advantage of the close relationship between attribute grammar (AG) evaluation and knowledge engineering methods to present a programmable hardware parser that performs logic derivations and combine it with an extension of a conventional RISC microprocessor that performs the unification process to report the success or failure of logic derivations. The extended RISC microprocessor is still capable of executing conventional procedural programs, thus hybrid applications can be implemented. The presented implementation increases the performance of logic derivations for the control inference process (experimental analysis yields an approximate 1000% – 10 times increase in performance) and reduces the complexity of the final implemented code through the introduction of an extended C language called C-AG that simplifies the programming of hybrid procedural-declarative applications.     

The comparisons of the influences of prior knowledge on two game-based learning systems

Game-based learning provides many benefits, such as enhancing the interaction with students and stimulating their learning motivation. Thus, it is popular to learners who have diverse characteristics. To this end, individual differences play an essential role. Among various individual differences, previous studies demonstrated that prior knowledge has great effects on game-based learning. However, such studies mainly considered a single game-based learning system. To address this issue, this study examined how prior knowledge affects students' reactions to two different types of game based learning systems, i.e., the Machinarium and the CSI: Web Adventures. The former delivers procedural knowledge while the latter provides declarative knowledge. The results from this study indicate that prior knowledge has positive impacts in the CSI group while it has negative impacts in the Machinarium group. These findings imply that prior knowledge is useful for the context of declarative knowledge while it may not be helpful for the context of procedural knowledge.     

INCREMENTAL LEARNING OF PROCEDURAL PLANNING KNOWLEDGE IN CHALLENGING ENVIRONMENTS

Autonomous agents that learn about their environment can be divided into two broad classes. One class of existing learners, reinforcement learners, typically employ weak learning methods to directly modify an agent's execution knowledge. These systems are robust in dynamic and complex environments but generally do not support planning or the pursuit of multiple goals. In contrast, symbolic theory revision systems learn declarative planning knowledge that allows them to pursue multiple goals in large state spaces, but these approaches are generally only applicable to fully sensed, deterministic environments with no exogenous events. This research investigates the hypothesis that by limiting an agent to procedural access to symbolic planning knowledge, the agent can combine the powerful, knowledge-intensive learning performance of the theory revision systems with the robust performance in complex environments of the reinforcement learners. The system, IMPROV, uses an expressive knowledge representation so that it can learn complex actions that produce conditional or sequential effects over time. By developing learning methods that only require limited procedural access to the agent's knowledge, IMPROV's learning remains tractable as the agent's knowledge is scaled to large problems. IMPROV learns to correct operator precondition and effect knowledge in complex environments that include such properties as noise, multiple agents and time-critical tasks, and demonstrates a general learning method that can be easily strengthened through the addition of many different kinds of knowledge.     

A Hybrid Procedural/Deductive Executive for Autonomous Spacecraft

The New Millennium Remote Agent (NMRA) will be the first AI system to control an actual spacecraft. The spacecraft domain places a strong premium on autonomy and requires dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a wide variety of possible failures. To achieve this level of execution robustness, we have integrated a procedural executive based on generic procedures with a deductive model-based executive. A procedural executive provides sophisticated control constructs such as loops, parallel activity, locks, and synchronization which are used for robust schedule execution, hierarchical task decomposition, and routine configuration management. A deductive executive provides algorithms for sophisticated state inference and optimal failure recovery planning. The integrated executive enables designers to code knowledge via a combination of procedures and declarative models, yielding a rich modeling capability suitable to the challenges of real spacecraft control. The interface between the two executives ensures both that recovery sequences are smoothly merged into high-level schedule execution and that a high degree of reactivity is retained to effectively handle additional failures during recovery.     

Decision Planning Knowledge Representation Framework: A Case-Study

This paper discusses experiences and perspectives of utilisation of declarative knowledge structures as a convenient knowledge base medium in configuration expert systems. Although many successful systems have been developed, these are often difficult to maintain and to generalize in rapidly changing domains. In this paper we address the problem of building intelligent knowledge based systems with emphasis on their maintainability. Firstly, several industrial applications of proof planning, a theorem proving technique, will be described and their advantages and flaws will be discussed. This discussion is followed by the theoretical foundation of decision planning knowledge representation framework that, based on proof planning, facilitates separate administration of inference problem solving knowledge and the domain theory axioms. Machine learning methods for maintaining the inference knowledge to be up-to-date with permanently changing domain theory are commented and evaluated.     

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.     

Stability of adaptive feedback-based resource managers for systems with execution time variations

Today’s embedded systems are exposed to variations in load demand due to complex software applications, dynamic hardware platforms, and the impact of the run-time environment. When these variations are large, and efficiency is required, adaptive on-line resource managers may be deployed on the system to control its resource usage. An often neglected problem is whether these resource managers are stable, meaning that the resource usage is controlled under all possible scenarios. In this paper we develop mathematical models for real-time embedded systems and we derive conditions which, if satisfied, lead to stable systems. For the developed system models, we also determine bounds on the worst case response times of tasks. We also give an intuition of what stability means in a real-time context and we show how it can be applied for several resource managers. We also discuss how our results can be extended in various ways.     

The Implementation Technology of the Mercury Debugger

Every programming language needs a debugger. Mercury now has three debuggers: a simple procedural debugger similar to the tracing systems of Prolog implementations, a prototype declarative debugger, and a debugger based on the idea of automatic trace analysis. In this paper, we present the shared infrastructure that underlies the three debuggers, and describe the implementation of the procedural debugger. We give our reasons for each of our main design decisions, and show how several of these decisions are rooted in our experience with the debugging of large programs working with large data structures.     

Engineering information integration using object‐oriented mediator technology

The mechanical product development process uses many different software systems to virtually simulate the behaviour of a design. The present work deals with flexible and efficient integration using object‐oriented mediator technology that provides transparent access to distributed engineering systems. The use of mediator technology is investigated for semi‐automatically integrating engineering information resident in computer aided design systems with a Common Object Request Broker Architecture based application programming interface. The purpose is to provide engineering analysis applications access to computer aided design system information and computational methods through a declarative query language. We conclude that the use of a declarative query language for developing engineering applications shows great potential in terms of flexibility, development productivity, performance, and ease of use, compared with using a procedural programming language. The work also shows new use of mediator technology, declarative queries, and active rules within engineering information integration that traditionally is accomplished using procedural programming. Copyright © 2004 John Wiley & Sons, Ltd.     

An overview of ontologies and data resources in medical domains

The ongoing rapid growth of diversity of data and their wide use to solve different complex tasks requires more sophisticated techniques of knowledge management and automated reasoning. Recent research efforts resulted in a significant number of semantic reference systems enriched with vocabularies, thesauri, terminologies, and ontologies. The extensive use of ontologies in the mainstream computer science has spread to many other branches of knowledge. These branches are included in a new approach to building modern intelligent systems, reusing and sharing pieces of declarative knowledge.In the meanwhile, a lot of effort has been made to produce standard ontologies for medicine and biology.This paper brings an overview and presentation of the state of the art in terminologies, ontologies and important resources/systems and tools for industry and academia in medicine and biology. It could be useful for researchers involved in multidisciplinary and interdisciplinary research areas and projects that include medicine, biology, and information technology.     

基于互联网技术的计算机软硬件 资源共享机制的研究

随着我国科学技术的不断发展,先进的计算机硬件和软件成为科研开发的主要工具。因此,加强计算机软件 和硬件的应用,尽可能提升其使用效率,对提高科研开发水平产生重要影响。本文从计算机软件及硬件特点入手,分析软硬件 资源共享实现机制, 讲述计算机软硬件资源共享实现方法。     

Organizational information management strategies

Abstract. Information technology is a key factor in competitiveness. Business managers increasingly have to make decisions in this field. Their involvement in planning and decision making should be active and genuine. There are numerous methods to support information systems planning and implementation. This study focuses on the organizational approach to generating plans. An interactive framework for evolutionary information systems planning is presented together with the results of applying this framework in six organizations. The author has been working with these organizations very intensively, using an action research approach. The main emphasis is on the success of the organizational method of creating information management strategies. The main finding is that an interactive way of generating strategies is not enough. The role of internal 'project champions' in both planning and implementation is extremely important. Outside facilitators cannot replace their contribution.     

An empirical study of the use of business expert systems

The evolution of computers from computational tools to “thinking machines” is causing businesses to evaluate their views of the computer's role. The inevitable availability of smart computers leads to questions of how and when fifth generation hardware and software will be integrated into corporate culture. Here, we present the results of a survey given to information systems managers to determine the extent of expert systems development by data processing departments and expert systems usage in organizations. The attitudes of management toward the future of expert systems are also discussed using the survey data. It was discovered that, while computer managers are receptive toward this new tool, most have no definite plans to develop expert systems in the near future. These results seem to be in conflict with other evidence about the growing numbers of expert systems in business applications. One explanation is that this new technology is part of the continuing “grass roots” movement of end-user computing.     

Flexible control in expert systems for construction tasks

In most expert systems for constructional tasks, the knowledge base consists of a set of facts or object definitions and a set of rules. These rules contain knowledge about correct or ideal solutions as well as knowledge on how to control the construction process. In this paper, we present an approach that avoids this type of rules and thus the disadvantages caused by them.We propose a static knowledge base consisting of a set of object definitions interconnected by is-a and part-of links. This conceptual hierarchy declaratively defines a taxonomy of domain objects and the aggregation of components to composite objects. Thus, the conceptual hierarchy describes the set of all admissible solutions to a constructional problem. Interdependencies between objects are represented by constraints. A solution is a syntactically complete and correct instantiation of the conceptual hierarchy.No control knowledge is included in the conceptual hierarchy. Instead, the control mechanism will use the conceptual hierarchy as a guideline. Thus it is possible to determine in which respects a current partial solution is incomplete simply by syntactical comparison with the conceptual hierarchy. The control architecture proposed here has the following characteristics: separation of control and object knowledge, declarative representation of control knowledge, and explicit control decisions in the problem solving process. Thus, a flexible control mechanism can be realized that supports interactive construction, integration of case-based approaches and simulation methods.This control method is part of an expert system kernel for planning and configuration tasks in technical domains. This kernel has been developed at the University of Hamburg and is currently applied to several domains.     

The impact of DSS on organizational communication

There is some controversy in the MIS literature concerning the potential impact of computer systems on interpersonal communication in organizations. Generally, MIS researchers have found that effective communication between users and designers is an important factor in determining user satisfaction and MIS success.Recently, however, Naylor [8] has argued that decision support systems (DSS) may isolate managers from interaction with others. This is in marked contrast to the contentions of Wagner [16] and Huber [6] that DSS may lead to more effective managerial communication.We would argue that DSS encourage communication for several reasons: adaptive DSS development requires continued manager-analyst interaction; DSS-based decisions often require groups of managers; and there is a greater need for human information processing in a DSS environment.This paper reports the results of a study of the relationships between DSS usage and organizational communications. It was found that users in three different categories (managers, financial or planning analysts (FPAs), and “others”) felt that DSS usage encouraged communication. Also, significant positive relationships were found between increased communication and overall satisfaction with the DSS for all three groups. Finally, for managers and FPAs, significant positive relationships were found between increased communication and satisfaction with the DSS in decision-making activities.     

Client/Server Performance Engineering

Performance engineering helps ensure that computer systems provide timely support to end users and that managers have the information they need to make major decisions concerning hardware and software. This article outlines the goals and components of an effective performance-engineering plan.     

Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections

Analysis of hospital processes is essential for development of improved methods, policies and decision tools for overall performance improvement of the hospital system. Amidst the current scenario of continuously increasing healthcare costs and scarcity of resources, optimal utilization of resources without hampering the quality of care has gained importance in any country. Modelling, analysis and management of patient flows, in this context, plays a key role in performance analysis and improvement of hospital processes as appropriate modelling of patient flows may help healthcare managers make decisions related to capacity planning, resource allocation and scheduling, appointment scheduling and for making necessary changes in the process of care. The concept of patient flow and its modelling has gained much attention in healthcare management literature over past few decades. In this paper, the existing approaches pertaining to modelling of patient flows in hospital systems have been classified and critically appraised focussing on the recent advancements in order to identify future research avenues. A generic framework for patient flow modelling and performance analysis of hospital systems that may serve as a guide for the practitioners dealing with similar kinds of problems to improve healthcare delivery has also been provided.