Showing papers on "Knowledge acquisition published in 1994"

The effects of instruction and experience on the acquisition of auditing knowledge

Abstract: SYNOPSIS AND INTRODUCTION: Libby (1993) suggests a simple model of the acquisition of expertise where knowledge and ability determine performance, and instruction, experience, and ability determine the acquisition of knowledge. An important implication of the model is that a detailed understanding of the knowledge acquisition process is needed before the practical implications of expertise research are evident (see also Bonner and Pennington 1991; Waller and Felix 1984). Libby's review indicates, however, that the vast majority of studies of the knowledge acquisition process focus on what knowledge auditors acquire during a particular period of time with the firm, but not on what particular aspects of instruction and experience lead to superior knowledge.1 The latter type of study is necessary before firms can determine how best to organize auditors' training and experiences to allow efficient and effective acquisition of the necessary knowledge. The importance of analytical procedures in auditing and the popularity of ratio analysis as an analytical procedure are well-documented (e.g., Biggs and Wild 1984; Libby 1985). The current study focuses on the knowledge necessary to perform ratio analysis in audit planning. Specifically, we examine the effectiveness of various combinations of instruction and experience (practice and feedback) in producing this knowledge. The results indicate that combinations of instruction and no experience or of instruction and practice without feedback do not produce knowledge. Practice with explanatory feedback and any form of instruction creates

Modeling the determinants of audit expertise

Abstract: Libby & Luft [ Accounting, Organizations and Society (1993) pp. 425–450] presented a model of the relations between experience, ability, knowledge, and performance in audit judgment. This paper extends the model by developing a framework for predicting the structure of these relations in different judgment in different judgment settings and provides an initial test of the predictions by analyzing data from Bonner & Lewis's [ Journal of Accounting Research (Supplement 1990) pp. 1–20] four tasks using LISREL. Key predictions were that problem-solving ability would directly affect performance only in unstructured tasks and would indirectly affect performance through its effect on knowledge acquisition where the learning environment was impoverished. The predictions were supported in most cases. In addition, the paper provides and tests a basis for predicting the associations between performance on different audit tasks. Construct measurement problems that need to be addressed in future research are also indicated.

Supporting knowledge-base evolution with incremental formalization

Abstract: A number of systems have been built which integrate the knowledge representations of hypermedia and knowledge-based systems. Experiences with such have shown users are willing to use the semi-formal mechanisms of such systems systems leaving much structure implicit rather than use the formal mechanisms provided. The problem remains that it is hard (1) to encode knowledge in the formal languages required by knowledge-based systems and (2) to provide support with the semi-formal knowledge found in hypermedia systems. Incremental formalization enables users to enter information into the system in an informal or semi-formal representation and to have computer support for the formalization of this information. The domain independent Hyper-Object Substrate (HOS) differs from other systems that integrate hypermedia and knowledge-based system styles of representations in that it enables the incremental addition of formalism to any piece of information in the system. HOS actively supports incremental formalization with a set of tools which suggest new formalizations to be added to the information space. These suggestions are based on patterns in the informally and semi-formally represented information and the existing formalized knowledge in the information space. An important assumption is that suggestions need not be completely accurate to be of general benefit to users. These suggestions provide a starting point which can be edited, thus changing part of of formalization from creation to modification. XNetwork, an environment the process supporting the design of computer networks, is one of several applications that have been created with HOS. Experiences with HOS show that its flexibility for incrementally adding and formalizing information is useful for the rapid prototyping and modification of semi-formal information spaces.

Automated Knowledge Acquisition

Abstract: General considerations induction algorithms using decision trees induction algorithms using progressive rule generation sub-symbolic learning methods - artificial neural networks other machine learning paradigms theoretical considerations the extraction of rules and concepts using a single-layered Hebbian neural network BRAINNE - automated knowledge acquisition using multi-layered neural network BRAINNE in the real world.

Document processing for automatic knowledge acquisition

Abstract: The knowledge acquisition bottleneck has become the major impediment to the development and application of effective information systems. To remove this bottleneck, new document processing techniques must be introduced to automatically acquire knowledge from various types of documents. By presenting a survey on the techniques and problems involved, this paper aims at serving as a catalyst to stimulate research in automatic knowledge acquisition through document processing. In this study, a document is considered to have two structures: geometric structure and logical structure. These play a key role in the process of the knowledge acquisition, which can be viewed as a process of acquiring the above structures. Extracting the geometric structure from a document refers to document analysis; mapping the geometric structure into logical structure is regarded as document understanding. Both areas are described in this paper, and the basic concept of document structure and its measurement based on entropy analysis is introduced. Logical structure and geometric models are proposed. Both top-down and bottom-up approaches and their entropy analyses are presented. Different techniques are discussed with practical examples. Mapping methods, such as tree transformation, document formatting knowledge and document format description language, are described. >

Designing Interactive Learning

Abstract: Interactive learning is a necessary and fundamental mechanism for knowledge acquisition and the development of both cognitive and physical skills. Before designing interactive learning resources it is important to understand how interactivity works and the nature of the environments that are needed to support it. The effectiveness of these environments also needs to be examined. This paper discusses the basic nature of interactive learning and describes some models to support the design and fabrication of interactive learning systems. A case study describing the application of these models to the creation of multimedia courseware for interactive language learning is then presented.

Investment in knowledge: a generalization of learning by experience

Abstract: Learning is often perceived as a cost-reducing endogenous by-product of production processes. In many applications this by-product is modeled as a learning curve; that is, a simple function of time or of cumulative production experience. In an earlier paper we presented an alternative explanation where managers decide what resources to devote to knowledge acquisition. In this paper we expand those results to a situation using a more flexible production technology and emphasizing discounted cost. Our model explains resource and output behavior for a firm that is producing specialized units to contractual order. However, the results are quite general and have implications for investment in research, engineering, science and technology, software development, and worker training. We provide examples where the cost-minimizing producer will choose to invest in knowledge creation early in the production program and then have the rate of investment decline over time. Other interesting results are noted by examining the optimal time paths of the control and state variables in a comparative dynamic analysis.

Overcoming process delays with decision tree induction

Abstract: Printers are always seeking higher productivity by increasing their production rates and minimizing process delays. When process delays have known causes, they can be mitigated by acquiring causal rules from human experts and then applying sensors and automated real-time diagnostic devices to the process. However, for some delays the experts have only weak causal knowledge or none at all. In such cases, machine learning tools can collect training data and process it through an induction engine in search of diagnostic knowledge. We have applied a machine learning strategy known as decision tree induction to derive a set of rules about a long-standing problem in rotogravure printing. The induction mechanism is embedded within a knowledge acquisition system that suggests plausible rules to an expert, who can override the rules or modify the data from which the rules were derived. By using decision tree induction to derive process control rules, this system lets experts participate in knowledge acquisition by doing what they do best: exercising their expertise. >

Generation of knowledge-acquisition tools from domain ontologies

Abstract: Metalevel tools can support the software development process by automating the design of task- and application-specific tools. DASH is a metalevel tool that allows developers to generate domain-specific knowledge-acquisition tools from domain ontologies. Domain specialists use the knowledge-acquisition tools generated by DASH to instantiate the concepts and relationships defined in the domain ontologies. The output of the knowledge-acquisition tools is a collection of instances that constitute the knowledge base for a knowledge-based system. To automate the generation of appropriate tools, the DASH architecture uses a dialog-design module to produce a dialog structure that defines the target tool at the editor and window level. Given the dialog structure, a layout-design module completes the window layouts. DASH allows the developer to custom tailor the layout of the knowledge-acquisition tool for its users, and to store such modifications persistently so that they can be reapplied when the target tool is regenerated. The DASH implementation is based on a mapping problem-solving method that defines the tool-design steps. The DASH Development Environment (DDE) is an application-specific environment that supports the configuration of the mapping method and the maintenance of DASH. We have used DASH to generate several knowledge-acquisition tools for a broad range of application tasks.

Learning and Transfer of Spatial Knowledge in a Virtual Environment

Abstract: Two experiments were conducted to investigate route and configurational knowledge acquisition in a virtual environment (VE). The results indicate that route knowledge can be acquired in a VE and th...

From decision tables to expert system shells

Abstract: Building and maintaining high quality knowledge based systems is not a trivial task. Decision tables have sometimes been recommended in this process, mainly in verification and validation. In this paper, however, it is shown how decision tables can also be used to generate, and not just to validate, knowledge bases and how the transformation process from decision tables to knowledge bases can be organized. Several options to generate rules or other knowledge representation from decision tables are described and evauluated. The proposed generation strategy enables the knowledge engineer to concentrate on the acquisition and modelling issues and allows him to isolate the knowledge body from its implementation. The generation process has been implemented for two commercial tools, AionDS and KBMS and has been applied to real world applications.

Decision-Analytic Intelligent Systems: Automated Explanation and Knowledge Acquisition

Abstract: This book presents a framework for building intelligent systems based on the mathematical decision models of Decision Analysis. The author provides new techniques for automated explanation and knowledge acquisition in formally sound systems that reason about complex tradeoffs in decisions. Also included are specifications for implementing these techniques in computer programs, along with demonstration applications in marketing, process control, and medicine. Readers with an interest in artificial intelligence will gain a foundation for building formally justifiable, intelligible, modifiable systems for computing decisions involving multiple considerations, with applications across a variety of domains. Beyond decision models, the methodology of the work reported suggests a more general approach to employing formal mathematical models in transparent intelligent systems. Decision-analysis experts will find a collection of methods for explaining decision-analytic advice to clients in intuitive terms, for simplifying parameter assessment, and for managing changing preferences over time. The book provides sufficient background material to promote understanding by readers who may be unfamiliar with artificial intelligence, with decision analysis, or with both fields, and such material is labeled to increase the well-versed reader's efficiency in skipping particular sections.

Using knowledge acquisition and representation tools to support scientific communities

Abstract: Widespread access to the Internet has led to the formation of geographically dispersed scientific communities collaborating through the network The tools supporting such collaboration currently are based primarily on electronic mail through mailing list servers, and access to archives of research reports through ftp, gopher and world wide web However, electronic communication can support the knowledge processes of scientific communities more directly through overtly represented knowledge structures This paper describes some experiments in the use of knowledge acquisition (KA) and representation (KR) tools to define and analyze major policy and technical issues in an international research community responsible for one of the test cases in the Intelligent Manufacturing Systems (IMS) research program It is concluded that distributed knowledge support systems in routine use by world-class scientific communities collaborating through the Internet will provide a major impetus to artificial intelligence research

Method of construction of hierarchically organized procedural node information structure including a method for extracting procedural knowledge from an expert, and procedural node information structure constructed thereby

Abstract: A knowledge acquisition tool for direct use by an expert in the automatic creation of a knowledge base derived from the knowledge of the expert, the knowledge acquisition tool including an input device usable by the expert for providing knowledge to the tool in response to questions, statements and/or prompts from the tool, a display for displaying the knowledge, questions, statements and prompts so that the expert can interact with the tool in creating the knowledge base, and a processor connected to the input device and the display for supplying the questions, statements and prompts to the display in order to extract the knowledge from the expert in the creation of the knowledge base incorporating the knowledge provided by the expert through use of the input device.

SodaJack: An Architecture For Agents That Search For And Manipulate Objects

Abstract: This paper presents an architecture for agents that search for and manipulate objects It is demonstrated in the SodaJack system a system that animates a human working at a soda fountain The system is constructed as a set of three interacting planners Two of these planners are special purpose modules which contribute context speci c plans for the tasks of searching for and manipu lating objects The search planner is used to convert knowledge acquisition goals into goals of searching locations An object spe ci c reasoner is used to build object sensitive plans for manipulat ing speci c objects Finally an incremental hierarchical planner is used to integrate these two special purpose planners into a complete system which interleaves planning and acting

Components of Problem Solving and Types of Problems

Abstract: A typology of problems is presented that is used for indexing and accessing reusable problem solving components in a library that supports the CommonKADS methodology for building knowledge based systems. Eight types of problems, such as planning, assessment etc., are distinguished, and their dependencies are explained. These dependencies suggest that the typology is to be viewed as a “suite” rather than the usual taxonomy of “generic tasks”. Developing the suite has lead to some new insights and elaborations of [Newell and Simon, 1972]'s theory for modeling problem solving.

Representing, analyzing, and synthesizing biochemical pathways

Abstract: This article describes computational problems that arise in representing knowledge about the metabolism in electronic form, in analyzing that knowledge to gain deeper insights into complexities of the metabolism, and in applying that knowledge to biology, biotechnology, and health care These problems push the limits of existing techniques for knowledge representation, planning, integration of heterogeneous databases, qualitative reasoning, knowledge acquisition and machine learning This article surveys several recent and ongoing efforts to solve these problems, including the EcoCyc project, a collaborative effort of the Marine Biological Laboratory, SRI International, and the National Library of Medicine >

A knowledge-based image-inspection system for automatic defect recognition, classification, and process diagnosis

Abstract: Combining knowledge-based processing with image processing is a key issue in the future of the visual inspection of complex patterns such as offset prints. Often the class of the defect determines the state of the process, which must known for eliminating the cause of the defect. We describe the architecture of such a complex knowledge-based inspection system. The system has been used for defect recognition and misprint diagnosis in offset printing, but it is flexible enough for other applications. The system is based on a set of general and powerful tools for the knowledge interpretation of sensor signals. An object-oriented concept and task-dependent algorithms for efficient image processing are implemented. The paper concentrates on four points: integration of the system in the offset printing process, a description of the system architecture, knowledge acquisition, and implementation results.

Corpus-driven knowledge acquisition for discourse analysis

Abstract: The availability of large on-line text corpora provides a natural and promising bridge between the worlds of natural language processing (NLP) and machine learning (ML). In recent years, the NLP community has been aggressively investigating statistical techniques to drive part-of-speech taggers, but application-specific text corpora can be used to drive knowledge acquisition at much higher levels as well. In this paper we will show how ML techniques can be used to support knowledge acquisition for information extraction systems. It is often very difficult to specify an explicit domain model for many information extraction applications, and it is always labor intensive to implement hand-coded heuristics for each new domain. We have discovered that it is nevertheless possible to use ML algorithms in order to capture knowledge that is only implicitly present in a representative text corpus. Our work addresses issues traditionally associated with discourse analysis and intersentential inference generation, and demonstrates the utility of ML algorithms at this higher level of language analysis. The benefits of our work address the portability and scalability of information extraction (IE) technologies. When hand-coded heuristics are used to manage discourse analysis in an information extraction system, months of programming effort are easily needed to port a successful IE system to a new domain. We will show how ML algorithms can reduce this development time to a few days of automated corpus analysis without any resulting degradation of overall system performance.

Knowledge refinement in a reflective architecture

Abstract: A knowledge acquisition tool should provide a user with maximum guidance in extending and debugging a knowledge base, by preventing inconsistencies and knowledge gaps that may arise inadvertently. Most current acquisition tools are not very flexible in that they are built for a predetermined inference structure or problem-solving mechanism, and the guidance they provide is specific to that inference structure and hard-coded by their designer. This paper focuses on EXPECT, a reflective architecture that supports knowledge acquisition based on an explicit analysis of the structure of a knowledge-based system, rather than on a fixed set of acquisition guidelines. EXPECT's problem solver is tightly integrated with LOOM, a state-of-the-art knowledge representation system. Domain facts and goals are represented declaratively, and the problem solver keeps records of their functionality within the task domain. When the user corrects the system's knowledge, EXPECT tracks any possible implications of this change in the overall system and cooperates with the user to correct any potential problems that may arise. The key to the flexibility of this knowledge acquisition tool is that it adapts its guidance as the knowledge bases evolve in response to changes introduced by the user.

Local Patching Produces Compact Knowledge Bases

Abstract: Knowledge acquisition (KA) encompasses working with the expert to model the domain and a suitable problem solving method as preconditions for building a knowledge based system (KBS) and secondly working with the expert to populate the knowledge base. Ripple Down Rules (RDR) focuses on the second of these activities and allows an expert to populate a knowledge base (KB) without any knowledge engineering assistance. It is based on the idea that since the knowledge an expert provides is a justification of his or her judgment given in a specific context, this knowledge should only be used in the same context. Although the approach has been used for large single classification systems, it has the potential problem that the local nature of the knowledge may result in much repeated knowledge in the KB and much repeated knowledge acquisition. The study here attempts to quantitate and compare KB size and performance for systems built by experts with various levels of expertise and also inductively. The study also proposes a novel way of conducting such studies in that the different levels of expertise were achieved by using simulated experts. The conclusion from this study is that experts are likely to produce reasonably compact and efficient knowledge bases using the Ripple-Down Rule approach.