Making diagnosis explicit

What is good diagnostic practice? The answer is elusive for many medical students and equally puzzling for those trying to build effective medical decision support systems. Much of the problem lies in the difficult of 'getting at' diagnosis. Expert diagnosticians find it difficult to introspect on their own strategies, thus making it difficult to pass on their expertise.Traditional knowledge acquisition methods are designed for gathering static domain knowledge and are inappropriate for the acquisition of knowledge about the diagnos¬ tic 'task'. More advanced knowledge acquisition methodologies, particularly those which focus on the modelling of problem-solving knowledge seem to hold more promise, but are not sufficiently practicable to allow anyone other than a knowledge engineer to operate directly. Given the difficulty experts have in accessing their own diagnostic strategies what is needed is a tool which would enable diagnosticians themselves to directly formu¬ late and experiment with their own methods of diagnosis.This research describes the development of a knowledge acquisition methodology geared specifically towards the exposition of medical diagnosis. The methodology is implemented as a toolkit enabling exploration and construction of medical diagnostic models and production of model-based medical diagnostic support systems. The toolkit allows someone skilled in diagnosis to articulate their diagnostic strategy so that it can be used by those with less experience

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

3. Toward a Cognitive Theory for the Measu rement of Achievement

INTRODUCTION Given the demands for higher levels of learning in our schools and the press for education in the skilled trades, the professions, and the sciences, we must develop more powerful and specific methods for assessing achievement. We need forms of assessment that educators can use to improve educational practice and to diagnose individual progress by monitoring the outcomes of learning and training. Compared to the well-developed technology for aptitude measurement and selection testing, however, the measurement of achievement and diagnosis of learning problems is underdeveloped. This is because the correlational models that support prediction are insufficient for the task of prescribing remediation or other instructional interventions. Tests can predict fa ilure without a theory of what causes success, but intervening to prevent failure and enhance competence requires deeper understanding. The study of the nature of learning is therefore integral to the assessment of achievement. We must use what we know about the cognitive properties of acquired proficiency and about the structures and processes that develop as a student becomes competent in a domain . We know that learning is not simply a matter of the accretion of subject-matter concepts and procedures; it consists rather of organizing and restructuring of this information to enable skillful procedures and processes of problem representation and solution. Somehow, tests must be sensitive to how well this structuring has proceeded in the student being tested. The usual forms of achievement tests are not effective diagnostic aids. In order for tests to become usefully prescriptive, they must identify performance components that facilitate or interfere with current proficiency and the attainment of eventual higher levels of achievement. Curriculum analysis of the content and skill to be learned in a subject matter does not automatically provide information about how students attain competence about the difficulties they meet in attaining it. An array of subject-matter subtests differing in difficulty is not enough for useful diagnosis. Rather, qualitative indicators of specific properties of performance that influence learning and characterize levels of competence need to be identified. In order to ascertain the critical differences between successful and unsuccessful student performance, we need to appraise the knowledge structures and cognitive processes that reveal degrees of competence in a field of study. We need a fuller understanding of what to test and how test items relate to target knowledge. In contrast, most of current testing technology is post hoc and has focused on what to do after test items are constructed. Analysis of item difficulty, development of discrimination indices, scaling and norming procedures, and analysis of test dimensions and factorial composition take place after the item is written. A theory of acquisition and performance is needed before and during item design

Proceedings of the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology, Volume 1

These proceedings are organized in the same manner as the conference's contributed sessions, with the papers grouped by topic area. These areas are as follows: VE (virtual environment) training for Space Flight, Virtual Environment Hardware, Knowledge Aquisition for ICAT (Intelligent Computer-Aided Training) & VE, Multimedia in ICAT Systems, VE in Training & Education (1 & 2), Virtual Environment Software (1 & 2), Models in ICAT systems, ICAT Commercial Applications, ICAT Architectures & Authoring Systems, ICAT Education & Medical Applications, Assessing VE for Training, VE & Human Systems (1 & 2), ICAT Theory & Natural Language, ICAT Applications in the Military, VE Applications in Engineering, Knowledge Acquisition for ICAT, and ICAT Applications in Aerospace

Development of an Expert System Based Experimental Frame for Modeling of Manufacturing Systems

Industrial Engineering and Managemen

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Payload training methodology study

The results of the Payload Training Methodology Study (PTMS) are documented. Methods and procedures are defined for the development of payload training programs to be conducted at the Marshall Space Flight Center Payload Training Complex (PCT) for the Space Station Freedom program. The study outlines the overall training program concept as well as the six methodologies associated with the program implementation. The program concept outlines the entire payload training program from initial identification of training requirements to the development of detailed design specifications for simulators and instructional material. The following six methodologies are defined: (1) The Training and Simulation Needs Assessment Methodology; (2) The Simulation Approach Methodology; (3) The Simulation Definition Analysis Methodology; (4) The Simulator Requirements Standardization Methodology; (5) The Simulator Development Verification Methodology; and (6) The Simulator Validation Methodology

Developing a computational framework for explanation generation in knowledge-based systems and its application in automated feature recognition

A Knowledge-Based System (KBS) is essentially an intelligent computer system which explicitly or tacitly possesses a knowledge repository that helps the system solve problems. Researches focusing on building KBSs for industrial applications to improve design quality and shorten research cycle are increasingly attracting interests. For the early models, explanability is considered as one of the major benefits of using KBSs since that most of them are generally rule-based systems and the explanation can be generated based on the rule traces of the reasoning behaviors. With the development of KBS, the definition of knowledge base is becoming much more general than just using rules, and the techniques used to solve problems in KBS are far more than just rule-based reasoning. Many Artificial Intelligence (AI) techniques are introduced, such as neural network, genetic algorithm, etc. The effectiveness and efficiency of KBS are thus improved. However, as a trade-off, the explanability of KBS is weakened. More and more KBSs are conceived as black-box systems that do not run transparently to users, resulting in loss of trusts for the KBSs. Developing an explanation model for modern KBSs has a positive impact on user acceptance of the KBSs and the advices they provided. This thesis proposes a novel computational framework for explanation generation in KBS. Different with existing models which are usually built inside a KBS and generate explanations based on the actual decision making process, the explanation model in our framework stands outside the KBS and attempts to generate explanations through the production of an alternative justification that is unrelated to the actual decision making process used by the system. In this case, the knowledge and reasoning approaches in the explanation model can be optimized specially for explanation generation. The quality of explanation is thus improved. Another contribution in this study is that the system aims to cover three types of explanations (where most of the existing models only focus on the first two): 1) decision explanation, which helps users understand how a KBS reached its conclusion; 2) domain explanation, which provides detailed descriptions of the concepts and relationships within the domain; 3) software diagnostic, which diagnoses user observations of unexpected behaviors of the system or some relevant domain phenomena. The framework is demonstrated with a case of Automated Feature Recognition (AFR). The resulting explanatory system uses Semantic Web languages to implement an individual knowledge base only for explanatory purpose, and integrates a novel reasoning approach for generating explanations. The system is tested with an industrial STEP file, and delivers good quality explanations for user queries about how a certain feature is recognized

Third Conference on Artificial Intelligence for Space Applications, part 1

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed

A Comparative Analysis of Design Techniques for the Construction of an Expert System for Aircraft Engine Diagnostics

The lack of knowledge and understanding of diagnostic aircraft propulsion systems causes inappropriate problem diagnosis. Because of increasing complexity, technicians are incapable of performing the necessary tasks in accordance with standard regulations. More sophisticated systems are needed today to assist the user technician in decision-making. This work provided a study of rule-based and frame-based expert system techniques to determine the most appropriate solution in the domain of complex diagnosis using large amounts of deterministic data. The study produced a framework that facilitates the diagnosing of faults on aircraft engines, thus reducing the burden on the aircraft mechanic regardless of experience level. An intelligent system, the Virtually Automated Maintenance Analysis System (V AMAS), was created as a test model. It was used to compare the relative efficiency of the different expert systems techniques and the effectiveness of expert systems. One aviation malfunction problem was identified. Information collected for the Main Ignition Malfunction was developed into question sets and coded. Six specific subsets of problems were addressed. This research compared the rule-based and frame-based knowledge representation techniques using a set of evaluation factors: computational efficiency, correctness, expressiveness, and consistency. From the analysis it was concluded that the frame based knowledge representation technique ranked higher than the rule-based representation, and is suitable for use with an expert system to represent an aircraft propulsion system \u27s deterministic data