Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base

Declarative Rules for Annotated Expert Knowledge in Change Management

In this paper, we use declarative and domain-specific languages for representing expert knowledge in the field of change management in organisational psychology. Expert rules obtained in practical case studies are represented as declarative rules in a deductive database. The expert rules are annotated by information describing their provenance and confidence. Additional provenance information for the whole - or parts of the - rule base can be given by ontologies. Deductive databases allow for declaratively defining the semantics of the expert knowledge with rules; the evaluation of the rules can be optimised and the inference mechanisms could be changed, since they are specified in an abstract way. As the logical syntax of rules had been a problem in previous applications of deductive databases, we use specially designed domain-specific languages to make the rule syntax easier for non-programmers. The semantics of the whole knowledge base is declarative. The rules are written declaratively in an extension datalogs of the well-known deductive database language datalog on the data level, and additional datalogs rules can configure the processing of the annotated rules and the ontologies

Application Of Fuzzy Mathematics Methods To Processing Geometric Parameters Of Degradation Of Building Structures

The aim of research is formalization of the expert experience, which is used in processing geometric parameters of building structure degradation, using fuzzy mathematics. Materials that are used to specify fuzzy models are contained in expert assessments and scientific and technical reports on the technical condition of buildings. The information contained in the reports and assessments is presented in text form and is accompanied by a large number of photographs and diagrams. Model specification methods, based on the analysis of such information on the technical state of structures with damages and defects of various types, primarily lead to difficulties associated with the presentation of knowledge and require the formalization of expert knowledge and experience in the form of fuzzy rules. Approbation and adaptation of the rules is carried out in the process of further research taking into account the influence of random loads and fields. The scientific novelty of the work is expanding of the knowledge base due to the geometric parameters of structural degradation, on the basis of which a fuzzy conclusion about their technical state in the systems of fuzzy product rules at different stages of the object's life cycle is realized. The results of the work are presented in the form of a formalized description of the geometric parameters of degradation. The knowledge presented in the work is intended for the development of technical documentation that is used at the pre-project stage of building reconstruction, but the gained experience is the source of information on the basis of which a constructive solution is selected in the design process of analogical objects. In addition, the knowledge gained from the analysis of expert assessments of the state of various designs is necessary for development of automated expert evaluation processing systems. The use of such evaluation systems will significantly reduce the risks of the human factor associated with the errors in the specification of models for predicting the processes of structural failure at various stages of ensuring the reliability and safety of buildings

Query Rewriting and Optimization for Ontological Databases

Ontological queries are evaluated against a knowledge base consisting of an extensional database and an ontology (i.e., a set of logical assertions and constraints which derive new intensional knowledge from the extensional database), rather than directly on the extensional database. The evaluation and optimization of such queries is an intriguing new problem for database research. In this paper, we discuss two important aspects of this problem: query rewriting and query optimization. Query rewriting consists of the compilation of an ontological query into an equivalent first-order query against the underlying extensional database. We present a novel query rewriting algorithm for rather general types of ontological constraints which is well-suited for practical implementations. In particular, we show how a conjunctive query against a knowledge base, expressed using linear and sticky existential rules, that is, members of the recently introduced Datalog+/- family of ontology languages, can be compiled into a union of conjunctive queries (UCQ) against the underlying database. Ontological query optimization, in this context, attempts to improve this rewriting process so to produce possibly small and cost-effective UCQ rewritings for an input query.Comment: arXiv admin note: text overlap with arXiv:1312.5914 by other author

Knowledge-based Systems and Interestingness Measures: Analysis with Clinical Datasets

Knowledge mined from clinical data can be used for medical diagnosis and prognosis. By improving the quality of knowledge base, the efficiency of prediction of a knowledge-based system can be enhanced. Designing accurate and precise clinical decision support systems, which use the mined knowledge, is still a broad area of research. This work analyses the variation in classification accuracy for such knowledge-based systems using different rule lists. The purpose of this work is not to improve the prediction accuracy of a decision support system, but analyze the factors that influence the efficiency and design of the knowledge base in a rule-based decision support system. Three benchmark medical datasets are used. Rules are extracted using a supervised machine learning algorithm (PART). Each rule in the ruleset is validated using nine frequently used rule interestingness measures. After calculating the measure values, the rule lists are used for performance evaluation. Experimental results show variation in classification accuracy for different rule lists. Confidence and Laplace measures yield relatively superior accuracy: 81.188% for heart disease dataset and 78.255% for diabetes dataset. The accuracy of the knowledge-based prediction system is predominantly dependent on the organization of the ruleset. Rule length needs to be considered when deciding the rule ordering. Subset of a rule, or combination of rule elements, may form new rules and sometimes be a member of the rule list. Redundant rules should be eliminated. Prior knowledge about the domain will enable knowledge engineers to design a better knowledge base

Modelling Uncertainty in Physical Database Design

Physical database design can be marked as a crucial step in the overall design process of databases. The outcome of physical database design is a physical schema which describes the storage and access structures of the stored database. The selection of an ecient physical schema is an NP-complete problem. A signi cant number of eorts has been reported to develop tools that assist in the selection of physical schemas. Most of the eorts implicitly apply a number of heuristics to avoid the evaluation of all schemas. In this paper, we present an approach, based on the Dempster-Shafer theory, that explicitly models a rich set of heuristics |used for the selection of an ecient physical schema | into knowledge rules. These rules may be loaded into a knowledge base, which, in turn, can be embedded in physical database design tools.

The use of an expert system to identify pupils' misconception in science: a prototype and evaluation

In this research, the author proposes a development which contributes towards a knowledge of linking research in diagnosing student misconception in science education and the expert systems technology. Specifically, the thesis will describe the development and evaluation of a prototype diagnostic system to become a supportive tool for classroom teachers. Three topics of electricity, speed and motion graphs, and floating and sinking were selected to explore the use of expert systems technology in diagnostic testing in science. For each topic, the strategy for building the rule-based diagnostic knowledge representation is discussed. The main steps are analysis of past research literature in pupil misconceptions, building a matrix table consisting of various parameters and logical relationship between these parameters, designing the questions for eliciting the understanding and building the rule base. Finally the rule base has to be organised for encoding into a format suitable for inclusion into a generic expert system shell (Leonardo). In general, the two forms of rules contained in the knowledge base are diagnostic rules and the question sequence rules. The diagnostic rule consists of if-then statements which describes the patterns of typical science misconceptions found in the literature. Detection of a specific pattern results in descriptive diagnostic feedback. The question sequence also consists of if-then rules which are used to support the branching of questions according to previous responses. In the topic of floating and sinking, the diagnostic rule makes use of the certainty factors feature of the shell in making a decision. Both school pupils and teachers were used to validate the program. The analysis of pupils' responses suggests that the program is capable of diagnosing pupil's misconception and that new diagnosis rules can be added to the program to cater for new patterns of understanding detected by the system. The teachers responded favourably to a questionnaire regarding the user interface, the accuracy and outcomes of the questions used in the program and the accuracy of the diagnostic feedback provided by the program. In conclusion, within the limitation of the scope of the diagnosis rule base contained in the program, the research shows that such a methodology for using the available expert knowledge is feasible

Psyxpert: An Expert system for aiding psychiatrists in the diagnosis of psychotic disorders

Psyxpert is an expert computer system designed to aid psychiatrists in the diagnosis of mental disorders when psychotic features are the prominent part of the presenting clinical picture. The knowledge base contains psychiatric knowledge in the form of production rules. The system uses a backward-chaining control strategy to guide the consultation. Psyxpert provides a menu-driven user interface and an explanation subsystem. The system uses certainty and importance measures to produce a diagnosis with an attached certainty factor and recommendations for further evaluation or therapy. Psyxpert is written in Virginia Tech HC Prolog and runs on Digital Equipment Corporation\u27s VAX 11/780 under the VMS operating system