Automatic Knowledge Acquisition Research Articles

Generating interpretable fuzzy rules for adaptive job dispatching

Adaptive scheduling is an approach that selects and applies the most suitable strategy considering the current state of the system. The performance of an adaptive scheduling system relies on the effectiveness of the mapping knowledge between system states and the best rules in the states. This study proposes a new fuzzy adaptive scheduling method and an automated knowledge acquisition method to acquire and continuously update the required knowledge. In this method, the criteria for scheduling priority are selected to correspond to the performance measures of interest. The decisions are made by rules that reflect those criteria with appropriate weights that are determined according to the system states. A situated rule base for this mapping is built by an automated knowledge acquisition method based on system simulation. Distributed fuzzy sets are used for evaluating the criteria and recognizing the system states. The combined method is distinctive in its similarity to the way human schedulers accumulate and adjust their expertise: qualitatively establishing meaningful criteria and quantitatively optimizing the use of them. As a result, the developed rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop effective and robust rules.

Automated knowledge acquisition for design and manufacturing: The case of micromachined atomizer

In order to remain competitive in the global market, original equipment manufacturers (OEMs) are developing a process-based, knowledge-driven product development environment with emphasis on the acquisition, storing, and utilization of manufacturing knowledge. This is usually achieved by using the symbolic artificial intelligence (AI) approach. Specifically, knowledge-based expert systems are developed to capture human expertise, mostly in terms of IF–THEN production rules. It has been recognized that the development of symbolic knowledge-based expert systems suffers from the so-called knowledge acquisition bottleneck. Knowledge acquisition is the process of collecting domain knowledge and transforming the knowledge into a computerized representation. It is a challenging and time-consuming process due to the difficulties involved in eliciting knowledge from human experts. This paper presents an automated approach for knowledge acquisition by integrating neural networks' learning ability and fuzzy logic's structured knowledge representation. Using this approach, knowledge is automatically acquired from data and represented using humanly intelligible fuzzy rules. The approach is applied to a case study of the design and manufacturing of micromachined atomizers for gas turbine engine. The influence of geometric features on the performance of the atomizers is investigated. The results are then compared with those obtained using traditional regression analysis approach (abstract mathematical models). It was found that the automated approach provides an efficient means for knowledge acquisition. Since the fuzzy rules extracted are easy to understand, they can be used to allow more clear specification of manufacturing processes and to shorten learning curves for novice manufacturing engineers.

Automatic Acquisition of Search Control Knowledge from Multiple Proof Attempts

We present two inference control heuristics for equational deduction that are based on the evaluation of previous successful proof attempts in domains of interest. The first evaluation function works by symbolic retrieval of generalized patterns from a knowledge base, and the second function compiles the knowledge into abstract term evaluation trees. Both heuristics have been implemented into the distributed equational proof system DISCOUNT. We analyze the performance of the heuristics on several sets of examples (including the subset of all unit-equality problems from the TPTP collection) and demonstrate their usefulness.

Self-instructive spreadsheets: an environment for automatic knowledge acquisition and tutor generation

Typically, spreadsheet applications are difficult to use for casual users (different from developers), mainly because of lack of support. In fact, building a tutoring facility for such applications is a time-consuming task. Our aim is the realization of a tool for the automatic generation of Intelligent Tutors for conventional spreadsheet applications. We have developed a system that works in two steps. In the first one, it extracts an explicit representation of the problem-solving pattern coded in a programmed spreadsheet. In the second step, it generates a hypertextual guide and an interactive tutor that can effectively support, in the native environment, the casual user of the spreadsheet with the specific application it is designed for. We have successfully tested our system on a class of students using an application for budget analysis.

A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems

The building of intelligent monitoring and diagnostic systems for complex industrial domains tends to be hindered by the knowledge-acquisition bottleneck. Creating good knowledge bases for such tasks is notoriously difficult, especially where human experts are not readily available. High dimensionality of the domain attributes presents a further obstacle for a number of rule-induction algorithms which would, otherwise, have the potential for automating knowledge acquisition. This paper attempts to tackle both problems, by proposing a highly modular framework for data-driven fuzzy ruleset induction incorporating a dimensionality-reduction step based on rough set theory. This removes redundant and information-poor attributes from the data, thereby significantly increasing the speed of the induction algorithm, which is employed to generalise historic data into fuzzy association rules. The aid of dimensionality reduction extends past the training stage of the system into its runtime. By removing information-poor attributes, the implemented system is kept simple by requiring fewer connections to physical instrumentation, while the system’s response times are increased. The paper introduces the techniques jointly forming the proposed framework, and demonstrates the applicability of the approach by building a monitoring system for an urban water treatment plant. The results of this application are presented and discussed, and comparisons to alternative approaches are given.

The application of automatic acquisition of knowledge to mix design of concrete

In this paper, an automatic knowledge-acquisition system based on neural networks is created to design concrete mix. This system consists of three models: the mix-design model, the slump-prediction model, and the strength-prediction model; the first model is the core of the system with the other two models supporting the core. Each model is made up of a mix-design database, a knowledge base, a neural network-learning block, and a problem solution block. The automatic acquisition of knowledge is realized through the learning process of a neural network from sample mix designs. The knowledge base is the network itself. This system not only makes full use of the workable mix designs that are already in existence but also provides a quick means to predict the slump and 28-day compressive strength of ready-made concrete. Examples and experimental work show that the application of the system to concrete mix design is practicable.

SCANKEE—computer system for interpretation of infrared spectra

Elucidation of the structure of an unknown compound may be achieved by using various methods of spectral interpretation. The method presented in this paper enables the recognition of structural fragments present in the molecule of an analyzed substance. Our new approach consists in generating spectra–structure correlations from a computer library of infrared spectra, e.g. an automated knowledge acquisition from an infrared spectral database. This algorithm imitates the human process of interpreting an infrared spectrum of an unknown compound. Acquisition of spectral information (a list of structural fragments and their characteristic spectral parameters) is done in order to enable identification of an unknown compound's molecular fragments from its infrared spectrum. The spectra–structure correlations are automatically transformed into rules for substructure identification. A set of rules forms a knowledgebase becoming then a part of the expert system for structure elucidation. The results of testing this system for a selected set of substructures is presented in this paper.

An integrated database and expert system for failure mechanism identification: Part I—automated knowledge acquisition

An integrated database and expert system has been developed for identifying the failure mechanism of mechanical components. The system comprises six major modules: database and management system, case maintenance; knowledge acquisition and editing; expert system; explanation and test-recommendation facilities; and user interface. Part I of a two-part paper details the knowledge acquisition and editing module, as presented here. Part II describes the remaining modules and also gives test results (Liao TW, Zhan Z-H, Mount CR. Engineering Failure Analysis 1999;6:387–406). The method used for automated knowledge acquisition is an inductive learning algorithm, which was modified from PRISM (Cendrowska J. Int J. Man–Machine Studies 1987;27:349–70) to handle noisy and missing data. Using the algorithm, a total of 48 rules were induced from 477 training examples gathered for the identification of 15 different failure mechanisms such as brittle fracture, fatigue, and stress corrosion cracking. Fifty-nine attributes were used to distinguish one failure mechanism from the others. They include pitted, beach marks, microvoids, etc. The knowledge editing function is provided to allow the verification of induced rules by the human expert.

A neuro-fuzzy computational approach to constructability knowledge acquisition for construction technology evaluation

This paper describes a methodology for constructability knowledge acquisition of construction technologies. The methodology combines a neuro-fuzzy network-based approach with genetic algorithms. The combination of fuzzy logic with learning abilities of neural networks and genetic algorithms may allow for automatic acquisition of constructability knowledge from training examples and for providing understandable explanations for the reasoning process. The proposed methodology can provide a mechanism to trace back factors causing unsatisfactory construction performance and the necessary feedback to construction engineers for technology innovation. An application example is provided to demonstrate the capabilities of the proposed methodology.

Knowledge acquisition assisted by CLIPS programming

Knowledge acquisition is important and machine learning techniques can be used to achieve automated knowledge acquisition. This article examines how knowledge acquisition can be assisted by programming using CLIPS (an acronym for C Language Integrated Production System). A machine learning preprocessor has been developed for the CLIPS environment, so that the CLIPS rule-base can be expanded by adding rules generated through machine learning techniques. The paper also shows how knowledge updating can be supported in the CLIPS environment itself. Operational engineering knowledge is captured in a data structure called a decision tree, and its structure can be updated when new knowledge is acquired. In addition, some advanced features are also briefly discussed, including using COOL (the CLIPS object-oriented language) for knowledge acquisition in a software product recommendation system, as well as the design of a self-evolving knowledge-acquisition tool.

ICTRL: Intensional conformal text representation language

A new compact and homogeneous symbolism is introduced to achieve a more general and exact representation of natural language texts. Traditional first-order and intensional logic cannot cope with numerous natural language phenomena such as the large variety of modalities, satisfactory interpretation of iterative application of modal operators or certain modelling problems like one-to-one sentence–formula mapping. The CTRL/iCTRL formalism can model them successfully and they are able to control many other different shades of meaning by applying only a minimal number of syntactic tools. The most profitable and beneficial AI application of the presented natural language syntax consistent knowledge representation technique is automated knowledge acquisition: computer-aided textual data base generation and logical inference based information retrieval. CTRL/iCTRL applicability is demonstrated by various illustrative examples including a transparent graphical interpretation analogous to Frege's graph language that help clarify new concepts and exemplify partial inappropriateness of traditional logical language. The CTRL/iCTRL paradigm is based on a novel and interesting synthesis of the two traditional logic schools, the Stoic and the Peripatetic school, refuting a century long scientific prejudice against the latter stated to be completely outworn. An interesting issue of this analysis points out that expressing subordination unconsciously and simply by co-ordination causes a typical restriction of meaning in classical logic.

Automatic acquisition of biographic knowledge from encyclopedic texts

Automatically acquiring knowledge from encyclopedic texts, specifically the biographies of famous people found in the World Book Encyclopedia, begins with an electronic version of the text of a biography and ends with knowledge structures representing the knowledge that has been acquired. This acquisition process is performed without human assistance of any kind and thus involves not only issues in the area of knowledge acquisition, but also issues in natural language understanding and knowledge representation. We describe one problem from each of these two areas: interpretation of deverbal nominalizations and representation of the a priori knowledge required by the semantic interpreter. The results of two comprehensive experiments are presented in this article, which show that these problems can be solved on the way to near human performance in answering a large set of questions.

An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

This paper presents some highlights in the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition. These techniques are capable of dealing with inexact and imprecise problem domains and have been demonstrated to be useful in the solution of classification problems. It addresses the issue of the application of appropriate evaluation criteria such as rule base accuracy and comprehensibility for new knowledge acquisition techniques. An empirical study is then described in which three approaches to knowledge acquisition are investigated. The first approach combines neural networks and fuzzy logic, the second, genetic algorithms and fuzzy logic, and in the third a rough sets approach has been examined, and compared. In this study neural network and genetic algorithm fuzzy rule induction systems have been developed and applied to three classification problems. Rule induction software based on rough sets theory was also used to generate and test rule bases for the same data. A comparison of these approaches with the C4.5 inductive algorithm was also carried out. Our research to date indicates that, based on the evaluation criteria used, the genetic/fuzzy approach compares more than favourably with the neuro/fuzzy and rough set approaches. On the data sets used the genetic algorithm system displays a higher accuracy of classification and rule base comprehensibility than the C4.5 inductive algorithm.

A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques.

To implement practice guideline entry tools in a reminder system in order to provide decision support to health care workers in clinical care and emergency care environments. To design a knowledge acquisition environment that enables physicians to formulate, update, and verify guidelines without the assistance of a knowledge engineer. We developed a knowledge acquisition environment for the Intensive Care Unit (ICU) consisting of 1) a graphical knowledge acquisition tool, 2) tools that perform logical and semantic tests on proposed guidelines, 3) a Patient Data Management System (PDMS) containing clinical patient data, and 4) an expert system that reminds ICU health care workers of inconsistencies between a treatment plan and implemented guidelines. Physicians enter the guidelines using the knowledge acquisition tool, after which consistency and correctness tests are performed on the guidelines. The guidelines are then transferred to the knowledge base of the reminder system and validated by applying the new guidelines to a large stored data set of previous patients. If the new guidelines are approved, they are exported to the reminder system that is used in daily practice. ICU physicians used the knowledge acquisition tool to enter 58 guidelines into the reminder system's knowledge base. These guidelines were tested on a data set consisting of 803 previously admitted patients. As a result, 27 guidelines fired at least once, generating 406 reminders in total. Of the 406 generated reminders, 356 (88%) were issued correctly and 50 (12%) were false alarms. The reminders that were issued correctly involved 3 situations: 1) the database contained inconsistent or incomplete information, 2) the actions or decisions of the health care workers were not the most appropriate ones, and 3) there was a potential risk involved. All false alarms were caused by the fact that the corresponding guidelines were not specific enough to handle certain exceptions. As a result of this analysis, the guidelines could be improved in such a way as to eliminate all false alarms. These first results demonstrate that this bottom-up knowledge acquisition strategy, implemented by the automated knowledge acquisition tools, enables medical specialists to improve the quality of computer support in an ICU without assistance of a knowledge engineer.

Evolutionary neural fuzzy systems for noise cancellation in image data

A new class of neural fuzzy filters for removing noise from two-dimensional (2-D) measurement data is presented. The proposed approach combines the advantages of the fuzzy and neural paradigms. The network structure is, in fact, specifically designed to exploit the effectiveness of fuzzy reasoning in removing noise without destroying the useful information embedded in the input data. An easy design of new filters is thus obtained because the neuro-fuzzy approach is capable of automatic acquisition of knowledge for a given network structure. The learning method based on genetic algorithms performs an effective training of the network yielding satisfactory results after a few generations. Experimental results show that the proposed approach is very effective also in the presence of data highly corrupted by noise. The neural fuzzy system is largely able to outperform other methods in the literature including state-of-the-art techniques.

Automated extraction of medical expert system rules from clinical databases based on rough set theory

Automated knowledge acquisition is an important research issue to solve the bottleneck problem in developing expert systems. Although many inductive learning methods have been proposed for this purpose, most of the approaches focus onlu on inducing classification rules. However, medical experts also learn other information important for diagnosis from clinical cases. In this paper, a rule induction method is introduced, which extracts not only classification rules but also other medical knowledge needed for diagnosis. This system was evaluated on three clinical databases, whose experimental results show that our proposed method correctly induces diagnostic rules and estimates the statistical measures of rules.

Objective evaluation and prediction of properties of a fused panel

The paper presents the objective evaluation and prediction of properties of a fused panel. A system for automatic knowledge acquisition from a given set of examples presents an alternative way to build a knowledge base to determine the quality of a fused panel. On the basis of analyses of influential factors of fused panel quality the learning and testing set of examples to predict the bonding strength of fused panel has been formed.

A global optimality criterion for evolutionary computation

Focussing on unification of concrete portions into a generic form of computational evolution, a generalized theoretical framework is necessary and imperative to be built to construct a universal computational theory of evolution machine. The NP problem solving capacity can be traced to the nature of meta-evolution mechanism with emergence features that determine corresponding homeostasis and diversity ranging in the domain of nonlinnear mapping from genotype to phenotype. In this paper a criterion that guarantees the global optimality of evolutionary computation process is proposed and proven rigorously. The global optimization criterion obtained is based on the non-parametric measarement for the whole evolution system and has great flexibility and evolvability. It leaves room for evolutionary system designing and developement. The formulization of the global description in statistical manifold space of information object family expresses evoluable evolutionary operator architecture and operation procedure in terms of evolution by evolution. The theoretical results are helpful to applications such as machine learning for automatic knowledge acquisition, pattern classification and recognition of complex images (e. q. OCR) and unsupervised system identification of nonlinear dynamical systems as well as chaos phenomena. The kernal of the formal system guided by global evolutionary optimization is proper to the implementation with object-oriented programming paradigm and abstract machine modelling.

From a time standard for medical informatics to a controlled language for health

CEN ENV 12381 is a European Prestandard focusing on formal representation and explicit reference of temporal information in healthcare informatics and telematics. One of its merits is not just the possibility to represent natural language expressions containing time-related information in a structured way, but also to give some mechanisms on how clinical language itself can be used to convey meaning unambiguously. As such, CEN ENV 12381 introduces the notion of `controlled language use' in the domain of healthcare. In this paper the principles behind controlled language design and use are explained. Through a detailed study of the inconsistencies and ambiguities that arise when interpreting Snomed procedure terms in the framework of the Galen-In-Use project, it is shown that most of them can be explained as a violation of sound term-formation principles. A proposal is made to develop a controlled language for health and to use it in subsequent versions of coding and classification systems. It is expected that such an endeavour will lead to a more effective application of linguistic engineering in areas such as automatic knowledge acquisition, automatic translation, and terminology validation in the domain of healthcare informatics.

On the Application of Explanation-Based Learning to Acquire Control Knowledge for Branch and Bound Algorithms

The goal of this article is to present a methodology for the automatic acquisition of new control knowledge in the form of dominance and equivalence conditions, using an explanation-based learning algorithm from artificial intelligence. The acquisition of new control knowledge proceeds through the following stages: 1) Analysis of the problem-solving activity, generated from the application of a branch and bound algorithm on specific instance(s) of a class of problems. 2) Interpretation of the problem-solving activity and synthesis of new control knowledge, which can lead to more efficient solution of future problems. The generated control knowledge is provably correct within the theory of the given class of problems and the employed branch and bound strategy. Consequently, the number of nodes evaluated by a specific branch and bound algorithm is guaranteed to be reduced, as new control knowledge is continuously acquired from the solution of specific problems. The article presents the theoretical foundations for the handling of the above two tasks. It concludes with an application of the proposed approach to a mixed integer linear programming formulation of a batch scheduling problem.