Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Techniques and potential capabilities of multi-resolutional information (knowledge) processing

A concept of nested hierarchical (multi-resolutional, pyramidal) information (knowledge) processing is introduced for a variety of systems including data and/or knowledge bases, vision, control, and manufacturing systems, industrial automated robots, and (self-programmed) autonomous intelligent machines. A set of practical recommendations is presented using a case study of a multiresolutional object representation. It is demonstrated here that any intelligent module transforms (sometimes, irreversibly) the knowledge it deals with, and this tranformation affects the subsequent computation processes, e.g., those of decision and control. Several types of knowledge transformation are reviewed. Definite conditions are analyzed, satisfaction of which is required for organization and processing of redundant information (knowledge) in the multi-resolutional systems. Providing a definite degree of redundancy is one of these conditions

Product development knowledge-processing system

This thesis develops a methodology that integrates Quality Function Deployment (QFD), Theory of Inventive Problem Solving (TRIZ) and Design Structure Matrix (DSM) to address the problem of analyzing, interpreting, and transforming the knowledge data flow during the product development process. A product development process is the sequence of steps an enterprise takes to conceive, design, and commercialize a product. The general process includes: Planning, Concept Development, System-Level Design, Detail Design, Testing and Refinement, and Production Ramp-Up. One way to think about the development process is as a knowledge-processing system. Prior to this study, there are few articles integrating TRIZ and QFD. QFD is a tool for identifying the needs of the customer and translating the language of the customer into the language of the engineer . The QFD process also identifies engineering contradictions within the existing system. TRIZ can provide solutions for these contradictions. It offers one of the most efficient means to generate creative solutions for the desired improvements prioritized in QFD. Once a creative solution is developed using TRIZ, the corresponding part characteristics can be further decomposed using QFD. If we think of knowledge-processing as a work process, then both rework and iterative design requires additional project resources and time. Iteration is the repetition of activities due to the input of new information. DSM is an effective tool to plan and manage product development processes through information flow analysis by helping managers understand and mitigate unintentional rework or iteration

Knowledge processing for structural design.

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Knowledge processing for classical Chinese text

Chinese characters have a features of morpheme, each character represents phonetic values and grammatical and/or semantical values by its shape. Shape can be regarded as spelling in alphabetical scripts. These characteristics of Chinese character mean that character level phenomena and higher level phenomena are observed as mixed behavior of character and we often don't distinguish them. In addition, shape (graphical information) and phonetic value also have such kind of structures. Each modal value has physical level (e. g. written shape, spoken pronunciation) and abstract level (e. g. abstract glyph, phoneme), and we can find various intermediate abstract/concrete levels, so we can draw a gradation of various levels. Each modal value has a gradation of abstract/concrete objects, so each character has a multidimensional gradation. In the present text processing mechanism for classical Chinese, however, characters are basically separated by abstract character layer and glyph (and glyph image) layer(s), and character level and higher level are separated by isolated layers, so they are not seamless. In addition, grammatical and semantical processing are very poor. Some difficulties of classical Chinese processing may be caused by the framework of text processing. Anyway, the current situation requires a lot of human resources to develop complicated markup texts and/or knowledge resources for semantical fields and maintain increasing data. In addition, domain specific data may not produce other kinds of data. Tools chains are very poor or not available. It may be a serious problem of the progress of classical Chinese informatics. This paper describes a feasible knowledge processing methods for classical Chinese text to resolve such kind of situations. It mainly focus morphological analyzer, glyph corpora and character ontology, then we discuss about multimodal knowledge processing mechanism based on their integration

Intelligence metasynthesis and knowledge processing in intelligent systems

Intelligence and Knowledge play more and more important roles in building complex intelligent systems, for instance, intrusion detection systems, and operational analysis systems. Knowledge processing in complex intelligent systems faces new challenges from the increased number of applications and environment, such as the requirements of representing domain and human knowledge in intelligent systems, and discovering actionable knowledge on a large scale in distributed web applications. In this paper, we discuss the main challenges of, and promising approaches to, intelligence metasynthesis and knowledge processing in open complex intelligent systems. We believe (1) ubiquitous intelligence, including data intelligence, domain intelligence, human intelligence, network intelligence and social intelligence, is necessary for OCIS, which needs to be meta-synthesized; and (2) knowledge processing should pay more attention to developing innovative and workable methodologies, techniques, tools and systems for representing, modelling, transforming, discovering and servicing the uncertain, large-scale, deep, distributed, domain-oriented, human-involved, and actionable knowledge highly expected in constructing open complex intelligent systems. To this end, the meta-synthesis of ubiquitous intelligence is an appropriate way in designing complex intelligent systems. To support intelligence meta-synthesis, m-interaction can play as the working mechanism to form rn-spaces as problem-solving systems. In building such m-spaces, advancement in knowledge processing is necessary. © J.UCS

Cooperative knowledge processing: the key technology for future organizations

Drawing from the challenges organizations are faced with today, there is a growing understanding that future market success, and long-term survival of enterprises will increasingly be related to the effectiveness of information technology utilization. This, however, requires to intertwine much more seriously organizational theory and research in information processing as it has been done before. Within this paper, we approached this aim from the perspective of radically decentralized, computerized enterprises. We further assume that organizations are increasingly processoriented, rather than applying to structuring organizations based on task decomposition and assignment. This scenario reveals that, due to the inherent autonomy of organizational units, the coordination of decentralized organizational activities (workflows, processes) necessitates a cooperative style of problem solving. On this basis, the paper introduces into the research area of cooperative knowledge processing, with a particular focus on multi-agent decision support systems, and human computer cooperative work. Finally, several important organizational applications of cooperative knowledge processing are presented that demonstrate how future enterprises can take great advantage from these new technologies.<br