An Expert System Shell Performing the Generic Task of Hierarchical Classification

Any expert system shell that performs with the generic task of hierarchical classificiation must deal explicitly with the issues of knowledge representations, control strategies, inductive learning, and ways of handling uncertainty, ambiguity, and contradictions. This resesarch is mainly concerned about the creation of the expert system shell HICLASS. Aspects crucial to this task are challenged from btoh a theoretical and an implementational point of view. The principles of generic tasks and hierarchical classification are described. Important concepts of HICLASS are introducted, followed by a detailed description of the knowledge representation and local control strategies developed for the system, including a discussion of special problems and respective solutions. IT is described how HICLASS handles uncertainty. Important issues like concluding values, explanation, learning, incorporating metaknowledge, and the global control strategy of HICLASS are discussed. Then, the actual implementation of the table editor HIEDIT as well as HICLASS is described in detail. It is show that HICLASS is a genuine tool for the generic task for hierarchical classification. The system is compared to two well-known tools for hierarchical classification. Using the ideas raised for HICLASS, the development of a hierarchical hypothesis matcher, HIHYPO, is proposed. Essential features of HIHYPO are introducted. A theoretic overview about algorithms for inductive learning is followed by the description of an inductive learning algorithm developed for HIHYPO. Appendix B provides an overview about software engineering methods, and a discussioin about methods actually used to create the HICLASS package. In Appendix C, the definitions of all modules developed for the package are shown

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues

Mapping knowledge management and organizational learning in support of organizational memory

The normative literature within the field of Knowledge Management has concentrated on techniques and methodologies for allowing knowledge to be codified and made available to individuals and groups within organizations. The literature on Organizational Learning however, has tended to focus on aspects of knowledge that are pertinent at the macro-organizational level (i.e. the overall business). The authors attempt in this paper to address a relative void in the literature, aiming to demonstrate the inter-locking factors within an enterprise information system that relate knowledge management and organizational learning, via a model that highlights key factors within such an inter-relationship. This is achieved by extrapolating data from a manufacturing organization using a case study, with these data then modeled using a cognitive mapping technique (Fuzzy Cognitive Mapping, FCM). The empirical enquiry explores an interpretivist view of knowledge, within an Information Systems Evaluation (ISE) process, through the associated classification of structural, interpretive and evaluative knowledge. This is achieved by visualizng inter-relationships within the ISE decision-making approach in the case organization. A number of decision paths within the cognitive map are then identified such that a greater understanding of ISE can be sought. The authors therefore present a model that defines a relationship between Knowledge Management (KM) and Organisational Learning (OL), and highlights factors that can lead a firm to develop itself towards a learning organization

Issues in designing learning by teaching systems

Abstract: Learning by teaching systems are a relatively recent approach to designing Intelligent Learning Environments that place learners in the role of tutors. These systems are based on the practice of peer tutoring where students take on defined roles of tutor and tutee. An architecture for learning by teaching systems is described that does not require the domain model of an Intelligent Tutoring System. However a mutual communication language is needed and is defined by a conceptual syntax that delimits the domain content of the dialogue. An example learning by teaching system is described for the domain of qualitative economics. The construction and testing of this system inform a discussion of the major design issues involved: the nature of the learnt model, the form of the conceptual syntax, the control of the interaction and the possible introduction of domain knowledge. 1

Developing an agent-based framework for intelligent geocoding

Geocoding is essential to translating a physical address such as a house, business or landmark into spatial coordinates which are used in a range of everyday activities. Geocoding is an active area of research, both within the literature and also in industry. Despite progress in the field, there remains a small portion of addresses which are difficult to geocode. The purpose of this research is to explore the use of agent-based techniques to add intelligence to the geocoding process. The importance of the research stems from its potential to move geocoding in a new direction, by complementing current theory and practice with control and knowledge improvements which will improve geocoding results.The investigation was undertaken by identifying the issues relevant to intelligent geocoding, designing an agent-based solution and building a prototype. The prototype was then evaluated using sample addresses to assess its quantitative performance, and its qualitative performance was evaluated based on the new functionality it provided. Results indicate that intelligence in geocoding is a product of both context and semantics (at a conceptual level) and control and knowledge (at an implementation level), where the two are “connected” by the agent paradigm which is both a representation and a solution. Other conclusions include that further development in learning and semantics in geocoding would allow the knowledge base to infer new knowledge and store insights regarding the spatial cognition of users

The use of additional information in problem-oriented learning environments

Self-directed learning with authentic and complex problems (problem-oriented learning) requires that learners observe their own learning and use additional information when it is appropriate – e.g. hypertextual information in computer-supported learning environments. Research results indicate that learners in problem-oriented learning environments often have difficulties using additional information adequately, and that they should be supported. Two studies with a computer-supported problem-oriented learning environment in the domain of medicine analyzed the effects of strategy instruction on the use of additional information and the quality of the problem representation. In study 1, an expert model was used for strategy instruction. Two groups were compared: one group with strategy modeling and one group without. Strategy modeling influenced the frequency of looked-up hypertextual information, but did not influence the quality of learners' problem representations. This could be explained by difficulties in applying the general hypertext information to the problem. In study 2, the additional information was presented in a more contextualized way as graphical representation of the case and its relevant concepts. Again, two groups were compared: one with a strategy instruction text and one without. Strategy instruction texts supported an adequate use of this graphical information by learners and had an effect on the quality of their problem representations. These findings are discussed with respect to the design of additional help systems in problem-oriented learning environments

Metacognition and Reflection by Interdisciplinary Experts: Insights from Cognitive Science and Philosophy

Interdisciplinary understanding requires integration of insights from different perspectives, yet it appears questionable whether disciplinary experts are well prepared for this. Indeed, psychological and cognitive scientific studies suggest that expertise can be disadvantageous because experts are often more biased than non-experts, for example, or fixed on certain approaches, and less flexible in novel situations or situations outside their domain of expertise. An explanation is that experts’ conscious and unconscious cognition and behavior depend upon their learning and acquisition of a set of mental representations or knowledge structures. Compared to beginners in a field, experts have assembled a much larger set of representations that are also more complex, facilitating fast and adequate perception in responding to relevant situations. This article argues how metacognition should be employed in order to mitigate such disadvantages of expertise: By metacognitively monitoring and regulating their own cognitive processes and representations, experts can prepare themselves for interdisciplinary understanding. Interdisciplinary collaboration is further facilitated by team metacognition about the team, tasks, process, goals, and representations developed in the team. Drawing attention to the need for metacognition, the article explains how philosophical reflection on the assumptions involved in different disciplinary perspectives must also be considered in a process complementary to metacognition and not completely overlapping with it. (Disciplinary assumptions are here understood as determining and constraining how the complex mental representations of experts are chunked and structured.) The article concludes with a brief reflection on how the process of Reflective Equilibrium should be added to the processes of metacognition and philosophical reflection in order for experts involved in interdisciplinary collaboration to reach a justifiable and coherent form of interdisciplinary integration. An Appendix of “Prompts or Questions for Metacognition” that can elicit metacognitive knowledge, monitoring, or regulation in individuals or teams is included at the end of the article