Mixed transfer function neural networks for generalization and knowledge extraction

This thesis develops a novel framework of nonlinear modelling to adaptively fit the complexity of the model to the problem domain resulting in a better modelling capability and a straightforward knowledge acquisition. The developed framework also permits increased comprehensibility and user acceptability of modelling results.

Dynamic knowledge modelling

The integration of knowledge from various modelling paradigms has posed great difficulty to knowledge engineers due to the inability to re-use knowledge from diverse domains. This research has identified novel processes, which collectively provide the basis for dynamic knowledge modelling, as the key step in knowledge integration. These processes required prototypical knowledge structures derived from abstractions of reasoning processes and have been shown to provide a knowledge modelling solution to complex knowledge management tasks.

Social intelligence and top management team: an exploratory study of external knowledge acquisition for strategic change in global IT service providers in India

This paper focuses on the processes by which firms, particularly knowledge intensive firms, can augment their overall knowledge stock by tapping into external sources of knowledge. It is argued that Top Management Teams' (TMTs') social intelligence is a critical learning capability in acquiring external knowledge that leads to strategic change. Social intelligence involves social awareness, social understanding and social skills. The study draws from the experience of 11 of the largest Information Technology Service Providers (ITSPs) in India and based on in-depth interviews. The findings show that TMTs' learning capability in the context of social intelligence to interact with external stakeholders is important to ITSPs in facilitating external knowledge acquisition and allowing new knowledge emerge within and across networks. The findings provide significant insights into ITSPs emerging in other developing countries such as in China. Research limitation and future research direction are also provided.

Extensions to knowledge acquisition and effect of multimodal representation in unsupervised learning

The phenomenal behaviour and composition of human cognition is yet to be defined comprehensibly. Developing the same, artificially, is a foremost research area in artificial intelligence and related fields. In this chapter we look at advances made in the unsupervised learning paradigm (self organising methods) and its potential in realising artificial cognitive machines. The first section delineates intricacies of the process of learning in humans with an articulate discussion of the function of thought and the function of memory. The self organising method and the biological rationalisations that led to its development are explored in the second section. The next focus is the effect of structure restrictions on unsupervised learning and the enhancements resulting from a structure adapting learning algorithm. Generation of a hierarchy of knowledge using this algorithm will also be discussed. Section four looks at new means of knowledge acquisition through this adaptive unsupervised learning algorithm while the fifth examines the contribution of multimodal representation of inputs to unsupervised learning. The chapter concludes with a summary of the extensions outlined.

Expert systems modeling for assessing climate change impacts and adaptation in agricultural systems at regional level

Australian agriculture is very susceptible to the adverse impacts of climate change, with major shifts in temperature and rainfall projected. In this context, this paper describes a research methodology for assessing potential climate change impacts on, and formulating adaptation options for, agriculture at regional level. The methodology was developed and applied in the analysis of climate change impacts on key horticultural commodities—pome fruits (apples and pears), stone fruits (peaches and nectarines) and wine grapes—in the Goulburn Broken catchment management region, State of Victoria, Australia. Core components of the methodology are mathematical models that enable to spatially represent the degree of biophysical land suitability for the growth of agricultural commodities in the region of interest given current and future climatic conditions. The methodology provides a sound analytic approach to 1) recognise regions under threat of declines in agricultural production due to unfolding climatic changes; 2) identify alternative agricultural systems better adapted to likely future climatic conditions and 3) investigate incremental and transformational adaptation actions to improve the problem situations that are being created by climate change.

From knowledge acquisition to knowledge production: Issues with Australian honours curricula

Although there have been increasing attempts to involve undergraduate students in conducting research, a pivotal moment when students engage in knowledge production is during honours programmes. Honours programmes, particularly those in Australia, seek to develop students' capacity to engage in higher order thinking that may lead to knowledge production. This transition is facilitated through advanced disciplinary knowledge, research training and a research project. However, there is a pedagogical tension between requiring students to engage in this deeper level of inquiry at the same time as they complete a heavy knowledge acquisition load. This paper explores how a number of disciplines in Australia balance these elements of the honours curricula. It argues that the combination of these curriculum goals can make it difficult for students to apply the knowledge they have gained in advanced disciplinary and research training courses to their research project work. This has serious implications for honours programmes. © 2012 Copyright Taylor and Francis Group, LLC.

Investigating information systems analysts' possession of tacit organisational knowledge

Outsourcing of Information Technology (IT) services which are central to business strategy may be risky. Managers have made the outsourcing decision both to concentrate financially on the core competencies and to rid themselves of a troublesome and cost inefficient department. More recent research has, however, cast doubt on the promises of huge savings. In this paper, we consider the likelihood that outsourcing may lead to the loss of organisational knowledge - that organisations outsourcing their total Information Systems operations may also have lost irreplaceable tacit, cross-functional knowledge which subsisted within the minds of the professional systems analysts. The findings of our research revealed that expert systems analysts possess a unique organisational understanding and draw on this knowledge to operate efficiently in their environment. We present a model that will allow future researchers to build on our findings and examine whether outsourcing can lead to a loss of organisational memory.

Building agent-based hybrid intelligent systems

Many complex problems including financial investment planning, foreign exchange trading, knowledge discovery from large/multiple databases require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. In this paper, it is argued that agent technology is well snited for constructing hybrid intelligent systems (especially loosely coupled hybrid intelligent systems) through a successful case study. A great number of heterogeneous computing techniques/packages are easily integlated into the experimental system under a unifying agent framework, which implies that agent technology can greatly facilitate the construction of hybrid intelligent systems.

Sustainable knowledge management systems: integration, personalisation and contextualisation

Many knowledge management (KM) systems have proven unsustainable to date, exhibiting low quantities and quality of knowledge, with systems falling into disuse. In this paper, we provide and explore a model for sustainable KM systems, focusing on the advantages to be gained from integrating knowledge work with everyday work practices, and enabling sensemaking through personalisation and contextualisation. We employ a discourse analysis of email as an exemplar of a sustainable KM system, thereby identifying a number of key characteristics for sustainable KM systems. Our model for sustainable KM systems adds to existing KM theory and, more immediately, assists companies by providing an understanding
of the kinds of characteristics likely to make KM systems more effective, and sustainable in the long term.

Instructional strategies integrating cognitive style construct : a meta-knowledge processing model

The overarching goal of this dissertation was to evaluate the contextual components of instructional strategies for the acquisition of complex programming concepts. A meta-knowledge processing model is proposed, on the basis of the research findings, thereby facilitating the selection of media treatment for electronic courseware. When implemented, this model extends the work of Smith (1998), as a front-end methodology, for his glass-box interpreter called Bradman, for teaching novice programmers. Technology now provides the means to produce individualized instructional packages with relative ease. Multimedia and Web courseware development accentuate a highly graphical (or visual) approach to instructional formats. Typically, little consideration is given to the effectiveness of screen-based visual stimuli, and curiously, students are expected to be visually literate, despite the complexity of human-computer interaction. Visual literacy is much harder for some people to acquire than for others! (see Chapter Four: Conditions-of-the-Learner) An innovative research programme was devised to investigate the interactive effect of instructional strategies, enhanced with text-plus-textual metaphors or text-plus-graphical metaphors, and cognitive style, on the acquisition of a special category of abstract (process) programming concept. This type of concept was chosen to focus on the role of analogic knowledge involved in computer programming. The results are discussed within the context of the internal/external exchange process, drawing on Ritchey's (1980) concepts of within-item and between-item encoding elaborations. The methodology developed for the doctoral project integrates earlier research knowledge in a novel, interdisciplinary, conceptual framework, including: from instructional science in the USA, for the concept learning models; British cognitive psychology and human memory research, for defining the cognitive style construct; and Australian educational research, to provide the measurement tools for instructional outcomes. The experimental design consisted of a screening test to determine cognitive style, a pretest to determine prior domain knowledge in abstract programming knowledge elements, the instruction period, and a post-test to measure improved performance. This research design provides a three-level discovery process to articulate: 1) the fusion of strategic knowledge required by the novice learner for dealing with contexts within instructional strategies 2) acquisition of knowledge using measurable instructional outcome and learner characteristics 3) knowledge of the innate environmental factors which influence the instructional outcomes This research has successfully identified the interactive effect of instructional strategy, within an individual's cognitive style construct, in their acquisition of complex programming concepts. However, the significance of the three-level discovery process lies in the scope of the methodology to inform the design of a meta-knowledge processing model for instructional science. Firstly, the British cognitive style testing procedure, is a low cost, user friendly, computer application that effectively measures an individual's position on the two cognitive style continua (Riding & Cheema,1991). Secondly, the QUEST Interactive Test Analysis System (Izard,1995), allows for a probabilistic determination of an individual's knowledge level, relative to other participants, and relative to test-item difficulties. Test-items can be related to skill levels, and consequently, can be used by instructional scientists to measure knowledge acquisition. Finally, an Effect Size Analysis (Cohen,1977) allows for a direct comparison between treatment groups, giving a statistical measurement of how large an effect the independent variables have on the dependent outcomes. Combined with QUEST's hierarchical positioning of participants, this tool can assist in identifying preferred learning conditions for the evaluation of treatment groups. By combining these three assessment analysis tools into instructional research, a computerized learning shell, customised for individuals' cognitive constructs can be created (McKay & Garner,1999). While this approach has widespread application, individual researchers/trainers would nonetheless, need to validate with an extensive pilot study programme (McKay,1999a; McKay,1999b), the interactive effects within their specific learning domain. Furthermore, the instructional material does not need to be limited to a textual/graphical comparison, but could be applied to any two or more instructional treatments of any kind. For instance: a structured versus exploratory strategy. The possibilities and combinations are believed to be endless, provided the focus is maintained on linking of the front-end identification of cognitive style with an improved performance outcome. My in-depth analysis provides a better understanding of the interactive effects of the cognitive style construct and instructional format on the acquisition of abstract concepts, involving spatial relations and logical reasoning. In providing the basis for a meta-knowledge processing model, this research is expected to be of interest to educators, cognitive psychologists, communications engineers and computer scientists specialising in computer-human interactions.

Student construction of a knowledge-based system as an actor network

The study contributes to the educational computing discourse in two ways. It extends our understandings of the way students use and understand the building of small knowledge-based systems, and provides a novel and holistic way of investigating the use of information technology in classrooms.