Cognitive and environmental factors influencing the process of spatial knowledge acquisition within virtual reality environments

As the fidelity of virtual environments (VE) continues to increase, the possibility of using them as training platforms is becoming increasingly realistic for a variety of application domains, including military and emergency personnel training. In the past, there was much debate on whether the acquisition and subsequent transfer of spatial knowledge from VEs to the real world is possible, or whether the differences in medium during training would essentially be an obstacle to truly learning geometric space. In this paper, the authors present various cognitive and environmental factors that not only contribute to this process, but also interact with each other to a certain degree, leading to a variable exposure time requirement in order for the process of spatial knowledge acquisition (SKA) to occur. The cognitive factors that the authors discuss include a variety of individual user differences such as: knowledge and experience; cognitive gender differences; aptitude and spatial orientation skill; and finally, cognitive styles. Environmental factors discussed include: Size, Spatial layout complexity and landmark distribution. It may seem obvious that since every individual's brain is unique - not only through experience, but also through genetic predisposition that a one size fits all approach to training would be illogical. Furthermore, considering that various cognitive differences may further emerge when a certain stimulus is present (e.g. complex environmental space), it would make even more sense to understand how these factors can impact spatial memory, and to try to adapt the training session by providing visual/auditory cues as well as by changing the exposure time requirements for each individual. The impact of this research domain is important to VE training in general, however within service and military domains, guaranteeing appropriate spatial training is critical in order to ensure that disorientation does not occur in a life or death scenario.

New Improvements in Automatic Structure Elucidation Using the LSD (Logic for Structure Determination) and the SISTEMAT Expert Systems

This article describes the integration of the LSD (Logic for Structure Determination) and SISTEMAT expert systems that were both designed for the computer-assisted structure elucidation of small organic molecules. A first step has been achieved towards the linking of the SISTEMAT database with the LSD structure generator. The skeletal descriptions found by the SISTEMAT programs are now easily transferred to LSD as substructural constraints. Examples of the synergy between these expert systems are given for recently reported natural products.

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.

An extended query-the-user tool for expert systems

An extended version of HIER, a query-the-user facility for expert systems is presented. HIER was developed to run over Prolog programs, and has been incorporated to systems that support the design of large and complex applications. The framework of the extended version is described,; as well as the major features of the implementation. An example is included to illustrate the use of the tool, involving the design of a specific database application.

Evaluation of an educational handbook as a knowledge-acquisition strategy for mastectomized women

This descriptive, cross-sectional and quantitative study presents an analysis of knowledge acquired by mastectomized women concerning breast cancer after reading an educational handbook. The sample was composed of 125 women. Data were collected in a specialized cancer facility in three phases: preparatory, operational I and operational II. As to the knowledge acquired, the posttest showed an 11% increase in the number of correct answers compared to the pretest. The most frequent correct answer regarded a question asking the name of the surgery (97.60%) while the question concerning breast reconstruction obtained the lowest number of correct answers (58.40%). Answers to all the questions significantly improved in the posttest, with the exception of a question addressing breast reconstruction (p=0.754). The assessment of knowledge showed positive results after reading, suggesting that cognition is essential to understanding and adhering to guidance, thus the handbook is a favorable resource to be used in the rehabilitation of mastectomized women.

KATS: A Knowledge Acquisition Tool Based on Electronic Document Processing

This paper describes a particular knowledge acquisition tool for the construction and maintenance of the knowledge model of an intelligent system for emergency management in the field of hydrology. This tool has been developed following an innovative approach directed to end-users non familiarized in computer oriented terminology. According to this approach, the tool is conceived as a document processor specialized in a particular domain (hydrology) in such a way that the whole knowledge model is viewed by the user as an electronic document. The paper first describes the characteristics of the knowledge model of the intelligent system and summarizes the problems that we found during the development and maintenance of such type of model. Then, the paper describes the KATS tool, a software application that we have designed to help in this task to be used by users who are not experts in computer programming. Finally, the paper shows a comparison between KATS and other approaches for knowledge acquisition.

Knowledge acquisition through conceptual clustering : a theoretical framework and an algorithm for partitioning data into conjunctive concepts /

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