Serendipity is a popular word that captures a rich phenomenon with potentially far-reaching implications from a personal to global level. Serendipity is associated with revelations, discoveries, life events, and innovations, both big and small, and the lack of consensus on its definition reflects this breadth of meaning. Serendipity is defined in this research as an unexpected experience prompted by an individual’s valuable interaction with ideas, information, objects, or phenomena. While efforts are being made to facilitate serendipity in digital environments (e.g., websites, databases, search engines), we know very little about the complex interaction between the individual and the environment and what actually facilitates serendipity. In three phases, this thesis investigated how individual differences and environmental factors influence work-related serendipity and how serendipity may be facilitated in digital environments. Phase 1 explored serendipity through semi-structured interviews with 12 professionals and scholars. Based on findings from Phase 1, in Phase 2 a serendipitous digital environment scale to measure how well a digital environment supports serendipity was developed, assessed, and honed though an expert review by eight researchers and a web-based survey of 107 university students. Phase 3 employed a web-based survey of 289 professionals and scholars. Through exploratory factor analysis, the serendipitous digital environment scale was refined and assessed. Using multivariate analyses, relationships were explored between serendipity, the underlying factors of the serendipitous digital environment scale, type of digital environment, creative environment perceptions, locus of control, extraversion, and openness to experience. My research found that the type of digital environment influences the frequency of serendipity, which in turn shares a relationship with three factors of the serendipitous digital environment scale – enables connections, trigger-rich, and leads to the unexpected. Furthermore, results indicate that individuals’ level of extraversion influences perceptions of serendipity in general. This research contributes to our knowledge of information seeking and use through findings that confirm and augment previous models of serendipity through the identification of what influences serendipity. This research also underscores the potential to design for serendipity in digital environments and provides a tool for developers to assess the serendipitous nature of their systems.

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Two observations underpin this thesis; 1. There is a need for automated pattem-recognition techniques that allow processes requiring skills normally associated with the human brain to be carried out rapidly, reliably and cheaply, and; 2. The current methods applied to solving artificial intelligence (AI) problems are insufficient to the task of creating generalised systems capable of pattem-recognition and environmental interaction. Neural networks (NNs) are a good method of solving AI problems that are difficult or impossible to solve using knowledge-based or symbolic techniques. NNs provide the flexibility to analyse poorly-defined systems or systems that are general in nature, and also provide the ability to learn from noisy, complex data sets. The main problem with the use of NNs to date has been that one NN's structure and dynamics may work for a specific problem, but if this problem is changed slightly then it is difficult to determine the optimal settings for the network to enable it to adapt to the new situation. The use of evolutionary methods is emphasised throughout this thesis as a way of optimising NN system performance. Several methods have been developed through the course of this thesis that improve the performance of NN models. One of the most important is the use of a biologically plausible node and connection modification algorithm. In this method, local effects such as the activation levels of nodes at either end of a connection or a node's past activation history are the only input parameters which network components use for their adjustment. Included in the biological plausibility argument are NN structuring methods that mimic specific areas of the brain. One example is the visual system, in which a pyramidal structure is applied that permits a hierarchical pattern recognition process to develop. This process builds the image recognition up from small 'substructures' in successive layers, allowing the system to recognise objects that are not specifically defined by the user. Arguments are made that an AI systems's utility is limited if it does not have the capability of interacting with its environment. A system that merely observes without attempting to alter or exist within an environment is only half of the story. From a biological standpoint, intelligence is the result of successive generations of organisms interacting with and altering their environment. Limiting an AI system's ability to interact with the environment can only place restrictions on the capabilities of that system, not improve them. Following development of a suite of applicable pattem-recognition techniques, work is carried out in order to implement these methods within a simple environment. For the moment, a virtual 'block world' is used that is relatively easy and cheap to manipulate. The importance of both modularity and sensory feedback to the ability to develop complex behaviours is investigated, with these two concepts included in the overall evolutionary strategy of system development. The results obtained show that the techniques developed provide a pattem- recognition and learning system that is capable of being applied to general problems and that learns without human intervention. In comparison to classical NN techniques the systems developed show superior learning abilities and can be applied in less specific situations. The use of modularity and sensory feedback in the animat simulations has allowed the development of behavioural patterns that are difficult to achieve using homogeneous, input-output systems. Evolutionary methods have allowed system optimisation in a way that is impossible to achieve through trial and error, and which also permit the system to be easily fine-tuned towards specific problems and situations. With current advances in computer speed and memory capacity, it is now possible to implement NNs comparable in size to the nervous systems of small animals. The methods used here provide the potential to provide these NNs with the sophistication displayed by their organic counterparts.

This research involved the design, development and implementation of an online survey instrument to identify the physical learning environments and resources students use when studying an online course. It was found, through a review of the literature, that there was no appropriate instrument available for this purpose. It was also found that the term physical learning environment actually is not well defined in the literature. These two factors have been addressed in this research. The results obtained from the survey found that students used a mixture of physical learning resources such as textbooks, and online resources such as email and online submission of assessment items. However, none of these resources were used all the time. It was also established that the majority of students preferred to learn at home using either online or paper-based resource material. The results also showed that the library both as a resource and as a learning environment was not being used to its full potential.

This report discusses the design and operation of dynamically-deployed robotic systems for use in confined, hazardous environments, such as those found in Department of Energy gloveboxes to handle nuclear material while keeping humans at a safe distance. The Department of Energy faces unique technical and operational challenges to automate glovebox operations. These operations share characteristics such as confined spaces, extremely harsh environmental conditions, simplified field serviceability, and portability. Human-scale uncertainty must be tolerated since many glovebox tasks require manipulation of objects whose positions are not predefined and vary in unpredictable ways due to external factors including humans in the loop, interactions with preexisting systems, and completing experimental (as opposed to manufacturing tasks) where the final state of handled objects may not be known. Completion of automated tasks is much more difficult without any a priori knowledge of the item to be handled. This effort will examine both the software and hardware requirements and technical challenges associated with this domain. The examined hardware testbeds include two seven degree-of-freedom glovebox manipulators (5 kg payload each) in a dual-arm configuration deployed via gloveports as well as a similar but larger (10 kg payload) manipulator deployed via a transfer port. Several critical operational capabilities are demonstrated, including deployment, collision detection, manipulation, trajectory generation, tele-manipulation, and calibration. Implementing automation within the confines of a glovebox is far from trivial. The unique environmental and system requirements include confined operating spaces, pre-existing, fixed environments, difficulties when performing complex maintenance and repair, and unconventional workspace envelopes. Many glovebox processes are still experimental, so flexible robotic systems are necessary to test and perfect process methodologies while keeping humans at a safe distance. The need for a gloveport-deployed robotic system that can be easily inserted and removed from an existing glovebox stems from these set of challenges. Port-deployed systems allow the operators to move away from hazards while allowing them to return when (or if) necessary. Ultimately, port-deployed manipulators provide a flexible and reversible approach for increasing the use of automation in glovebox environments, without significant redesign of existing processes or the environment where they occur. / text