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Dissertations / Theses on the topic 'Learning behaviour'
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Binz, Marcel. "Learning Goal-Directed Behaviour." Thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213015.
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Learning behaviour of artificial agents is commonly studied in the framework of Reinforcement Learning. Reinforcement Learning gained increasing popularity in the past years. This is partially due to developments that enabled the possibility to employ complex function approximators, such as deep networks, in combination with the framework. Two of the core challenges in Reinforcement Learning are the correct assignment of credits over long periods of time and dealing with sparse rewards. In this thesis we propose a framework based on the notions of goals to tackle these problems. This work implements several components required to obtain a form of goal-directed behaviour, similar to how it is observed in human reasoning. This includes the representation of a goal space, learning how to set goals and finally how to reach them. The framework itself is build upon the options model, which is a common approach for representing temporally extended actions in Reinforcement Learning. All components of the proposed method can be implemented as deep networks and the complete system can be learned in an end-to-end fashion using standard optimization techniques. We evaluate the approachon a set of continuous control problems of increasing difficulty. We show, that we are able to solve a difficult gathering task, which poses a challenge to state-of-the-art Reinforcement Learning algorithms. The presented approach is furthermore able to scale to complex kinematic agents of the MuJoCo benchmark.Inlärning av beteende för artificiella agenter studeras vanligen inom Reinforcement Learning.Reinforcement Learning har på senare tid fått ökad uppmärksamhet, detta berordelvis på utvecklingen som gjort det möjligt att använda komplexa funktionsapproximerare, såsom djupa nätverk, i kombination med Reinforcement Learning. Två av kärnutmaningarnainom reinforcement learning är credit assignment-problemet över långaperioder samt hantering av glesa belöningar. I denna uppsats föreslår vi ett ramverk baseratpå delvisa mål för att hantera dessa problem. Detta arbete undersöker de komponentersom krävs för att få en form av målinriktat beteende, som liknar det som observerasi mänskligt resonemang. Detta inkluderar representation av en målrymd, inlärningav målsättning, och till sist inlärning av beteende för att nå målen. Ramverket byggerpå options-modellen, som är ett gemensamt tillvägagångssätt för att representera temporaltutsträckta åtgärder inom Reinforcement Learning. Alla komponenter i den föreslagnametoden kan implementeras med djupa nätverk och det kompletta systemet kan tränasend-to-end med hjälp av vanliga optimeringstekniker. Vi utvärderar tillvägagångssättetpå en rad kontinuerliga kontrollproblem med varierande svårighetsgrad. Vi visar att vikan lösa en utmanande samlingsuppgift, som tidigare state-of-the-art algoritmer har uppvisatsvårigheter för att hitta lösningar. Den presenterade metoden kan vidare skalas upptill komplexa kinematiska agenter i MuJoCo-simuleringar.
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Johnson, Neil. "Learning object behaviour models." Thesis, University of Leeds, 1998. http://etheses.whiterose.ac.uk/1281/.
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The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models. The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy. This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences. The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine.
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Dahl, Torbjørn Semb. "Behaviour based learning : evolution inspired development of adaptive robot behaviours." Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251543.
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Wiggs, Luci. "Sleep problems and daytime behaviour in children with severe learning disabilities." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320113.
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Morales, Siles Antonio Jose. "Learning, imitation and economic rationality." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313824.
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Logan, Nicola I. "Parents' attributional, emotional and behavioural reactions towards aggressive behaviour in learning disabled and non-learning disabled children." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/26697.
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Objective: The attributions parents make about the challenging behaviour of their children have been shown to be important determinants of their emotional and behavioural responses to such behaviour. In general, studies have found that if parents judge a child's behaviour to be caused by factors that are internal or controllable, then they will experience more negative emotions and respond using more punitive discipline strategies. To date, no study has directly compared parents' attributional, emotional and behavioural responses to challenging behaviour in learning disabled children with that of non-learning disabled children. In this study, it was hypothesised that parents would have different attributional, emotional and behavioural responses to aggressive challenging behaviour in a learning disabled child in comparison to a non-learning disabled child, on account of the child's learning disability. Design: A questionnaire method was used to analyse within-subjects and betweensubjects differences on measures of attributional, emotional and behavioural responses to vignettes of aggressive challenging behaviour in learning disabled and non-learning disabled children. Method: Fifty-four parents of children with aggressive challenging behaviour (20 with a learning disabled child and 34 with a non-learning disabled child) took part in the study. Participants read two vignettes depicting a learning disabled and a non learning-disabled child with aggressive challenging behaviour. They were then asked to complete questionnaire measures of attributional, emotional and behavioural response in relation to each vignette. Results: In comparison to aggressive behaviour in the non-learning disabled child, participants rated the learning disabled child's aggressive behaviour as being due to more global, more stable and less controllable causes, and reported that they would respond with less negative emotion and less punitive discipline strategies. No group differences (i.e. comparing parents with a learning disabled child and parents with a non-learning disabled child) were found in attributional, emotional or behavioural responses to the two vignettes. The results are discussed with reference to previous research findings and clinical implications. Consideration is also given to the methodological shortcomings of the current study and suggestions for future research are made.
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Trudel, Carol-Ina. "Exploratory learning of an interactive device." Thesis, Cardiff University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287039.
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McKenzie, Ian W. "Educational principles for engineering behaviour learning strategies." Thesis, Heriot-Watt University, 1990. http://hdl.handle.net/10399/1480.
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Allen, David. "Challenging behaviour in people with learning disabilities." Thesis, University of Surrey, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388791.
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Bilakhia, Sanjay. "Machine learning for high-level social behaviour." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/59041.
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The ability to recognize and interpret the complex displays of nonverbal behavioral cues that arise in social interaction comes naturally to humans. Indeed, the survival and flourishing of early groups of homo sapiens may have depended on this ability to share implicit social information. It is a process so innate that complex social behaviours can occur without conscious awareness, even in young babies. Though we would benefit from artificial devices having the ability to understand these nonverbal cues, it has proven an elusive goal. In this thesis we are primarily motivated by the problem of recognizing and exploiting displays of high–level social behavior, focusing on behavioural mimicry. Mimicry describes the tendency of individuals to adopt the postures, gestures and expressions of social interaction partners. We first provide a background to the phenomenon of behavioural mimicry, disambiguate it from other related phenomena in social interaction, and survey its surprisingly complex dependencies on the broader social context. We then discuss a number of methods that could be used to recognize mimicry behaviour in naturalistic interaction. We list some publicly available databases these tools could be trained on for the analysis of spontaneous instances of mimicry. We also examine the scarce prior work on recognition of naturalistic mimicry behaviour, and we discuss the challenges in automatically recognizing mimicry in spontaneous data. Subsequently we present a database of naturalistic social interactions, designed for analysis of spontaneous mimicry behaviour. This has been annotated for mimicry episodes, low-level non-verbal behavioural cues, and continuous affect. We also present a new software package for web-based annotation, AstAn, which has been extensively deployed for temporal event segmentation and continuous annotation. Collecting annotation data for high-level social affect is a difficult problem. This is due to inter-annotator variance, dependent on a variety of factors including i) the content of the data to annotate ii) the complexity of the variables to annotate, and iii) the annotators' cultures and personality traits. AstAn is the first software package to enable large-scale collection of annotations relevant to affective computing, without the costly manual distribution and management of (perhaps sensitive) data. Large-scale and cost-effective data collection can significantly help to overcome the aforementioned difficulties. We present experiments showing that prevailing methods for mimicry recognition on posed data, generalize suboptimally to spontaneous data. These include methods based on cross-correlation and dynamic time warping, which are prevalent in current work on recognition of interpersonal co-ordination, including mimicry and synchrony. We also show that popular temporal models such as recurrent neural networks, when applied in a straightforward classification approach, also find it challenging to discriminate between mimicry and non-mimicry. We expand upon these baseline results using methods adapted from work on multimodal classification. Nonlinear regression models are used to learn the relationships between the non-verbal cues from each subject. Namely, for mimicry and non-mimicry classes, we learn a set of neural networks to forecast the behaviour of each subject, given the behaviour of their counterpart. The set of networks that produces the best behavioural forecast corresponds to the predicted class. Subsequently, we investigate whether high-level social affect like mimicry, conflict, valence and arousal are uniquely displayed between individuals. Specifically, we show that for episodes of a given behavioural display such as mimicry or high-conflict, the spatiotemporal movement characteristics are unique enough to construct a "kinematic template" for that behaviour. Given an unseen episode of the same behavioural display, we can compare it against the template in order to verify identity. This is useful in verification contexts where facial appearance and geometry can change due to lighting, facial hair, facial decoration, or weight loss. We present a new method, Multi-Sequence Robust Canonical Time Warping (M-RCTW), in order to construct this subject- and behaviour-specific template. Unlike prior methods, M-RCTW can warp together multiple multivariate sequences in the presence of large non-Gaussian errors, which can occur due to e.g. tracking artefacts in naturalistic behaviour, such as those resulting from occlusions. We show on two databases of natural interaction that identity verification is possible from a number of high- and low-level behaviours, and that M-RCTW outperforms existing methods for multiple sequence warping on the task of subject verification.
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Comanici, Gheorghe. "Optimal time scales for reinforcement learning behaviour strategies." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=92340.
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Ruiz, Ovejero Adrià. "Weakly-supervised learning for automatic facial behaviour analysis." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457708.
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In this Thesis we focus on Automatic Facial Behavior Analysis, which attempts to develop autonomous systems able to recognize and understand human facial expressions. Given the amount of information expressed by facial gestures, this type of systems has potential applications in multiple domains such as Human Computer Interaction, Marketing or Healthcare. For this reason, the topic has attracted a lot of attention in Computer Vision and Machine Learning communities during the past two decades. Despite the advances in the field, most of facial expression analysis problems can be considered far from being solved.
In this context, this dissertation is motivated by the observation that the vast majority of methods in the literature has followed the Supervised Learning paradigm, where models are trained by using data explicitly labelled according to the target problem. However, this approach presents some limitations given the difficult annotation process typically involved in facial expression analysis tasks. In order to address this challenge, we propose to pose Automatic Facial Behavior Analysis from a weakly-supervised perspective. Different from the fully-supervised strategy, weakly-supervised models are trained by using labels which are easy to collect but only provide partial information about the task that aims to be solved (i.e, weak-labels). Following this idea, we present different weakly-supervised methods to address standard problems in the field such as Action Unit Recognition, Expression Intensity Estimation or Affect Analysis. Our results obtained by evaluating the proposed approaches on these tasks, demonstrate that weakly-supervised learning may provide a potential solution to alleviate the need of annotated data in Automatic Facial Behavior Analysis. Moreover we also show how these approaches are able to facilitate the labelling process of databases designed for this purpose.Aquesta tesi doctoral se centra en el problema de l'Anàlisi Automàtic del Comportament Facial, on l'objectiu és desenvolupar sistemes autònoms capaços de reconèixer i entendre les expressions facials humanes. Donada la quantitat d'informació que es pot extreure d'aquestes expressions, sistemes d'aquest tipus tenen multitud d'aplicacions en camps com la Interacció Home-Màquina, el Marketing o l'Assistència Clínica. Per aquesta raó, investigadors en Visió per Computador i Aprenentatge Automàtic han destinat molts esforços en les últimes dècades per tal d'aconseguir avenços en aquest sentit. Malgrat això, la majoria de problemes relacionats amb l'anàlisi automàtic d'expressions facials encara estan lluny de ser conisderats com a resolts. En aquest context, aquesta tesi està motivada pel fet que la majoria de mètodes proposats fins ara han seguit el paradigma d'aprenentatge supervisat, on els models són entrenats mitjançant dades anotades explícitament en funció del problema a resoldre. Desafortunadament, aquesta estratègia té grans limitacions donat que l'anotació d'expressions en bases de dades és una tasca molt costosa i lenta. Per tal d'afrontar aquest repte, aquesta tesi proposa encarar l'Anàlisi Automàtic del Comportament Facial mitjançant el paradigma d'aprenentatge dèbilment supervisat. A diferència del cas anterior, aquests models poden ser entrenats utilitzant etiquetes que són fàcils d'anotar però que només donen informació parcial sobre la tasca que es vol aprendre. Seguint aquesta idea, desenvolupem un conjunt de mètodes per tal de resoldre problemes típics en el camp com el reconeixement d' "Action Units", l'Estimació d'Intensitat d'Expressions Facials o l'Anàlisi Emocional. Els resultats obtinguts avaluant els mètodes presentats en aquestes tasques, demostren que l'aprenentatge dèbilment supervisat pot ser una solució per tal de reduir l'esforç d'anotació en l'Anàlisi Automàtic del Comportament Facial. De la mateixa manera, aquests mètodes es mostren útils a l'hora de facilitar el procés d'etiquetatge de bases de dades creades per aquest propòsit.
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Kochenderfer, Mykel J. "Adaptive modelling and planning for learning intelligent behaviour." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/1408.
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An intelligent agent must be capable of using its past experience to develop an understanding of how its actions affect the world in which it is situated. Given some objective, the agent must be able to effectively use its understanding of the world to produce a plan that is robust to the uncertainty present in the world. This thesis presents a novel computational framework called the Adaptive Modelling and Planning System (AMPS) that aims to meet these requirements for intelligence. The challenge of the agent is to use its experience in the world to generate a model. In problems with large state and action spaces, the agent can generalise from limited experience by grouping together similar states and actions, effectively partitioning the state and action spaces into finite sets of regions. This process is called abstraction. Several different abstraction approaches have been proposed in the literature, but the existing algorithms have many limitations. They generally only increase resolution, require a large amount of data before changing the abstraction, do not generalise over actions, and are computationally expensive. AMPS aims to solve these problems using a new kind of approach. AMPS splits and merges existing regions in its abstraction according to a set of heuristics. The system introduces splits using a mechanism related to supervised learning and is defined in a general way, allowing AMPS to leverage a wide variety of representations. The system merges existing regions when an analysis of the current plan indicates that doing so could be useful. Because several different regions may require revision at any given time, AMPS prioritises revision to best utilise whatever computational resources are available. Changes in the abstraction lead to changes in the model, requiring changes to the plan. AMPS prioritises the planning process, and when the agent has time, it replans in high-priority regions. This thesis demonstrates the flexibility and strength of this approach in learning intelligent behaviour from limited experience.
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Egginton, Robert. "Predicting and Learning the Behaviour of Intelligent Agents." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521100.
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Wood, Dawn Helaine. "Personality representation : predicting behaviour for personalised learning support." Thesis, University of Hull, 2010. http://hydra.hull.ac.uk/resources/hull:6862.
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The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles. This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods. This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support? Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services. The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach. The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support. The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming.
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Wilson, Benjamin. "Artificial grammar learning in primates : behaviour and neuroimaging." Thesis, University of Newcastle upon Tyne, 2014. http://hdl.handle.net/10443/2373.
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Neuroimaging studies have shown that natural language processes engage left hemisphere perisylvian brain regions. Artificial Grammars (AG), which are designed to emulate aspects of language syntactic structure, recruit comparable brain areas. Nonhuman animals have been shown to learn a range of different AGs. However, no data is currently available regarding the brain areas that support these processes. In this thesis, I combined behavioural artificial grammar learning (AGL) and fMRI experiments to generate insights regarding language evolution, and as a first step to developing animal model systems for aspects of language processing. These experiments provide novel evidence that nonhuman primates are able to learn a non-deterministic AG, designed to emulate some of the variability of the structure of sentences in natural language, and demonstrated notable correspondences between the brain regions involved in macaque and human AGL. I developed a quantitative method to compare AGL abilities across species and studies, and a novel eye-tracking technique with which to collect objective behavioural data. Using this technique, and a refined version of a traditional video-coding paradigm, I demonstrated that Rhesus macaques notice violations of the AG structure and that these results could not be explained by reliance on simple cues. Common marmosets also showed evidence of AGL however, these results may have been driven by simple learning strategies. Comparative fMRI experiments showed that, in humans, violations of the AG activated a number of perisylvian brain regions associated with language processing, including the ventral frontal cortex (vFC), temporal and temporo-parietal regions, although not Broca’s area (BA44/45). In Rhesus macaques, comparable patterns of activation were seen in the ventral frontal cortex and temporo-parietal regions. Additional activation in BA44/45 in macaques provides interesting insights into the evolution of this region. These experiments provide novel evidence regarding the AGL capabilities of nonhuman primates, and the brain areas that support them, suggesting that some language related functions may represent generic, rather than language specific processes. Therefore, some of the brain regions involved in AGL in both species might share a common evolutionary heritage, and therefore Rhesus macaques might represent a valuable animal model system for aspects of language processing.
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Nicolaou, Michael (Mihalis). "Machine learning for automatic analysis of affective behaviour." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/44543.
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The automated analysis of affect has been gaining rapidly increasing attention by researchers over the past two decades, as it constitutes a fundamental step towards achieving next-generation computing technologies and integrating them into everyday life (e.g. via affect-aware, user-adaptive interfaces, medical imaging, health assessment, ambient intelligence etc.). The work presented in this thesis focuses on several fundamental problems manifesting in the course towards the achievement of reliable, accurate and robust affect sensing systems. In more detail, the motivation behind this work lies in recent developments in the field, namely (i) the creation of large, audiovisual databases for affect analysis in the so-called ''Big-Data'' era, along with (ii) the need to deploy systems under demanding, real-world conditions. These developments led to the requirement for the analysis of emotion expressions continuously in time, instead of merely processing static images, thus unveiling the wide range of temporal dynamics related to human behaviour to researchers. The latter entails another deviation from the traditional line of research in the field: instead of focusing on predicting posed, discrete basic emotions (happiness, surprise etc.), it became necessary to focus on spontaneous, naturalistic expressions captured under settings more proximal to real-world conditions, utilising more expressive emotion descriptions than a set of discrete labels. To this end, the main motivation of this thesis is to deal with challenges arising from the adoption of continuous dimensional emotion descriptions under naturalistic scenarios, considered to capture a much wider spectrum of expressive variability than basic emotions, and most importantly model emotional states which are commonly expressed by humans in their everyday life. In the first part of this thesis, we attempt to demystify the quite unexplored problem of predicting continuous emotional dimensions. This work is amongst the first to explore the problem of predicting emotion dimensions via multi-modal fusion, utilising facial expressions, auditory cues and shoulder gestures. A major contribution of the work presented in this thesis lies in proposing the utilisation of various relationships exhibited by emotion dimensions in order to improve the prediction accuracy of machine learning methods - an idea which has been taken on by other researchers in the field since. In order to experimentally evaluate this, we extend methods such as the Long Short-Term Memory Neural Networks (LSTM), the Relevance Vector Machine (RVM) and Canonical Correlation Analysis (CCA) in order to exploit output relationships in learning. As it is shown, this increases the accuracy of machine learning models applied to this task. The annotation of continuous dimensional emotions is a tedious task, highly prone to the influence of various types of noise. Performed real-time by several annotators (usually experts), the annotation process can be heavily biased by factors such as subjective interpretations of the emotional states observed, the inherent ambiguity of labels related to human behaviour, the varying reaction lags exhibited by each annotator as well as other factors such as input device noise and annotation errors. In effect, the annotations manifest a strong spatio-temporal annotator-specific bias. Failing to properly deal with annotation bias and noise leads to an inaccurate ground truth, and therefore to ill-generalisable machine learning models. This deems the proper fusion of multiple annotations, and the inference of a clean, corrected version of the ''ground truth'' as one of the most significant challenges in the area. A highly important contribution of this thesis lies in the introduction of Dynamic Probabilistic Canonical Correlation Analysis (DPCCA), a method aimed at fusing noisy continuous annotations. By adopting a private-shared space model, we isolate the individual characteristics that are annotator-specific and not shared, while most importantly we model the common, underlying annotation which is shared by annotators (i.e., the derived ground truth). By further learning temporal dynamics and incorporating a time-warping process, we are able to derive a clean version of the ground truth given multiple annotations, eliminating temporal discrepancies and other nuisances. The integration of the temporal alignment process within the proposed private-shared space model deems DPCCA suitable for the problem of temporally aligning human behaviour; that is, given temporally unsynchronised sequences (e.g., videos of two persons smiling), the goal is to generate the temporally synchronised sequences (e.g., the smile apex should co-occur in the videos). Temporal alignment is an important problem for many applications where multiple datasets need to be aligned in time. Furthermore, it is particularly suitable for the analysis of facial expressions, where the activation of facial muscles (Action Units) typically follows a set of predefined temporal phases. A highly challenging scenario is when the observations are perturbed by gross, non-Gaussian noise (e.g., occlusions), as is often the case when analysing data acquired under real-world conditions. To account for non-Gaussian noise, a robust variant of Canonical Correlation Analysis (RCCA) for robust fusion and temporal alignment is proposed. The model captures the shared, low-rank subspace of the observations, isolating the gross noise in a sparse noise term. RCCA is amongst the first robust variants of CCA proposed in literature, and as we show in related experiments outperforms other, state-of-the-art methods for related tasks such as the fusion of multiple modalities under gross noise. Beyond private-shared space models, Component Analysis (CA) is an integral component of most computer vision systems, particularly in terms of reducing the usually high-dimensional input spaces in a meaningful manner pertaining to the task-at-hand (e.g., prediction, clustering). A final, significant contribution of this thesis lies in proposing the first unifying framework for probabilistic component analysis. The proposed framework covers most well-known CA methods, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Locality Preserving Projections (LPP) and Slow Feature Analysis (SFA), providing further theoretical insights into the workings of CA. Moreover, the proposed framework is highly flexible, enabling novel CA methods to be generated by simply manipulating the connectivity of latent variables (i.e. the latent neighbourhood). As shown experimentally, methods derived via the proposed framework outperform other equivalents in several problems related to affect sensing and facial expression analysis, while providing advantages such as reduced complexity and explicit variance modelling.
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Mackney, Pamela Ann. "Memory windows in stickleback behaviour." Thesis, Bangor University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321388.
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Hastings, Richard Patrick. "A functional approach to care staff behaviour." Thesis, University of Southampton, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239538.
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Perrot, Michaël. "Theory and algorithms for learning metrics with controlled behaviour." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES072/document.
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De nombreux algorithmes en Apprentissage Automatique utilisent une notion de distance ou de similarité entre les exemples pour résoudre divers problèmes tels que la classification, le partitionnement ou l'adaptation de domaine. En fonction des tâches considérées ces métriques devraient avoir des propriétés différentes mais les choisir manuellement peut-être fastidieux et difficile. Une solution naturelle est alors d'adapter automatiquement ces métriques à la tâche considérée. Il s'agit alors d'un problème connu sous le nom d'Apprentissage de Métriques et où le but est principalement de trouver les meilleurs paramètres d'une métrique respectant des contraintes spécifiques. Les approches classiques dans ce domaine se focalisent habituellement sur l'apprentissage de distances de Mahalanobis ou de similarités bilinéaires et l'une des principales limitations est le fait que le contrôle du comportement de ces métriques est souvent limité. De plus, si des travaux théoriques existent pour justifier de la capacité de généralisation des modèles appris, la plupart des approches ne présentent pas de telles garanties. Dans cette thèse nous proposons de nouveaux algorithmes pour apprendre des métriques à comportement contrôlé et nous mettons l'accent sur les propriétés théoriques de ceux-ci. Nous proposons quatre contributions distinctes qui peuvent être séparées en deux parties: (i) contrôler la métrique apprise en utilisant une métrique de référence et (ii) contrôler la transformation induite par la métrique apprise. Notre première contribution est une approche locale d'apprentissage de métriques où le but est de régresser une distance proportionnelle à la perception humaine des couleurs. Notre approche est justifiée théoriquement par des garanties en généralisation sur les métriques apprises. Dans notre deuxième contribution nous nous sommes intéressés à l'analyse théorique de l'intérêt d'utiliser une métrique de référence dans un terme de régularisation biaisé pour aider lors du processus d'apprentissage. Nous proposons d'utiliser trois cadres théoriques différents qui nous permettent de dériver trois mesures différentes de l'apport de la métrique de référence. Ces mesures nous donnent un aperçu de l'impact de la métrique de référence sur celle apprise. Dans notre troisième contribution nous proposons un algorithme d'apprentissage de métriques où la transformation induite est contrôlée. L'idée est que, plutôt que d'utiliser des contraintes de similarité et de dissimilarité, chaque exemple est associé à un point virtuel qui appartient déjà à l'espace induit par la métrique apprise. D'un point de vue théorique nous montrons que les métriques apprises de cette façon généralisent bien mais aussi que notre approche est liée à une méthode plus classique d'apprentissage de métriques basée sur des contraintes de paires. Dans notre quatrième contribution nous essayons aussi de contrôler la transformation induite par une métrique apprise. Cependant, plutôt que considérer un contrôle individuel pour chaque exemple, nous proposons une approche plus globale en forçant la transformation à suivre une transformation géométrique associée à un problème de transport optimal. D'un point de vue théorique nous proposons une discussion sur le lien entre la transformation associée à la métrique apprise et la transformation associée au problème de transport optimal. D'un point de vue plus pratique nous montrons l'intérêt de notre approche pour l'adaptation de domaine mais aussi pour l'édition d'imagesMany Machine Learning algorithms make use of a notion of distance or similarity between examples to solve various problems such as classification, clustering or domain adaptation. Depending on the tasks considered these metrics should have different properties but manually choosing an adapted comparison function can be tedious and difficult. A natural trend is then to automatically tailor such metrics to the task at hand. This is known as Metric Learning and the goal is mainly to find the best parameters of a metric under some specific constraints. Standard approaches in this field usually focus on learning Mahalanobis distances or Bilinear similarities and one of the main limitations is that the control over the behaviour of the learned metrics is often limited. Furthermore if some theoretical works exist to justify the generalization ability of the learned models, most of the approaches do not come with such guarantees. In this thesis we propose new algorithms to learn metrics with a controlled behaviour and we put a particular emphasis on the theoretical properties of these algorithms. We propose four distinct contributions which can be separated in two parts, namely (i) controlling the metric with respect to a reference metric and (ii) controlling the underlying transformation corresponding to the learned metric. Our first contribution is a local metric learning method where the goal is to regress a distance proportional to the human perception of colors. Our approach is backed up by theoretical guarantees on the generalization ability of the learned metrics. In our second contribution we are interested in theoretically studying the interest of using a reference metric in a biased regularization term to help during the learning process. We propose to use three different theoretical frameworks allowing us to derive three different measures of goodness for the reference metric. These measures give us some insights on the impact of the reference metric on the learned one. In our third contribution we propose a metric learning algorithm where the underlying transformation is controlled. The idea is that instead of using similarity and dissimilarity constraints we associate each learning example to a so-called virtual point belonging to the output space associated with the learned metric. We theoretically show that metrics learned in this way generalize well but also that our approach is linked to a classic metric learning method based on pairs constraints. In our fourth contribution we also try to control the underlying transformation of a learned metric. However instead of considering a point-wise control we consider a global one by forcing the transformation to follow the geometrical transformation associated to an optimal transport problem. From a theoretical standpoint we propose a discussion on the link between the transformation associated with the learned metric and the transformation associated with the optimal transport problem. On a more practical side we show the interest of our approach for domain adaptation but also for a task of seamless copy in images
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Meachum, Cynthia L. "The regulation of instrumental behaviour by toxicosis." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.256395.
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Bromfield, Carolyn. "Behaviour for learning : an analysis of trainee teachers' concerns." Thesis, University of the West of England, Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444528.
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Bruce, Melanie. "Preparing people with learning disabilities for cognitive behaviour therapy." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435156.
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Gonzalez, Claudia Cristina. "Linking brain and behaviour in motor sequence learning tasks." Thesis, University of Leeds, 2012. http://etheses.whiterose.ac.uk/3603/.
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Sequence learning is a fundamental brain function that allows for the acquisition of a wide range of skills. Unlearned movements become faster and more accurate with repetition, due to a process called prediction. Predictive behaviour observed in the eye and hand compensates for the inherent temporal delays in the sensorimotor system and allows for the generation of motor actions prior to visual guidance. We investigated predictive behaviour and the brain areas associated with this processing in (i) the oculomotor system (Eye Only (EO): saccade vs. pursuit) and (ii) during eye and hand coordination (EH). Participants were asked to track a continuous moving target in predictable or random sequence conditions. EO and EH experiments were divided into 1) EO behavioural and 2) EO fMRI findings, and 3) EH behavioural and 4) EH fMRI findings. Results provide new insights into how individuals predict when learning a sequence of target movements, which is not limited to short--‐term memory capacities and that forms a link between shorter and longer--‐term motor skill learning. Furthermore, brain imaging results revealed distinct levels of activation within and between brain areas for repeated and randomized sequences that reflect the distinct timing threshold and adaptation levels needed for the two oculomotor systems. EH results revealed similar predictive behaviour in the eye and the hand, but also demonstrated enhanced coupling between the two motor systems during sequence learning. EH brain imaging findings have provided novel insights into the brain areas involved in coordination, and those areas more associated with sequence learning. Results show evidence of common predictive networks used for the eye and hand during learning.
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Jebara, Tony (Tony S. ). 1974. "Action-reaction learning : analysis and synthesis of human behaviour." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/29544.
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Hughes, Donna. "The diagnosis of student learning styles and study behaviour /." Title page, table of contents and abstract only, 1989. http://web4.library.adelaide.edu.au/thesis/09SB/09sbh893.pdf.
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Witkowski, Christopher Mark. "Schemes for learning and behaviour : a new expectancy model." Thesis, Queen Mary, University of London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265403.
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Allam, Hossam. "Modelling learning behaviour of intelligent agents using UML 2.0." Thesis, University of Plymouth, 2005. http://hdl.handle.net/10026.1/338.
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This thesis aims to explore and demonstrate the ability of the new standard of structural and behavioural components in Unified Modelling Language (UML 2.0 / 2004) to model the learning behaviour of Intelligent Agents. The thesis adopts the research direction that views agent-oriented systems as an extension to object-oriented systems. In view of the fact that UML has been the de facto standard for modelling object-oriented systems, this thesis concentrates on exploring such modelling potential with Intelligent Agent-oriented systems. Intelligent Agents are Agents that have the capability to learn and reach agreement with other Agents or users. The research focuses on modelling the learning behaviour of a single Intelligent Agent, as it is the core of multi-agent systems. During the writing of the thesis, the only work done to use UML 2.0 to model structural components of Agents was from the Foundation for Intelligent Physical Agent (FIPA). The research builds upon, explores, and utilises this work and provides further development to model the structural components of learning behaviour of Intelligent Agents. The research also shows the ability of UML version 2.0 behaviour diagrams, namely activity diagrams and sequence diagrams, to model the learning behaviour of Intelligent Agents that use learning from observation and discovery as well as learning from examples of strategies. The research also evaluates if UML 2.0 state machine diagrams can model specific reinforcement learning algorithms, namely dynamic programming, Monte Carlo, and temporal difference algorithms. The thesis includes user guides of UML 2.0 activity, sequence, and state machine diagrams to allow researchers in agent-oriented systems to use the UML 2.0 diagrams in modelling the learning components of Intelligent Agents. The capacity for learning is a crucial feature of Intelligent Agents. The research identifies different learning components required to model the learning behaviour of Intelligent Agents such as learning goals, learning strategies, and learning feedback methods. In recent years, the Agent-oriented research has been geared towards the agency dimension of Intelligent Agents. Thus, there is a need to conduct more research on the intelligence dimension of Intelligent Agents, such as negotiation and argumentation skills. The research shows that behavioural components of UML 2.0 are capable of modelling the learning behaviour of Intelligent Agents while structural components of UML 2.0 need extension to cover structural requirements of Agents and Intelligent Agents. UML 2.0 has an extension mechanism to fulfil Agents and Intelligent Agents for such requirements. This thesis will lead to increasing interest in the intelligence dimension rather than the agency dimension of Intelligent Agents, and pave the way for objectoriented methodologies to shift more easily to paradigms of Intelligent Agent-oriented systems.
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Yadav, Mayank. "Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285563.
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Programming a complex robot is difficult, time-consuming and expensive. Learning from Demonstration (LfD) is a methodology where a teacher demonst--rates a task and the robot learns to execute the task. This thesis presents a method which generates reactive robot behaviour learned from demonstration where sequences of action are implicitly coded in a rule-based manner. It also presents a novel approach to find behaviour hierarchy among behaviours of a demonstration.In the thesis, the system learns the activation rule of primitives as well as the association that should be performed between sensor and motor primitives. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. The underlying rule for the activation of associations is learned using a neural network from demonstrated data. Behaviour hierarchy among different sensor-motor associations is learnt using heuristic logic minimization technique called espresso algorithm. Once relationship among the associations is learnt, all the logical relationships are used to generate a hierarchical tree of behaviours using a novel approach that is proposed in the thesis. This allows us to represent the behaviour in hierarchical way as a set of associations between sensor and motor primitives in a readable script which is deployed on Playful.The method is tested on a simulation by varying the number of targets, showing that the system learns underlying rules for sensor-motor association providing high F1-score for each association. It is also shown by changing the complexity of simulation that the system generalises the solution and the knowledge learnt from a sensor-motor association is transferable with all the instances of that association.Att programmera en komplex robot är svårt, tidskrävande och dyrt. Learning from Demonstration (LfD) är en metod där en lärare visar en uppgift och roboten lär sig att utföra uppgiften. Denna avhandling presenterar en metod som genererar reaktivt robotbeteende lärt från demonstration där handlingssek--venser implicit kodas på ett regelbaserat sätt. Den presenterar också ett nytt tillvägagån- -gssätt för att hitta beteendeshierarki bland beteenden i en demonstration.I avhandlingen lär sig systemet aktiveringsregeln för primitiva såväl som sambandet som ska utföras mellan sensor och motor primitives. För att göra det använder vi det lekfulla programmeringsspråket som bygger på reaktivt programmeringsparadigm. Den underliggande regeln för aktivering av föreningar lärs med hjälp av ett neuralt nätverk från demonstrerade data. Beteendeshierarki mellan olika sensor-motorföreningar lärs med hjälp av heuristisk logikminimeringsteknik som kallas espressoalgoritm. När förhållandet mellan föreningarna har lärt sig används alla logiska förhållanden för att generera ett hierarkiskt beteendeträd med den nya metoden som föreslås i avhandlingen. Detta gör att vi kan representera beteendet på hierarkiskt sätt som en uppsättning associeringar mellan sensor och motorprimitiv i ett läsbart skript som används på lekfull.Metoden testas på en simulering genom att variera antalet mål, vilket visar att systemet lär sig underliggande regler för sensor-motorassociation som ger hög F1-poäng för varje association. Det visas också genom att ändra komplexiteten i simuleringen att systemet generaliserar lösningen och kunskapen som lärts från en sensor-motorförening är överförbar med alla förekomster av den associeringen.
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Rao, Rashmi Jayathirtha. "Modeling learning behaviour and cognitive bias from web logs." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492560600002105.
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Yu, Shen. "A Bayesian machine learning system for recognizing group behaviour." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=32565.
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Sharma, Nirwan. "Facilitating individual learning, collaborative learning and behaviour change in citizen science through interface design." Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=238539.
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Citizen science is a collaboration between members of the public and scientific experts. Within the environmental realm – where citizen science is particularly well expressed – this collaboration often concerns members of the public involved in scientific data gathering and processing at a large-scale to generate data that can subsequently be used by the scientists to improve scientific knowledge, understanding and theories. As these collaborations are increasingly being mediated via digital technologies, the overall aim of this thesis was to explore the potential of user interface design for citizen science, within the context of environmental sciences while using an established citizen science platform, BeeWatch. Particular attention was paid to the potential of such interface development to foster a move from situations of 'expert-novice' to progressive forms of collaborations and participation in citizen science. The overall conclusion from this thesis is that interactive technologies can lead to the development of expertise for biological recording – and thus, narrowing the gap between expert and novice – as well as progressing the level of participation within and fostering behaviour changes for conservation action.
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Fath, El-Bab Mohamed. "Cognitive event related potentials during a learning task." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367971.
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Costa, Daniel S. J. "Maintenance of behaviour when reinforcement becomes delayed." Connect to full text, 2009. http://ses.library.usyd.edu.au/handle/2123/5078.
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Thesis (Ph. D.)--University of Sydney, 2009.Includes graphs and tables. Title from title screen (viewed June 15, 2009) Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Psychology, Faculty of Science. Includes bibliographical references. Also available in print form.
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Martens, Marilee A. "Williams syndrome : links between brain, cognition, and behaviour /." Connect to thesis, 2005. http://eprints.unimelb.edu.au/archive/00001515.
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Lindquist, Barbro. "Hydrocephalus in children : cognition and behaviour /." Göteborg : Department of Pediatrics, Institute of Clinical Sciences, Sahlgrenska Academy at Göteborg University, 2007. http://hdl.handle.net/2077/2557.
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Manoochehri, Lisa. "Exploring the effects of CBT on motivation for learning and changing learning behaviour using IPA." Thesis, University of Essex, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537960.
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Pigott, Julian. "English learning as a means of self-fulfilment : a grounded theory of language learning behaviour." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/79953/.
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In this thesis I present an original theory of language learning behaviour derived from a grounded theory analysis of interview testimony from five Japanese learners of English. The theory takes the form of the basic English Learning as a Means of Self-fulfilment (ELMS) model. This model explains English-learning behaviour in terms of the engagement of four types of self-fulfilment drive: a drive for intellectual and affective stimulation (entertainment drive); a drive to ‘expand one’s horizons’ (perspective drive); a drive to make a ‘success’ of oneself (status drive); and a drive to engage in interaction with others (communication drive). Two additional models built on the foundation of the basic ELMS model are also introduced: the expanded ELMS model explains how learning behaviour is mediated by cultural and institutional context, and by the individual’s attempts to make sense of, and control, experience; and the Learning as a Means of Self-fulfilment (LMS) model is a hypothetical general model of learning which incorporates existing concepts from the literature. The results of the analysis demonstrate the importance of structure, rather than agency, in shaping language-learning behaviour. The theoretical rendering of motivation that emerges from the analysis is differentiated from that of motivation as a force constantly underlying behaviour. Instead, motivation is seen to make only sporadic appearances on the stage of consciousness, and to be responsible for behavioural change rather than behavioural routine. It follows that unexpected events that stimulate changes in beliefs about the self or about language learning may have much to tell us about motivation. This research does not so much build upon existing theory as problematise it. The results challenge prevailing conceptualisations of motivation, dominant discourses and practices associated with the term within applied linguistics and Japanese English language education, and the utility of the concept itself. It is a methodologically innovative investigation into the relationship between motivation and English learning in the Japanese context, with implications that extend beyond this context.
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Croy, Marion Isobel. "Characteristics of learning associated with feeding in marine predators." Thesis, Bangor University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236351.
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Russell, Avery L., and Daniel R. Papaj. "Artificial pollen dispensing flowers and feeders for bee behaviour experiments." Enviroquest Ltd, 2016. http://hdl.handle.net/10150/621206.
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The study of foraging behaviour in plant-pollinator mutualisms has benefitted from the use of artificial flowers to manipulate floral display traits and the delivery of floral rewards. The two most common floral rewards are pollen and nectar; some pollinators, such as bees, are obliged to collect both for survival and reproduction. While flexible designs for artificial flowers providing nectar rewards abound, useful designs for artificial flowers that dispense pollen are few. This disparity mirrors a heavy emphasis on nectar collection in the study of pollinator foraging behaviour. In this study we describe a novel, easily constructed and modifiable artificial flower that dispenses flexible amounts of pollen via an ‘anther’ composed of a chenille stem. Using controlled lab assays, we show that more pulverized honeybee pollen is collected by bumblebee (Bombus impatiens) workers at chenille stem feeders than at dish-type feeders. We suggest that the paucity of studies examining pollinator cognition in the context of pollen rewards might be partly remedied if researchers had access to inexpensive and easily adjustable pollen-offering surrogate flowers.
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Herron, Denise. "The attributional style of paid carers of people with learning disabilities." Thesis, University of East Anglia, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300067.
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Gu, Dongbing. "Behaviour-based learning and fuzzy control of autonomous quadruped robots." Thesis, University of Essex, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400989.
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Wang, Hongfang. "Non-rigid motion behaviour learning : a spectral and graphical approach." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441066.
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Thompson, Doreen Elizabeth. "Observational learning of goal-directed behaviour in very young children." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621250.
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Newton, Jonathan Charles Scott. "Essays on coalitional behaviour, social learning & strategic information transmission." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609307.
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Le, Pelley Michael Edward. "The interaction of learning and memory in human causal behaviour." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620526.
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Wright, Emma. "The effect of pathogens on honeybee learning and foraging behaviour." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/57266/.
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The European honeybee, Apis mellifera, is important economically not just for honey production but also as a pollinator. Bee pollinated plants contribute towards one third of the food eaten worldwide. However, honeybee numbers in some areas are declining. A range of interacting factors are thought to be involved, including pathogens and parasites, loss of forage, pesticide use, bad weather, and limited genetic variability. Pathogens are also known to cause changes in the behaviour of their hosts and these premortality and sublethal effects of disease may well play a role in colony declines and are the focus of this thesis. For individual bees the fungus Metarhizium anisopliae was used as a model pathogen and RT-Q-PCR was used to detect and quantify naturally occurring pathogens. In field colonies the level of infestation of the parasitic mite Varroa destructor was modified as a surrogate for disease load as the amounts of many viruses correlate with mite levels. Survival experiments showed that both disease load and forage availability had an effect on honeybee longevity and feeding the bees pollen increased their survival. Learning experiments showed that both the fungus and some of the bees’ naturally occurring pathogens caused changes in the learning ability of young adult and older forager bees. Young adult bees were better able to learn when infected with the fungus, possibly because it made them more responsive to the sucrose stimulus, whilst older forager bees where less able to learn when infected with the fungus. Harmonic radar was used to show that honeybee flight ability was affected by naturally occurring pathogens, especially deformed wing virus which caused bees to fly shorter distances and for shorter amounts of time than uninfected bees. Observation hives were used to study in-hive behaviour showing that bees with more pathogens were likely to start foraging earlier than healthier bees.
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Pillay, Nalisha. "The influence of social learning behaviour during entrepreneurial opportunity development." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/79638.
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The sustainability of nascent entrepreneurs remains a critical factor in addressing society's deep-rooted problems and overall welfare. From this perspective, supporting entrepreneurial learning during their entrepreneurial ventures is critical to the entire community’s success. This study investigates the influence of an entrepreneur’s social learnings, their capacity to cope with new ventures, and the perceptions about their successes or failures during the opportunity development phase.
The study finds that entrepreneurs adopt learning behaviour primarily from their social background, driven by a strong desire to rebel against society’s bias, engage in iterative, collaborative engagement with the community. Frequently adopting self-reflection, learning by interpreting and an emotional evolution of their entrepreneurial mind-set relying on the logic of bricolage and emotional cognition to create opportunities that support the communities.
This study is limited to a sample of South African entrepreneurs and entrepreneurial mentors using in-depth semi-structured interviews. The study adopts an exploratory and qualitative methodology.
The study finds application for both all entrepreneurs, policy makers and educational learning programs involved in the mentorship and development of entrepreneurs.
This study provides a deeper analysis of opportunity development phase of a nascent entrepreneur. With a focus on the social learns, emotional cognitions and experiential learning factors that drive and inhibit them and further obtaining an understanding the coping mechanisms that could be adopted for effective and sustainable opportunity development and, ultimately, ensuring that nascent entrepreneurs transition to the final phase of entrepreneurship.Mini Dissertation (MBA)--University of Pretoria, 2020.
pt2021
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
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Widmann, Andreas [Verfasser], and Regina H. [Akademischer Betreuer] Mulder. "Team learning toward enhancing innovative work behaviour in vocational educator teams - The relationship between team learning conditions, team learning behaviours and team learning products over time / Andreas Widmann ; Betreuer: Regina H. Mulder." Regensburg : Universitätsbibliothek Regensburg, 2019. http://d-nb.info/1201160685/34.
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Good, M. "The role of the avian hippocampus in learning and memory." Thesis, University of York, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234899.
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