Thesis (Ph. D.)--University of Sydney, 2009. / Title from title screen (viewed October 15, 2009) Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Dept. of Philosophy, Faculty of Arts. Degree awarded 2009; thesis submitted 2008. Includes bibliographical references. Also available in print form.

Thesis (M.A.)--Trinity Evangelical Divinity School, Deerfield, Ill., 1998. / Abstract. Includes bibliographical references (leaves 101-114).

Thesis (M.A.)--Trinity Evangelical Divinity School, Deerfield, Ill., 1998. / Abstract. Includes bibliographical references (leaves 101-114).

Thesis (M.A.)--Trinity Evangelical Divinity School, Deerfield, Ill., 1998. / Abstract. Includes bibliographical references (leaves 101-114).

Thesis (Th. M.)--Dallas Theological Seminary, 2006. / Includes bibliographical references (leaves [62]-66).

This dissertation examines John Rawls’s doctrine of public reason in light of Thomas Reid’s Common Sense philosophy. Chapter 1 introduces the topics of public reason and common sense in order to suggest that Thomas Reid’s philosophy can provide a beneficial contribution to public policy debates. Chapter 2 examines key features of Rawls’s doctrine of public reason. Chapter 3 suggests that there are critical problems with Rawls’s doctrine of public reason. Chapter 4 examines key features of Thomas Reid’s common sense philosophy. Chapter 5 shows Reid’s common sense application in early American history as well as contemporary scholarship. It argues that common sense can provide insight into two test-cases, abortion and same-sex marriage. Chapter 6 concludes the dissertation, summarizing the arguments and providing suggestions for further research.

One of the long-standing problems in artificial intelligence is the development of intelligent agents with complete visual understanding. Understanding entails recognition of scene attributes such as actors, objects and actions as well as reasoning about the common semantic structure that combines these attributes into a coherent description. While significant milestones have been achieved in the field of computer vision, majority of the work has been concentrated on supervised visual recognition where complex visual representations are learned and a few discrete categories or labels are assigned to these representations. This implies a closed world where the underlying assumption is that all environments contain the same objects and events, which are in one-to-one correspondence with the ground evidence in the image. Hence, the learned knowledge is limited to the annotated training set. An open world, on the other hand, does not assume the distribution of semantics and requires generalization beyond the training annotations. Increasingly complex models require massive amounts of training data and offer little to no explainability due to the lack of transparency in the decision-making process. The strength of artificial intelligence systems to offer explanations for their decisions is central to building user confidence and structuring smart human-machine interactions. In this dissertation, we develop an inherently explainable approach for generating rich interpretations of visual scenes. We move towards an open world open-domain visual understanding by decoupling the ideas of recognition and reasoning. We integrate common sense knowledge from large knowledge bases such as ConceptNet and the representation learning capabilities of deep learning approaches in a pattern theory formalism to interpret a complex visual scene. To be specific, we first define and develop the idea of contextualization to model and establish complex semantic relationships among concepts grounded in visual data. The resulting semantic structures, called interpretations allow us to represent the visual scene in an intermediate representation that can then be used as the source of knowledge for various modes of expression such as labels, captions and even question answering. Second, we explore the inherent explainability of such visual interpretations and define key components for extending the notion of explainability to intelligent agents for visual recognition. Finally, we describe a self-supervised model for segmenting untrimmed videos into its constituent events. We show that this approach can segment videos without the need for supervision - neither implicit nor explicit. Combined, we argue that these approaches offer an elegant path to inherently explainable, open domain visual understanding while negating the need for human supervision in the form of labels and/or captions. We show that the proposed approach can advance the state-of-the-art results in complex benchmarks to handle data imbalance, complex semantics, and complex visual scenes without the need for vast amounts of domain-specific training data. Extensive experiments on several publicly available datasets show the efficacy of the proposed approaches. We show that the proposed approaches outperform weakly-supervised and unsupervised baselines by up to 24% and achieves competitive segmentation results compared to fully supervised baselines. The self-supervised approach for video segmentation complements this top-down inference with efficient bottom-up processing, resulting in an elegant formalism for open-domain visual understanding.

I begin by examining Moore's notion of common sense because it is my contention that his work is not neatly separated; rather, his ethical and non-ethical philosophy have common sense as an underlying theme. For Moore, common sense was not reducible to indubitable, cracker-barrel wisdom; it is popularized science, what would be a matter of common sense if we were to take the trouble to learn. Moore's allegiance to science is the cord that connects his ethical and non-ethical work. In Principia Ethica, he attempts to introduce the spirit of scientific investigation into ethics. The 'naturalistic fallacy', to which special attention is given, is shown to be a guide to avoiding the error of essentialism, that is, of presuming that there is a unique good-making property or quality, common to all good things. Essentialism is rejected because it leads away from the open investigation characteristic of science, toward dogmatism. From this perspective, several popular criticisms of Moore's ethics are shown to be misguided. In the last chapter, an interpretation of Moore's non-naturalism based on my understanding of the naturalistic fallacy is given, that is quite different from the one most commonly accepted. / Thesis / Master of Arts (MA)

In this paper I will argue that some of the contradictions in the Problem of the Many occur since we're dealing with two different types of concepts of a cloud, one that is 'common-sense', and one that is 'scientific'. I will borrow from Noam Chomsky’s distinction between common-sense concepts, which are understood intuitively and are usually human-centric, and scientific concepts, which are carefully constructed and aspire to be objective. The common-sense concept of a cloud has certain properties (such as having a sharp boundary) which contradict some found in the scientific concept (such as having a vague boundary), and this is the source of some contradictions found within the Problem of the Many. I will propose that in the future we should approach the problem with this in mind, and not treat it as if we're dealing with only one concept of a cloud, but instead, several.

Yes / Illness representations shape responses to illness experienced by the self or by others. The illness representations held by family members of those with long-term conditions such as dementia influence their understanding of what is happening to the person and how they respond and provide support. The aim of this study is to explore components of illness representations (label, cause, control and timeline) in caregivers of people with dementia. This was an exploratory study; the data reported came from the Memory Impairment and Dementia Awareness Study (MIDAS). Data from semi-structured interviews with 50 caregivers of people with dementia were analysed using content analysis. The majority of caregivers gave accounts that appeared to endorse a medical/diagnostic label, although many used different terms interchangeably. Caregivers differentiated between direct causes and contributory factors, but the predominant explanation was that dementia had a biological cause. Other perceived causes were hereditary factors, ageing, lifestyle, life events and environmental factors. A limited number of caregivers were able to identify things that people with dementia could do to help manage the condition, while others thought nothing could be done. There were varying views about the efficacy of medication. In terms of timeline, there was considerable uncertainty about how dementia would progress over time. The extent of uncertainty about the cause, timeline and controllability of dementia indicated that caregivers need information on these areas. Tailored information and support taking account of caregivers' existing representations may be most beneficial.