For my dissertation, I analyze the impact of English philosopher Stephen Toulmin's 1958 The Uses of Argument on philosophy and rhetoric. Because Toulmin holds that arguments must be assessed in relation to their original contexts, I begin by examining the intellectual context surrounding The Uses of Argument. I trace the development of formal, symbolic logic from the latter part of the nineteenth century and into the middle of the twentieth century. I argue that Toulmin was inspired by the work of ordinary language philosophers such as Ludwig Wittgenstein; with The Uses of Argument, Toulmin challenges the mathematical, deductive, and positivistic logics of philosophers such as Rene Descartes and Bertrand Russell. Next, I consider features of The Uses of Argument that are overlooked or misunderstood by critics and reviewers, including Toulmin's discussion of probability, field-dependent and independent aspects of arguments, and epistemological theory.After finding that many of the reviews of Toulmin's work were not as critical as recent scholars of rhetoric have portrayed, I evaluate the various ways philosophers and speech communication scholars have responded to Toulmin's arguments. Before examining composition scholars' uses of Toulmin, I briefly trace the development of argumentation in composition instruction. Then, I evaluate the wide range of reactions to and uses of Toulmin's work by composition scholars.Finally, having examined the intellectual history, the critical reception, and the variety of scholarly applications of The Uses of Argument, I offer my own application of Toulmin's work. I explain that good, "Toulmin-inspired" arguments are (1) contextual, (2) supported, and (3) multi-sided. Using student examples from my freshman composition courses, I demonstrate how students and I have used Toulmin's schema in several stages of the writing process, including invention and revision. I conclude that while Toulmin's work has had a significant impact on argumentation theory and pedagogy, rhetoricians-particularly composition scholars-have not yet fully realized the rhetorical and epistemological implications of The Uses of Argument. / Department of English

This research adopts a sociocultural framework (Vygotsky, 1978) to investigate how multilingual children express their mathematical reasoning during collaborative problem solving. The topic is important because North America is becoming increasingly multicultural, and according to mathematics teachers this has complicated the challenges of teaching and learning mathematics. Many educators assume that children should be competent in the language of instruction before they engage with mathematical content (Civil, 2008; Gorgorió & Planas, 2001). A review of recent research in this area challenges the idea that multilingual students need to have mastered the official language of instruction prior to learning mathematics (Barwell, 2005; Civil, 2008; Moschkovich, 2007). These researchers demonstrate that the knowledge of the language of instruction is only one aspect of becoming competent in mathematics. My research was designed to build on the findings of the current research on multilingual children’s reasoning in order to more fully understand how multilingual children express their mathematical understanding and reasoning. For this study, two multilingual families, each with 3 children between the ages of 8 and 12, participated in a mathematical problem-solving activity. Findings show the children’s mathematical reasoning was evidence-based drawing on mathematical knowledge and world knowledge.

No description available.

No description available.

A central problem in case based reasoning (CBR) is how to store and retrieve cases. One approach to this problem is to use exemplar based models, where only the prototypical cases are stored. However, the development of an exemplar based model (EBM) requires the solution of several problems: (i) how can a EBM be represented? (ii) given a new case, how can a suitable exemplar be retrieved? (iii) what makes a good exemplar? (iv) how can an EBM be learned incrementally? This thesis develops a new model, called a probabilistic exemplar based model, that addresses these research questions. The model utilizes Bayesian networks to develop a suitable representation and uses probability theory to develop the foundations of the developed model. A probability propagation method is used to retrieve exemplars when a new case is presented and for assessing the prototypicality of an exemplar. The model learns incrementally by revising the exemplars retained and by updating the conditional probabilities required by the Bayesian network. The problem of ignorance, encountered when only a few cases have been observed, is tackled by introducing the concept of a virtual exemplar to represent all the unseen cases. The model is implemented in C and evaluated on three datasets. It is also contrasted with related work in CBR and machine learning (ML).

Research in decision making has identified the importance of prior knowledge and heuristics on decision making behaviour. These develop with experience in a fashion similar to how domain experts develop specialized knowledge structures and heuristic reasoning patterns. This research is extended to the domain of health and lay decision making in a series of studies characterizing conceptualizations of health and illness, information-seeking strategies, and the impact of medical information on lay decision making. Lay subjects included those with diabetes, heart disease, and no identified ongoing medical diagnosis. / Semi-structured interviews and think aloud methodology were employed. Interviews focused on understanding of health and illness, prior knowledge and beliefs, and decision making. In Study One, subjects were presented with health-related problem scenarios and instructed to think aloud as they reasoned through them to make decisions. In Study Two, subjects (lay and medical) were presented with a telecommunications device and scenarios of data to enter into the system. All data were audiorecorded, transcribed, and analyzed for factors and strategies related to information-seeking and decision making behaviours. / Lay understanding of health and illness was characterized as feeling well and functioning in everyday life. The knowledge used in making decisions was based on experience and socio-cultural tradition. Knowledge about disease was found to be decoupled from decisions to act related to illness. Additional information was sought using four criteria grounded in common experience: accessibility, familiarity, complexity, and credibility. These characteristics influenced interactions between lay people and domain experts, such as health care providers, and with technology designed by experts for lay users. / Both technical and lay people make decisions with incomplete information and uncertain outcomes. For lay people making decisions about health-related issues, this incomplete knowledge is filled in based on everyday life rather than medical and scientific facts.

No description available.

Providing a logical characterisation of rational agent reasoning has been a long standing challenge in artificial intelligence (AI) research. It is a challenge that is not only of interest for the construction of AI agents, but is of equal importance in the modelling of agent behaviour. The goal of this thesis is to contribute to the formalisation of agent reasoning by showing that the computational limitations of agents is a vital component of modelling rational behaviour. To achieve this aim, both motivational and formal aspects of resource-bounded agents are examined. It is a central argument of this thesis that accounting for computational limitations is critical to the success of agent reasoning, yet has received only limited attention from the broader research community. Consequently, an important contribution of this thesis is in its advancing of motivational arguments in support of the need to account for computational limitations in agent reasoning research. As a natural progression from the motivational arguments, the majority of this thesis is devoted to an examination of propositional approximate logics. These logics represent a step towards the development of resource-bounded agents, but are also applicable to other areas of automated reasoning. This thesis makes a number of contributions in mapping the space of approximate logics. In particular, it draws a connection between approximate logics and knowledge compilation, by developing an approximate knowledge compilation method based on Cadoli and Schaerf??s S-3 family of approximate logics. This method allows for the incremental compilation of a knowledge base, thus reducing the need for a costly recompilation process. Furthermore, each approximate compilation has well-defined logical properties due to its correspondence to a particular S-3 logic. Important contributions are also made in the examination of approximate logics for clausal reasoning. Clausal reasoning is of particular interest due to the efficiency of modern clausal satisfiability solvers and the related research into problem hardness. In particular, Finger's Logics of Limited Bivalence are shown to be applicable to clausal reasoning. This is subsequently shown to logically characterise the behaviour of the well-known DPLL algorithm for determining boolean satisfiability, when subjected to restricted branching.

Bibliography: p. 121-125. / xiv, 125 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / One of the central issues in Artificial Intelligence (AI) is common sense reasoning. This includes logics of knowledge and belief, non-monotonic reasoning, truth-maintenance and belief revision. Within these fields the notion of a consistent belief state is the crucial one. The issues of inconsistency and partiality of information are central to this thesis which proposes a logical knowledge representation formalism employing partial objects and partial worlds on its semantic side. The syntax includes a language, formulae, and partial theories. Partial worlds and theories are consistent, and contradictory information is assumed to arise in multiple agent situations. Relevant mathematical structures are discussed, in particular partial theories are related to partial worlds. A multiple agent case is considered. Partial theories can be partially ordered by an information ordering and the obtained lattice structure facilitates the theory selection process based on information value and truthness of theories. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998

Bibliography: p. 121-125. / xiv, 125 p. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / One of the central issues in Artificial Intelligence (AI) is common sense reasoning. This includes logics of knowledge and belief, non-monotonic reasoning, truth-maintenance and belief revision. Within these fields the notion of a consistent belief state is the crucial one. The issues of inconsistency and partiality of information are central to this thesis which proposes a logical knowledge representation formalism employing partial objects and partial worlds on its semantic side. The syntax includes a language, formulae, and partial theories. Partial worlds and theories are consistent, and contradictory information is assumed to arise in multiple agent situations. Relevant mathematical structures are discussed, in particular partial theories are related to partial worlds. A multiple agent case is considered. Partial theories can be partially ordered by an information ordering and the obtained lattice structure facilitates the theory selection process based on information value and truthness of theories. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 1998