Comparative analysis is the problem of predicting how a system will react to perturbations in its parameters, and why. For example, comparative analysis could be asked to explain why the period of an oscillating spring/block system would increase if the mass of the block were larger. This thesis formalizes the task of comparative analysis and presents two solution techniques: differential qualitative (DQ) analysis and exaggeration. Both techniques solve many comparative analysis problems, providing explanations suitable for use by design systems, automated diagnosis, intelligent tutoring systems, and explanation based generalization. This thesis explains the theoretical basis for each technique, describes how they are implemented, and discusses the difference between the two. DQ analysis is sound; it never generates an incorrect answer to a comparative analysis question. Although exaggeration does occasionally produce misleading answers, it solves a larger class of problems than DQ analysis and frequently results in simpler explanations.

As exploration of our solar system and outerspace move into the future, spacecraft are being developed to venture on increasingly challenging missions with bold objectives. The spacecraft tasked with completing these missions are becoming progressively more complex. This increases the potential for mission failure due to hardware malfunctions and unexpected spacecraft behavior. A solution to this problem lies in the development of an advanced fault management system. Fault management enables spacecraft to respond to failures and take repair actions so that it may continue its mission. The two main approaches developed for spacecraft fault management have been rule-based and model-based systems. Rules map sensor information to system behaviors, thus achieving fast response times, and making the actions of the fault management system explicit. These rules are developed by having a human reason through the interactions between spacecraft components. This process is limited by the number of interactions a human can reason about correctly. In the model-based approach, the human provides component models, and the fault management system reasons automatically about system wide interactions and complex fault combinations. This approach improves correctness, and makes explicit the underlying system models, whereas these are implicit in the rule-based approach. We propose a fault detection engine, Compiled Mode Estimation (CME) that unifies the strengths of the rule-based and model-based approaches. CME uses a compiled model to determine spacecraft behavior more accurately. Reasoning related to fault detection is compiled in an off-line process into a set of concurrent, localized diagnostic rules. These are then combined on-line along with sensor information to reconstruct the diagnosis of the system. These rules enable a human to inspect the diagnostic consequences of CME. Additionally, CME is capable of reasoning through component interactions automatically and still provide fast and correct responses. The implementation of this engine has been tested against the NEAR spacecraft advanced rule-based system, resulting in detection of failures beyond that of the rules. This evolution in fault detection will enable future missions to explore the furthest reaches of the solar system without the burden of human intervention to repair failed components.

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical.

There is general consensus that context can be a rich source of information about an object's identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin to those that apply to a single object. Here we introduce a simple probabilistic framework for modeling the relationship between context and object properties based on the correlation between the statistics of low-level features across the entire scene and the objects that it contains. The resulting scheme serves as an effective procedure for object priming, context driven focus of attention and automatic scale-selection on real-world scenes.

Wittgenstein once remarked that the same kind of reasoning that occurs in ordinary conversations about works of art can be found “in Ethics, but also in Philosophy.” That observation has been almost entirely overlooked by his commentators. What is aesthetic reasoning? What does it look like in conversations about art? And where might we find examples of such reasoning “in Ethics”? To set the stage for my answers, I begin with an overview of the early Wittgenstein’s view of ethics and aesthetics, emphasizing two ideas that were retained in his later view of aesthetic reasoning: the moral importance of non-moral descriptions, and the power of a “picture” to regulate action and thought. I illustrate those ideas by considering the moral influence of Tolstoy’s parable of the Good Samaritan on Wittgenstein. Next, I examine the passage in which Wittgenstein introduced aesthetic reasoning, and I articulate some general features of that concept. I also contend that we learn more about aesthetic reasoning by understanding Wittgenstein’s invention of the language-game concept as his reasoning aesthetically “in Philosophy.” Furthermore, I argue that the later Wittgenstein’s notions of aspect perception and grammatical pictures further inform aesthetic reasoning, revealing that it involves the introduction of grammar that can draw a person’s attention to unnoticed aspects of an object and equip him with further descriptions of that object. To illustrate that characterization of aesthetic reasoning, and to offer an example of such reasoning “in Ethics,” I return to Tolstoy’s parable and show that my interacting with it in a particular way involves aesthetic reasoning. Finally, I argue that aesthetic reasoning continues to occur in ethics in that it is woven into discussions of stories in bioethics classes. A student can have her grammatical picture of the case that a story presents reshaped as she sees and accepts aspects of that story that she had not noticed, and this, in turn, might influence her ways of seeing and responding morally to other cases. I close by considering whether aesthetic reasoning occurs in ethics in other ways, and I articulate some implications of my work for further Wittgenstein studies.

This study is an evaluation of the rehabilitative effects of the Cognitive Skills program, an accredited correctional treatment program offered by the Swedish probation service since 1995. Whereas the program has previously been studied in Swedish prison settings, this is the first Swedish study in a non-custodial setting. All 117 male probationer program participants 1995-2000 were closely matched to 349 controls in a survival analysis of long-term recidivism in the sample. Program completers did not show lower relative risk of relapse than the control group, consisting of probationers who did not enter the program. A survival analysis of a violent offender subsample was inconclusive due to sample size but suggests a potential program effect on this type of offender.

¡@Including of HTML, client-side script, and other relative technology, Dynamic HTML (DHTML) is a mechanism of creating dynamic contents in a web page. Nowadays, because of the demand of dynamic web pages and the diffusion of web applications, attackers get a new, easily-spread, and hard-detected intrusion vector ¡Ð DHTML. And commercial anti-virus softwares, commonly using pattern-matching approach, still have weakness against commonly obfuscated malicious DHTML. ¡@According to this condition, we propose a new detective algorithm Model-based Reasoning (MoBR), basing on the respects of model and reasoning, that is resilient to common obfuscations used by attackers and can correctly determine whether a webpage is malicious or not. Through describing text and semantic signatures, we constructs the model of a malicious DHTML by the mechanism of templates. Experimental evaluation by actual DHTML demonstrates that our detection algorithm is tolerant to obfuscation and perform much superior to commercial anti-virus softwares. Furthermore, it can detect variants of malicious DHTML with a low false positive rate.

The problem of handling dependent evidence is an important practical issue for applications of reasoning with uncertainty in artificial intelligence. The existing solutions to the problem are not satisfactory because of their ad hoc nature, complexities, or limitations. In this dissertation, we develop a general framework that can be used for extending the leading uncertainty calculi to allow the combining of dependent evidence. The leading calculi are the Shafer Theory of Evidence and Odds-likelihood-ratio formulation of Bayes Theory. This framework overcomes some of the disadvantages of existing approaches. Dependence among evidence from dependent sources is assigned dependence parameters which weight the shared portion of evidence. This view of dependence leads to a Decomposition-Combination method for combining bodies of dependent evidence. Two algorithms based on this method, one for merging, the other for pooling a sequence of dependent evidence, are developed. An experiment in soybean disease diagnosis is described for demonstrating the correctness and applicability of these methods in a domain of the real world application. As a potential application of these methods, a model of an automatic decision maker for distributed multi-expert systems is proposed. This model is a solution to the difficult problem of non-independence of experts. / Graduation date: 1991

<p>Background: Practice knowledge exists as a complex relationship between questions and answers in a context of meaning that is often intuitive and hidden from the novice practitioner. Physiotherapy education, which aims to develop patterns of thinking, reflection and reasoning as part of practice knowledge, is often based on didactic teaching methods that emphasise the learning of facts without highlighting the relationships between them. In order to improve health outcomes for patients, clinical educators must&nbsp / consider redesigning the curriculum to take into account the changing and complex nature of physiotherapy education. There is some evidence that a blended approach to&nbsp / teaching and learning may facilitate the development of graduates who are more capable of reflection, reasoning and critical thinking, and who can adapt and respond to the&nbsp / complex clinical environment. The purpose of this study was to develop principles that could be used to guide the design of blended learning environments that aim to develop&nbsp / capability in undergraduate physiotherapy students. Method: The study took place in a university physiotherapy department in the Western Cape in South Africa, among&nbsp / undergraduate students. Design research was used as a framework to guide the study, and included a range of research methods as part of that process. The problem was&nbsp / identified using a systematic review of the literature and a survey of students. The design of the blended intervention that aimed to address the problem was informed by a&nbsp / narrative review of theoretical frameworks, two pilot studies that evaluated different aspects of blended learning, and a Delphi study. This process led to the development of a set&nbsp / of design principles which were used to inform the blended intervention, which was implemented and evaluated during 2012. Results: The final results showed that students had undergone a transformation in how they thought about the process and practice of learning as part of physiotherapy education, demonstrating critical approaches towards&nbsp / knowledge, the profession and authority. These changes were brought about by changing teaching and learning practices that were informed by the design principles in the&nbsp / preliminary phases of the project. These principles emphasised the use of technology to interact, articulate understanding, build relationships, embrace complexity, encourage&nbsp / creativity, stimulate reflection, acknowledge emotion, enhance flexibility and immerse students in the learning space. Discussion: While clinical education is a complex undertaking with many challenges, evidence presented in this study demonstrates that the development of clinical reasoning, critical thinking and reflection can be enhanced through the intentional use of technology as part of a blended approach to teaching and learning. The design principles offer clinical educators a framework upon which to construct learning environments where the affordances of technology can be mapped to the principles, which are based on a sound pedagogical foundation. In this way, the use of technology in the learning environment is constructed around principles that are informed by theory. However, clinical educators who are considering the integration of&nbsp / innovative strategies in the curriculum should be aware that students may initially be reluctant to engage in self-directed learning activities, and that resistance from colleagues&nbsp / may obstruct the process. Conclusion: The development of clinical reasoning, critical thinking and reflection in undergraduate physiotherapy students may be enhanced through&nbsp / the intentional use of appropriate technology that aims to fundamentally change teaching and learning practices. Design research offers a practical approach to conducting&nbsp / research in clinical education, leading to the development of principles of learning that are based on theory. <br /> iii</p>

When evaluating a situation that results in harm, it is critical to consider how a person’s prior intention may have been causally responsible for the action that resulted in the harmful outcome. This thesis examined children’s developing understanding of intentional causation in reasoning about harmful outcomes, and the relation between this understanding and mental-state reasoning. Four-, 6-, and 8-year-old children, and adults, were told eight stories in which characters’ actions resulted in harmful outcomes. Story types differed in how the actions that resulted in harm were causally linked to their prior intentions such that: (1) characters wanted to, intended to, and did perform a harmful act; (2) they wanted and intended to perform a harmful act, but instead, accidentally brought about the harmful outcome; (3) they wanted and intended to perform a harmful act, then changed their mind, but accidentally brought about the harmful outcome; (4) they did not want or intend to harm, but accidentally brought about a harmful outcome. Participants were asked to judge the characters’ intentions, make punishment judgments, and justify their responses. Additionally, children were given first- and second-order false-belief tasks, commonly used to assess mental-state reasoning. The results indicated that intention judgment accuracy improved with age. However, all age groups had difficulty evaluating the intention in the deviant causal chain scenario (Searle, 1983), in which the causal link between intention and action was broken but a harmful intention was maintained. Further, the results showed a developmental pattern in children’s punishment judgments based on their understanding of intentional causation, although the adults’ performance did not follow the same pattern. Also, younger children referred to the characters’ intentions less frequently in their justifications of their punishment judgments. The results also revealed a relation between belief-state reasoning and intentional-causation reasoning in scenarios that did not involve, or no longer involved, an intention to harm. Further, reasoning about intentional causation was related to higher-level understanding of mental states. The implications of these findings in clarifying and adding to previous research on the development of understanding of intentional causation and intentions in moral reasoning are discussed.