This thesis presents a structural model of one aspect of science, the theory-driven discovery of empirical laws, in terms of the knowledge structures and reasoning processes that it involves; and describes a machine learning system designed to embody the major features of the model, called OZ, which is designed to investigate the transport properties of an unknown membrane separating two solutions. Inductive data-driven discovery is an important process in science, but takes place within very tightly constrained limits defined by theoretical reasoning. An explicit specification of the possible search space for a law is a <i>law framework</i>; this takes the form of a law with some undetermined parameters. Inductive law discovery is the search for the values of these free parameters. According to the model <i>informal qualitative models</i> (IQMs) describing the essential structural features of a physical system are used to guide the selection of appropriate variables for scientific law discovery, and the selection of an appropriate mathematical function for a law. Our analysis differs from previous work in machine discovery in stressing the importance of models of internal structure in scientific discovery. OZ comprises a domain independent control structure and a set of domain independent procedures, plus a set of domain dependent heuristics for the membrane properties domain. It constructs a set of candidate IQMs for the unknown membrane, and designs goal-directed experiments to determine which IQM is the right one, generating and testing qualitative predictions about the patterns to be expected in numerical data. When it has identified a single model as correct, it constructs law frameworks for possible laws describing the transport properties of the membrane, then designs different experiments to gather data to supply to an inductive law discovery function, which looks for a law of the type specified by each law framework.

The role of factors previously implicated as leading to confirmation bias during hypothesis testing was explored. Confirmation bias is a phenomenon in which people select cases for testing when the expected results of the case are more likely to support their current belief than falsify it. Klayman (1995) proposed three primary determinants for confirmation bias. Klayman and his colleagues proposed that a general positive testing strategy leads to the phenomenon of confirmation bias. According to Klayman’s account, participants in previous research were not actively working to support their hypothesis. Rather, they were applying a valid hypothesis testing strategy that works well outside of laboratory tasks. In laboratory tasks, such as Wason’s 2-4-6 task (Wason, 1960), the strategy failed because the nature of the task takes advantage of particular flaws in the positive testing behavior participants learned through their experience with the real-world. Given Klayman’s proposed set of determinants for the positive testing strategy phenomenon, treatments were developed that would directly violate the assumptions supporting application of the positive testing strategy. If participants were able to identify and act on these violations of the assumptions, the number of positive tests was expected to be reduced. The test selection portion of the Mynatt, Doherty, and Tweney (1977) microworld experiment was modified with additional instruction conditions and a new scenario description to investigate the impact of the treatments to reduce confirmation bias in test selection. Despite expectations, the thematic content modifications and determinant-targeting instruction conditions had no effect on participant positive test selection.

Yes / This report addresses the scientific and technological discoveries in the biological sciences that enabled the early interest in biological warfare to move from hurling infected corpses into enemy cities in ancient times, through use of small cultures of animal pathogens to sabotage enemy livestock in World War I, to the origins of organised military biological weapons (BW) programmes directed at humans, animals, and plants in the inter-war period. It builds on Dando¿s 1999 paper: The Impact of the Development of Modern Biology and Medicine on the Evolution of Offensive Biological Warfare Programs in the Twentieth Century.1 For the historical aspects of biological warfare programmes this report primarily draws from the Stockholm International Peace Research Institute volume: Biological and Toxin Weapons: Research, Development and Use from the Middle Ages to 1945.2

The automation of scientific discovery has been an active research topic for many years. The promise of a formalized approach to developing and testing scientific hypotheses has attracted researchers from the sciences, machine learning, and philosophy alike. Leveraging the concept of dynamical symmetries a new paradigm is proposed for the collection of scientific knowledge, and algorithms are presented for the development of EUGENE – an automated scientific discovery tool-set. These algorithms have direct applications in model validation, time series analysis, and system identification. Further, the EUGENE tool-set provides a novel metric of dynamical similarity that would allow a system to be clustered into its dynamical regimes. This dynamical distance is sensitive to the presence of chaos, effective order, and nonlinearity. I discuss the history and background of these algorithms, provide examples of their behavior, and present their use for exploring system dynamics. / Master of Science / Determining why a system exhibits a particular behavior can be a difficult task. Some turn to causal analysis to show what particular variables lead to what outcomes, but this can be time-consuming, requires precise knowledge of the system’s internals, and often abstracts poorly to salient behaviors. Others attempt to build models from the principles of the system, or try to learn models from observations of the system, but these models can miss important interactions between variables, and often have difficulty recreating high-level behaviors. To help scientists understand systems better, an algorithm has been developed that estimates how similar the causes of one system’s behaviors are to the causes of another. This similarity between two systems is called their ”dynamical distance” from each other, and can be used to validate models, detect anomalies in a system, and explore how complex systems work.

The complexity of biological systems constitute a significant problem for the development of biological models. This inspired the creation of a few Computational Scientific Discovery systems that attempt to address this problem in the context of metabolomics through the use of computers and automation. These systems have important limitations, however, like limited revision and experiment design abilities and the inability to revise refuted models. The goal of this project was to address some of these limitations. The system developed for this project, "Huginn", was based on the use of Abductive Logic Programming to automate crucial development tasks, like experiment design, testing consistency of models with experimental results and revision of refuted models. The main questions of this project were (1) whether the proposed system can successfully develop Metabolic Network Models and (2) whether it can do it better than its predecessors. To answer these questions we tested Huginn in a simulated environment. Its task was to relearn the structures of disrupted fragments of a state-of-the-art model of yeast metabolism. The results of the simulations show that Huginn can relearn the structure of metabolic models, and that it can do it better than previous systems thanks to the specific features introduced in it. Furthermore, we show how the design of extended crucial experiments can be automated using Answer Set Programming for the first time.

Made available in DSpace on 2016-04-28T14:16:23Z (GMT). No. of bitstreams: 1 Decio Hermes Cestari Junior.pdf: 1836314 bytes, checksum: e7d63f5b7f7450761733426e00c19a54 (MD5) Previous issue date: 2015-09-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work aims to analyse the concept of discovery in the nineteenth century by exploring the behaviour of scientists and the common people at that time. We have started by studying original documents on X rays published by Wilhelm Conrad Roentgen. We have found that some evidences proving that scientists claim the discoveries to themselves. This claim was important because it led them to achieve an elevated scientific recognition. To develop this work we have considered different scientific approaches in order to understand the behaviour of members of scientific community. Therefore, the fields of philosophy of science and sociology of science were also applied to support some parts of this research. By analysing publications of that time it was possible to understand the concept of scientific discovery among common people during the nineteenth century. In the last part of our research we have analysed the concept of science currently used in popular science books. We could find misconceptions such as trying to explain science from discoveries or describing experiments as if they were crucial, that is, the story of winners. It's possible to note that some of those misconceptions found in the common sense of the nineteenth century can also be found in the current popular science books / Este trabalho tem por objetivo analisar o conceito de descoberta científica no final do século XIX a partir do estudo do comportamento dos cientistas e da sociedade da época. Iniciamos nosso trabalho com a análise dos documentos originais publicados por Wilhelm Conrad Roentgen, nos quais encontramos evidências de que o cientista reivindica prioridade sobre a descoberta. Essa prioridade é importante para que o cientista receba o reconhecimento de seus pares. Para desenvolver este trabalho foi necessário analisar o comportamento dos membros da comunidade científica a partir de diferentes perspectivas, para isso, buscamos referências em outros campos do conhecimento, como a filosofia da ciência e a sociologia da ciência. Através da análise dos periódicos do final do século XIX e início do século XX, foi possível compreender a concepção de descoberta predominante no senso comum da sociedade daquele período. Na parte final deste trabalho analisamos a concepção de descoberta científica utilizada nos livros de divulgação científica atuais. Encontramos abordagens que procuram explicar a ciência a partir das descobertas científicas ou de experimentos considerados definitivos, ou seja, a história dos vencedores. Observamos que é possível encontrar nos atuais livros de divulgação científica concepções de descoberta semelhantes às encontradas no senso comum do século XIX

Dans le cadre de cette recherche, nous posons la problématique suivante : comment représente-t-on la découverte botanique en sciences naturelles ? Notre démarche sera sémiotique et particulièrement fondée sur la théorie greimassienne. Nous la lierons à une recherche sémantique qui s’appuiera notamment sur les travaux de François Rastier. Et parce que la représentation scientifique articule étroitement le texte linguistique au texte visuel, nous engagerons notre étude dans une perspective qui rendra compte du système visuel. Ce dernier sera interrogé à partir des écrits de Maria Giulia Dondero et Jacques Fontanillle, d’Anne Beyaert-Geslin ou encore de Jean-Marie Klinkenberg. A partir de ces fondements théoriques, cet objectif de recherche nous permettra de comprendre les mécanismes à l’œuvre lorsqu’il s’agit de représenter le vivant « non humain ». Le texte sera au cœur de notre recherche. Il supposera que nous l’abordions du point de vue de l’énoncé, mais aussi de l’énonciation. Pour envisager ce second volet, la théorie de l’énonciation d’Emile Benveniste sera convoquée. Parce que nous voulons mettre à l’épreuve la théorie, notre recherche intégrera une dimension empirique à partir d’un projet expérimental de lecture fondé sur la technique de l’eye-tracking (ou oculométrie). Celle-ci se montrera pertinente pour interroger la signification à partir de nos modes d’expression systématisés qui construisent le discours scientifique de la découverte depuis au moins deux siècles. A partir d’un corpus circonscrit issu des publications scientifiques du Muséum national d’histoire naturelle, nous définirons et expliquerons le parcours de la découverte et ses stratégies monstratives. Cela nous mènera à questionner nos modes de pensée. Pensons en effet que la médiation de nos représentations (le paraître) est le reflet d’une dimension ontologique (l’être). Elle fait de nous des instances connaissantes, conscientes et capables d’agir en conséquence. / As part of this research, we ask the following question: how do we represent the botanical discovery in natural sciences? Our approach will be semiotic and particularly based on Greimassian theory, which we will also link to a semantic analysis derived from the works of François Rastier. As scientific representation closely articulates linguistic text and visual text, this study will engage our object with a perspective that takes into account the visual system. The latter will be questioned in particular through the writings of Maria Giulia Dondero, Jacques Fontanillle, Anne Beyaert-Geslin and Jean-Marie Klinkenberg. With this theoretical foundation, our research objective will allow us to understand the mechanisms at work regarding the representation of non-human life-forms. Text will be at the heart of our research. He will suppose that we approach him from the point of view of the utterance, but also of the enunciation. This second part of our research will rely on the theory of the enunciation developped by Emile Benveniste. As our study aims to test the theory, therefore our research will integrate an empirical dimension based on an experimental reading project that uses eye-tracking. This will allow us to question meaning in the systematized modes of expression that have constructed scientific discourse in discovery for at least two centuries. Within a circumscribed corpus from the scientific publications of the National Museum of Natural History, we will define and explain the course of discovery and its monstrative strategies. This will lead us to question our ways of thinking. Let us think that the mediation of our representations (the appearance) is the reflection of an ontological dimension (being). It makes us knowledgeable, conscious and able to act accordingly.