Yes / Regular language inference, initiated by Angluin, has many developments, including applications in software engineering and testing. However, the capability of finite automata to model the system data is quite limited and, in many cases, extended finite state machine formalisms, that combine the system control with data structures, are used instead. The application of Angluin-style inference algorithms to extended state machines would involve constructing a minimal deterministic extended finite state machine consistent with a deterministic 3-valued deterministic finite automaton. In addition to the usual, accepting and rejecting, states of finite automaton, a 3-valued deterministic finite automaton may have “don't care” states; the sequences of inputs that reach such states may be considered as accepted or rejected, as is convenient. The aforementioned construction reduces to finding a minimal deterministic finite automaton consistent with a 3-valued deterministic finite automaton, that preserves the deterministic nature of the extended model that also handles the data structure associated with it. This paper investigates fundamental properties of extended finite state machines in relation to Angluin's language inference problem and provides an inference algorithm for such models.

Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly because they are expected to learn a task from scratch merely through an agent's own experience. In this thesis, we show that learning from scratch is a limiting factor for the learning performance, and that when prior knowledge is available RL agents can learn a task faster. We evaluate relevant previous work and our own algorithms in various experiments. Our first contribution is the first implementation and evaluation of an existing interactive RL algorithm in a real-world domain with a humanoid robot. Interactive RL was evaluated in a simulated domain which motivated us for evaluating its practicality on a robot. Our evaluation shows that guidance reduces learning time, and that its positive effects increase with state space size. A natural follow up question after our first evaluation was, how do some other previous works compare to interactive RL. Our second contribution is an analysis of a user study, where na"ive human teachers demonstrated a real-world object catching with a humanoid robot. We present the first comparison of several previous works in a common real-world domain with a user study. One conclusion of the user study was the high potential of RL despite poor usability due to slow learning rate. As an effort to improve the learning efficiency of RL learners, our third contribution is a novel human-agent knowledge transfer algorithm. Using demonstrations from three teachers with varying expertise in a simulated domain, we show that regardless of the skill level, human demonstrations can improve the asymptotic performance of an RL agent. As an alternative approach for encoding human knowledge in RL, we investigated the use of reward shaping. Our final contributions are Static Inverse Reinforcement Learning Shaping and Dynamic Inverse Reinforcement Learning Shaping algorithms that use human demonstrations for recovering a shaping reward function. Our experiments in simulated domains show that our approach outperforms the state-of-the-art in cumulative reward, learning rate and asymptotic performance. Overall we show that human demonstrators with varying skills can help RL agents to learn tasks more efficiently.

Patient safety has progressed in 15 years from being a relatively insignificant issue to a position high on the agenda for health care providers, managers and policymakers as well as the general public. Sweden has seen increased national, regional and local patient safety efforts since 2011 when a new patient safety law was introduced and a four-year financial incentive plan was launched to encourage county councils to carry out specified measures and meet certain patient safety related criteria. However, little is known about what structures and processes contribute to improved patient safety outcomes and how the context influences the results. The overall aim of this thesis was to generate knowledge for improved understanding and explanation of influences on patient safety in the county councils in Sweden. To address this issue, five studies were con-ducted: interviews with nurses and infection control practitioners, surveys to patient safety officers and a document analysis of patient safety reports. Patient safety officers are healthcare professionals who hold key positions in their county council’s patient safety work. The findings from the studies were structured through a framework based on Donabedian’s triad (with a contextual element added) and applying a learning perspective, highlight areas that are potentially important to improve the patient safety in Swe-dish county councils. Study I showed that the conditions for the county councils’ patient safety work could be improved. Conducting root-cause analysis and attaining an organizational culture that encourages reporting and avoids blame were perceived to be of importance for improving patient safety. Study II showed that nurses perceived facilitators and barriers for improved pa-tient safety at several system levels. Study III revealed many different types of obstacles to effective surveillance of health care-associated infec-tions (HAIs), the majority belonging to the early stages of the surveillance process. Many of the obstacles described by the infection control practi-tioners restricted the use of results in efforts to reduce HAIs. Study IV of the Patient Safety Reports identified 14 different structure elements of patient safety work, 31 process elements and 23 outcome elements. These reports were perceived by patient safety officers to be useful for providing a structure for patient safety work in the county councils, for enhancing the focus on patient safety issues and for learning from the patient safety work that is undertaken. In Study V the patient safety officers rated efforts to reduce the use of antibiotics and improved communication be-tween health care practitioners and patients as most important for attaining current and future levels of patient safety in their county council. The patient safety officers also perceived that the most successful county councils regarding patient safety have good leadership support, a long-term commitment and a functional work organisation for patient safety work. Taken together, the five studies of this thesis demonstrate that patient safety is a multifaceted problem that requires multifaceted solutions. The findings point to an insufficient transition of assembled data and information into action and learning for improved patient safety.

This essay examines Swedish nursery children’s oral vocabulary knowledge in English. The aim is to investigate what they know and find the source to their knowledge. This essay will focus on the receptive and productive knowledge the children know based on the categories tested, how much children learn in their Zone of Proximal Development, how much they learn from context and how much does media and electronic devices influence their learning. The data of this essay were collected from interviews of 25 children and from questionnaires answered by their parents. The results of the study indicate that the children have more receptive knowledge of the English words tested. It also suggests that the productive knowledge they have is connected with the words that sound similar in Swedish and that children learn English from their surroundings such as siblings and parents, and from games on iPads and video clips from YouTube.

Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2018-12-14T11:44:09Z No. of bitstreams: 1 Eliane Teixeira Rennó.pdf: 720998 bytes, checksum: 45ebe62796f539e3bb9a0dcb8640d543 (MD5) / Made available in DSpace on 2018-12-14T11:44:10Z (GMT). No. of bitstreams: 1 Eliane Teixeira Rennó.pdf: 720998 bytes, checksum: 45ebe62796f539e3bb9a0dcb8640d543 (MD5) Previous issue date: 2018-11-30 / Starting from the hypotheses that, from an early age, the individual may have difficulties in relation to the stages of learning, impediments to becoming sufficient in their school learning and presenting difficulties in their academic and/or professional development, this research verified what lies beneath non-learning under a psychoanalytic view. The theoretical-methodological approach used is that of listening research and investigation research (NAFFAH NETO; CINTRA, 2012). Three clinical cases, transcribed with reports and vignettes, and collected from unidentified collaborators served as a basis for seeking answers to the following research questions: Which factors in the initial bond prevent a person form interacting with knowing/learning? What causes a person not to relate well to the pursuit of knowledge? What would the reason for them not to develop in the academic and professional spheres be? The concepts that underlie this study are mainly those of intellectual inhibition (KLEIN, 1931) and those on the primitive aspects of development and also those present in A theory of thinking (BION, 1962c), presenting the concepts of alpha function, reverie, K and (-K) link and learning from experience. In conclusion, this research revealed that learning/knowing difficulties are related to how the baby‘s early experiences in parental bonding occur and that emotional factors significantly influence the way a person interacts whit his/her intellectual development / Partindo das hipóteses de que, desde a mais tenra idade, o indivíduo pode ter dificuldades em relação às etapas do aprender, impedimentos para tornar-se suficiente em seu aprendizado escolar e apresentar dificuldades em seu desenvolvimento acadêmico e/ou profissional, esta pesquisa verificou o que há por trás do não aprender, sob um olhar psicanalítico. O enfoque teórico-metodológico é o da pesquisa-investigação e pesquisa-escuta (NAFFAH NETO; CINTRA, 2012). Três casos clínicos, transcritos com relatos e vinhetas e colhidos de colaboradores não identificados, serviram de base para buscar respostas às seguintes questões de pesquisa: Quais fatores, no vínculo inicial, impedem uma pessoa de interagir com o conhecer/aprender? O que leva uma pessoa a não se relacionar bem com a busca do conhecimento? Qual seria o motivo de não se desenvolverem no âmbito acadêmico e profissional? Os conceitos que fundamentam este estudo são, principalmente, os de inibição intelectual (KLEIN, 1931), os relativos aos aspectos primitivos do desenvolvimento e também os presentes em Uma teoria sobre o pensar (BION, 1962c), a partir dos conceitos de função alfa, rêverie, vínculo K e (-K) e o aprender com a experiência. Concluída, esta pesquisa revelou que as dificuldades do aprender/conhecer estão relacionadas à forma como acontecem as experiências iniciais do bebê no vínculo parental e que os fatores emocionais influenciam significativamente na forma como a pessoa interage com o seu desenvolvimento intelectual

"A number of outstanding problems in robotic motion and manipulation involve tasks where degrees of freedom (DoF), be they part of the robot, an object being manipulated, or the surrounding environment, cannot be accurately controlled by the actuators of the robot alone. Rather, they are also controlled by physical properties or interactions - contact, robot dynamics, actuator behavior - that are influenced by the actuators of the robot. In particular, we focus on two important areas of poorly controlled robotic manipulation: motion planning for deformable objects and in deformable environments; and manipulation with uncertainty. Many everyday tasks we wish robots to perform, such as cooking and cleaning, require the robot to manipulate deformable objects. The limitations of real robotic actuators and sensors result in uncertainty that we must address to reliably perform fine manipulation. Notably, both areas share a common principle: contact, which is usually prohibited in motion planners, is not only sometimes unavoidable, but often necessary to accurately complete the task at hand. We make four contributions that enable robot manipulation in these poorly controlled tasks: First, an efficient discretized representation of elastic deformable objects and cost function that assess a ``cost of deformation' for a specific configuration of a deformable object that enables deformable object manipulation tasks to be performed without physical simulation. Second, a method using active learning and inverse-optimal control to build these discretized representations from expert demonstrations. Third, a motion planner and policy-based execution approach to manipulation with uncertainty which incorporates contact with the environment and compliance of the robot to generate motion policies which are then adapted during execution to reflect actual robot behavior. Fourth, work towards the development of an efficient path quality metric for paths executed with actuation uncertainty that can be used inside a motion planner or trajectory optimizer."

One of the hardest challenges in the field of machine learning is to build agents, such as robotic assistants in homes and hospitals, that can autonomously learn new tasks that they were not pre-programmed to tackle, without the intervention of an engineer. Reinforcement learning (RL) and learning from demonstration (LfD) are popular approaches for task learning, but they are often ineffective in high-dimensional domains unless provided with either a great deal of problem-specific domain information or a carefully crafted representation of the state and dynamics of the world. Unfortunately, autonomous agents trying to learn new tasks usually do not have access to such domain information nor to an appropriate representation. We demonstrate that algorithms that focus, at each moment, on the relevant features of the state space can achieve significant speed-ups over previous reinforcement learning algorithms with respect to the number of state features in complex domains. To do so, we introduce and evaluate a family of attention focus algorithms. We show that these algorithms can reduce the dimensionality of complex domains, creating a compact representation of the state space with which satisficing policies can be learned efficiently. Our approach obtains exponential speed-ups with respect to the number of features considered when compared with table-based learning algorithms and polynomial speed-ups when compared with state-of-the-art function approximation RL algorithms such as LSPI or fitted Q-learning. Our attention focus algorithms are divided in two classes, depending on the source of the focus information they require. Attention focus from human demonstrations infers the features to focus on from a set of demonstrations from human teachers performing the task the agent must learn. We introduce two algorithms within this class. The first one, abstraction from demonstration (AfD), identifies features that can be safely ignored in the whole state space and builds a state-space abstraction where a satisficing policy can be learned efficiently. The second, automatic decomposition and abstraction from demonstration, goes one step further, using the demonstrations to identify a set of subtasks and to find an appropriate abstraction for each subtask found. The other class of algorithms we present, attention focus with a world model, does not require a set of human demonstrations. Instead, it extracts the attention focus information from an object-based model of the world together with the agent experience in performing the task. Within this class, we introduce object-focused Q-learning (OF-Q), at first with an assumption of object independence that is later removed to support domains where objects interact with each other. Finally, we show that both sources of focus information can be combined for further speed-ups.

The environment of knowledge exchange between computation and biology elicits a contemporary approach towards architecture. Computation, as an overarching mode of thinking, instructs the analysis, understanding and reinterpretation of the un-formal structure of natural organizations (such as systematic construct, information flow, and process through time) for architectural form generation. Consequently, the computing theory originates a mind-shift where processes, relations, and dependencies are a major concern for reconsidering and re-comprehending the environment. Besides, computation presents universal modes of thinking and tools for modeling, within which transdisciplinary studies and knowledge interchange between distinct disciplines are flourished. This thesis will discuss architectural form generation through interpreting computation as &ldquo / transcoding&rdquo / and an interface, while nature will be regarded as a &ldquo / model&rdquo / and a source for learning. A case study will be conducted by analyzing cactus plants and their common generative logic in the framework of computation. Consequently, the produced computational model of cactus plants will be scrutinized for probable outcomes, questioning what such a re-interpretation of natural systems may imply for architecture.

Is it probable that a reader can have an empathetic and learning experience of an historical event facilitated through text? Research in neuroscience indicates that the form of a text can trigger mirror neurons, enhancing empathy with the events and characters portrayed and enabling introspective learning through stimulation of the default state network in a reading brain. Narrative elements in historical and fictional literature are analyzed for their potential in facilitating the stimulation of these states. The historical fiction novel Morenga by Uwe Timm is analyzed in order to deduce what a reader neurologically experiences in relation to the text and the historical event portrayed in the novel during the reading process. The probability of the reader experiencing empathy and learning through text so that their perspectives on inter-textual and extra-textual similar events are affected is then developed.

Is it probable that a reader can have an empathetic and learning experience of an historical event facilitated through text? Research in neuroscience indicates that the form of a text can trigger mirror neurons, enhancing empathy with the events and characters portrayed and enabling introspective learning through stimulation of the default state network in a reading brain. Narrative elements in historical and fictional literature are analyzed for their potential in facilitating the stimulation of these states. The historical fiction novel Morenga by Uwe Timm is analyzed in order to deduce what a reader neurologically experiences in relation to the text and the historical event portrayed in the novel during the reading process. The probability of the reader experiencing empathy and learning through text so that their perspectives on inter-textual and extra-textual similar events are affected is then developed.