GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

Good research practices

In this dissertation, entitled “Good Research Practices”, I examine research practices and reform ideas aiming to combat the crisis of confidence in psychology (Pashler & Wagenmakers, 2012). I do so through theoretical contributions and empirical work, propose practical guidelines for researchers, and demonstrate how principles of good research can be conveyed to students. The research methods and statistical practices I present facilitate the adherence to the following three principles: (1) respect the empirical cycle; (2) acknowledge uncertainty; and (3) enrich statistical models with theoretical knowledge

Digital Interaction and Machine Intelligence

This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

Testing the Ability to Apply Mathematical Knowledge

Since the 1960s, the advocacy of teaching mathematics so as to be useful is not without hindrance in school curricula, partly due to the lack of appropriate assessment tools. Practical approaches have been accumulating quickly, but researchers showed that they are not satisfactory in testing students’ ability to apply mathematical knowledge, be they “word problems” in school textbooks, national tests, or large-scale international assessments. To understand the causes behind the dissatisfaction, there is a need to reveal (1) the theories that are used in the test designs, and (2) what the actual assessments are in various curricula. This motive leads to the purpose of the current study, which is to identify empirically consistent theories about students’ ability to apply; the results can be organized as a framework to analyze assessment tools such as PISA, as well as various curricular materials. Based on the current theories, a framework of assessment analysis is created in order to study the coverage of modeling steps of public assessment items. This study finds that, though many education systems have claims of introducing modeling and application into their curricula, high-stake assessments mostly involve a small fraction of the steps that are required in a full modeling cycle. It furthers an earlier result that certain textbooks, though claiming the importance of modeling, almost ignored the first and last steps of modeling. It is found in this study that public assessments are even more limited: most test items that are supposed to test students’ knowledge of application involve only one or two steps of modeling. Furthermore, the tool “modeling spectrum” that is used in the analysis does not only reveal how modeling steps are covered, but can also assists educators to improve or create problems with modeling and application

Spatial subgoal learning in the mouse: behavioral and computational mechanisms

Here we aim to better understand how animals navigate structured environments. The prevailing wisdom is that they can select among two distinct approaches: querying a mental map of the environment or repeating previously successful trajectories to a goal. However, this dichotomy has been built around data from rodents trained to solve mazes, and it is unclear how it applies to more naturalistic scenarios such as self-motivated navigation in open environments with obstacles. In this project, we leveraged instinctive escape behavior in mice to investigate how rodents use a period of exploration to learn about goals and obstacles in an unfamiliar environment. In our most basic assay, mice explore an environment with a shelter and an obstacle for 5-20 minutes and then we present threat stimuli to trigger escapes to shelter. After 5-10 minutes of exploration, mice took inefficient paths to the shelter, often nearly running into the obstacle and then relying on visual and tactile cues to avoid it. Within twenty minutes, however, they spontaneously developed an efficient subgoal strategy, escaping directly to the obstacle edge before heading to the shelter. Mice escaped in this manner even if the obstacle was removed, suggesting that they had memorized a mental map of subgoals. Unlike typical models of map-based planning, however, we found that investigating the obstacle was not important for updating the map. Instead, learning resembled trajectory repetition: mice had to execute `practice runs' toward an obstacle edge in order to memorize subgoals. To test this hypothesis directly, we developed a closed-loop neural manipulation, interrupting spontaneous practice runs by stimulating premotor cortex. This manipulation successfully prevented subgoal learning, whereas several control manipulations did not. We modelled these results using a panel of reinforcement learning approaches and found that mice behavior is best matched by systems that explore in a non-uniform manner and possess a high-level spatial representation of regions in the arena. We conclude that mice use practice runs to learn useful subgoals and integrate them into a hierarchical cognitive map of their surroundings. These results broaden our understanding of the cognitive toolkit that mammals use to acquire spatial knowledge

University catalog, 2016-2017

The catalog is a comprehensive reference for your academic studies. It includes a list of all degree programs offered at MU, including bachelors, masters, specialists, doctorates, minors, certificates, and emphasis areas. It details the university wide requirements, the curricular requirements for each program, and in some cases provides a sample plan of study. The catalog includes a complete listing and description of approved courses. It also provides information on academic policies, contact information for supporting offices, and a complete listing of faculty members. -- Page 3