203 research outputs found

    Limited Information Shared Control and its Applications to Large Vehicle Manipulators

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    Diese Dissertation beschäftigt sich mit der kooperativen Regelung einer mobilen Arbeitsmaschine, welche aus einem Nutzfahrzeug und einem oder mehreren hydraulischen Manipulatoren besteht. Solche Maschinen werden für Aufgaben in der Straßenunterhaltungsaufgaben eingesetzt. Die Arbeitsumgebung des Manipulators ist unstrukturiert, was die Bestimmung einer Referenztrajektorie erschwert oder unmöglich macht. Deshalb wird in dieser Arbeit ein Ansatz vorgeschlagen, welcher nur das Fahrzeug automatisiert, während der menschliche Bediener ein Teil des Systems bleibt und den Manipulator steuert. Eine solche Teilautomatisierung des Gesamtsystems führt zu einer speziellen Klasse von Mensch-Maschine-Interaktionen, welche in der Literatur noch nicht untersucht wurde: Eine kooperative Regelung zwischen zwei Teilsystemen, bei der die Automatisierung keine Informationen von dem vom Menschen gesteuerten Teilsystem hat. Deswegen wird in dieser Arbeit ein systematischer Ansatz der kooperativen Regelung mit begrenzter Information vorgestellt, der den menschlichen Bediener unterstützen kann, ohne die Referenzen oder die Systemzustände des Manipulators zu messen. Außerdem wird ein systematisches Entwurfskonzept für die kooperative Regelung mit begrenzter Information vorgestellt. Für diese Entwurfsmethode werden zwei neue Unterklassen der sogenannten Potenzialspiele eingeführt, die eine systematische Berechnung der Parameter der entwickelten kooperativen Regelung ohne manuelle Abstimmung ermöglichen. Schließlich wird das entwickelte Konzept der kooperativen Regelung am Beispiel einer großen mobilen Arbeitsmaschine angewandt, um seine Vorteile zu ermitteln und zu bewerten. Nach der Analyse in Simulationen wird die praktische Anwendbarkeit der Methode in drei Experimenten mit menschlichen Probanden an einem Simulator untersucht. Die Ergebnisse zeigen die Überlegenheit des entwickelten kooperativen Regelungskonzepts gegenüber der manuellen Steuerung und der nicht-kooperativen Steuerung hinsichtlich sowohl der objektiven Performanz als auch der subjektiven Bewertung der Probanden. Somit zeigt diese Dissertation, dass die kooperative Regelung mobiler Arbeitsmaschinen mit den entwickelten theoretischen Konzepten sowohl hilfreich als auch praktisch anwendbar ist

    Code Beats - Teaching Computer Programming Coding via Hip Hop Beats

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    Computer programming is a crucial skill for future professionals, not only those working as computer programmers but most modern workers. To train the next generation, society has created many initiatives to introduce computer programming to young students. These initiatives range from classes in a formal academic setting to informal, extracurricular sessions in after-school and summer camps. Even with the increasing offer of these initiatives to expand the opportunities to learn computer programming, the interest in computer programming remains low, especially among populations underrepresented in computing. This lack of interest could be impacted by stereotypical views of computer programming as tedious and difficult to learn and the idea that programming is only for those with a geek gene. Therefore, introducing students to a different side of computer programming, such as its ability to make high-quality music in connection to the use of culturally relevant pedagogy, may be an essential tool in changing students’ perceptions of this field. To investigate this, this dissertation describes and evaluates my approach that introduces the foundational concepts of computer programming using music. First, it investigates prior work that has used music to teach programming. Next, it describes my approach and curriculum design, which combines programming with hip-hop music. Then, it analyzes my approach\u27s impact on attracting and engaging students in several contexts. Finally, it demonstrates how pedagogical approaches commonly used in computer science education can be adapted to this musical context without losing effectiveness. The results indicate that my approach attracts, motivates, and engages students in computer science, a promising step in the effort to broaden the appeal of computer science to increase diversity

    "Teach AI How to Code": Using Large Language Models as Teachable Agents for Programming Education

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    This work investigates large language models (LLMs) as teachable agents for learning by teaching (LBT). LBT with teachable agents helps learners identify their knowledge gaps and discover new knowledge. However, teachable agents require expensive programming of subject-specific knowledge. While LLMs as teachable agents can reduce the cost, LLMs' over-competence as tutees discourages learners from teaching. We propose a prompting pipeline that restrains LLMs' competence and makes them initiate "why" and "how" questions for effective knowledge-building. We combined these techniques into TeachYou, an LBT environment for algorithm learning, and AlgoBo, an LLM-based tutee chatbot that can simulate misconceptions and unawareness prescribed in its knowledge state. Our technical evaluation confirmed that our prompting pipeline can effectively configure AlgoBo's problem-solving performance. Through a between-subject study with 40 algorithm novices, we also observed that AlgoBo's questions led to knowledge-dense conversations (effect size=0.73). Lastly, we discuss design implications, cost-efficiency, and personalization of LLM-based teachable agents

    Proof-theoretic Semantics for Intuitionistic Multiplicative Linear Logic

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    This work is the first exploration of proof-theoretic semantics for a substructural logic. It focuses on the base-extension semantics (B-eS) for intuitionistic multiplicative linear logic (IMLL). The starting point is a review of Sandqvist’s B-eS for intuitionistic propositional logic (IPL), for which we propose an alternative treatment of conjunction that takes the form of the generalized elimination rule for the connective. The resulting semantics is shown to be sound and complete. This motivates our main contribution, a B-eS for IMLL , in which the definitions of the logical constants all take the form of their elimination rule and for which soundness and completeness are established

    How Supervisors Describe the Development of Competence in Trainee School Counselors

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    AbstractSchool counselors serve an important role in providing frontline counseling services to students in local school districts. Professional associations and educational institutions emphasize the importance of professional competence. However, research on professional competence is fragmented, outdated, and unintegrated. This qualitative study explored the development of competence in trainee school counselors during supervision, from the supervisors’ perspectives. Using the conceptual frameworks of self-efficacy and professional identity development, 16 experienced supervisors were interviewed and asked to describe the development of competence in trainee school counselors and how they instructed the development of competence. Thorne’s interpretive description method was combined with a reality-testing framework to guide the research procedures and data analysis plan. Following an inductive process, 663 codes were distilled into 37 themes, which resulted in 17 assertions. It was found that competence was gained across a continuum of growth and was recognized as either self-confidence or self-initiative development; and that supervisors could cultivate self-efficacy through the acquisition of skills and professional identity along with the development of values and beliefs. Recommendations for further research included using the Delphi method and participatory action research to build consensus statements and guidelines. Sharing these findings can inform positive social change by supporting supervisors as they prepare school counselor trainees for a successful career, in turn improving the social emotional development of their students in their local districts

    Contelog: A Formal Declarative Framework for Contextual Knowledge Representation and Reasoning

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    Context-awareness is at the core of providing timely adaptations in safety-critical secure applications of pervasive computing and Artificial Intelligence (AI) domains. In the current AI and application context-aware frameworks, the distinction between knowledge and context are blurred and not formally integrated. As a result, adaptation behaviors based on contextual reasoning cannot be formally derived and reasoned about. Also, in many smart systems such as automated manufacturing, decision making, and healthcare, it is essential for context-awareness units to synchronize with contextual reasoning modules to derive new knowledge in order to adapt, alert, and predict. A rigorous formalism is therefore essential to (1) represent contextual domain knowledge as well as application rules, and (2) efficiently and effectively reason to draw contextual conclusions. This thesis is a contribution in this direction. The thesis introduces first a formal context representation and a context calculus used to build context models for applications. Then, it introduces query processing and optimization techniques to perform context-based reasoning. The formal framework that achieves these two tasks is called Contelog Framework, obtained by a conservative extension of the syntax and semantics of Datalog. It models contextual knowledge and infers new knowledge. In its design, contextual knowledge and contextual reasoning are loosely coupled, and hence contextual knowledge is reusable on its own. The significance is that by fixing the contextual knowledge, rules in the program and/or query may be changed. Contelog provides a theory of context, in a way that is independent of the application logic rules. The context calculus developed in this thesis allows exporting knowledge inferred in one context to be used in another context. Following the idea of Magic sets from Datalog, Magic Contexts together with query rewriting algorithms are introduced to optimize bottom-up query evaluation of Contelog programs. A Book of Examples has been compiled for Contelog, and these examples are implemented to showcase a proof of concept for the generality, expressiveness, and rigor of the proposed Contelog framework. A variety of experiments that compare the performance of Contelog with earlier Datalog implementations reveal a significant improvement and bring out practical merits of current stage of Contelog and its potential for future extensions in context representation and reasoning of emerging applications of context-aware computing

    Graphical scaffolding for the learning of data wrangling APIs

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    In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction

    Nature-Inspired Inductive Biases in Learning Robots

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    Die in dieser Dissertation vorgestellten Arbeiten studieren verschiedene von der Natur inspirierte induktive Verzerrungen im Kontext von modellfreiem und modellbasiertem selbstverstärkenden Lernen, mit dem Ziel, KI Agenten zu entwerfen, die effizient und autonom in der realen Welt handeln. Dabei sind von Robotern zu bewältigende Objektmanipulationsaufgaben von besonderem Interesse, da die zeitliche Entwicklung dieser dynamischen Systeme nicht trivial ist und Manipulationsaufgaben schwierige Planungsprobleme darstellen. Die betrachteten induktiven Verzerrungen sind hauptsächlich von in der Natur zu findenden intelligenten Agenten, wie Tiere und Menschen, inspiriert. Die primären Inspirationsquellen sind wie folgt. (1) Hierarchisch organisierte und spezialisierte kortikale Strukturen, die die effektive Erlernung von Fähigkeiten unterstützen. (2) Das selbstorganisierte Spielen von Kindern zum Zwecke der Formung intuitiver Modelle und Theorien über die Welt. (3) Strukturierte Explorationsstrategien basierend auf unterschiedliche Formen von intrinsischer Motivation und lang anhaltender zeitlicher Korrelationen in motorischen Befehlen. (4) Imitationslernen. (5) Die Planung von Aktionssequenzen unter der Berücksichtigung von Unsicherheiten in mentalen Modellen der nichtdeterministischen Welt. Diese Arbeit ist die Fortsetzung einer langen Historie von Ideen und Forschungsbemühungen, die Inspiration aus der Natur ziehen, um kompetentere KI Agenten zu entwickeln. Die Bemühungen in diesen Forschungsfeldern mündeten in der Ausbildung verschiedener Forschungsfelder wie hierarchisches selbstverstärkendes Lernen, Entwicklungsrobotik, intrinsisch motiviertes selbstverstärkendes Lernen und Repräsentationslernen. Diese Arbeit baut auf den in diesen Feldern entwickelten Ideen und Konzepten auf und kombiniert diese mit Methoden von modellfreiem und modellbasiertem selbstverstärkenden Lernen, um es Robotern zu ermöglichen, herausfordernde Objektmanipulationsaufgaben von Grund auf zu lösen. Die Hypothese, dass von der Natur inspirierte induktive Verzerrungen einen essenziellen Beitrag zur Erschaffung kompetenterer KI Agenten liefern könnten, wird dabei durch zahlreiche empirische Studien unterstützt.The work presented in this thesis studies various nature-inspired inductive biases in the domain of model-free and model-based reinforcement learning with the goal of designing AI agents that act more efficiently and autonomously in natural environments. The domain of robotic manipulation tasks is particularly interesting as it involves non-trivial system dynamics and requires abundant planning and reasoning. The inductive biases under investigation are primarily inspired by intelligent agents found in nature, such as humans and other animals. The primary sources of inspiration are as follows. (1) Hierarchically organized and specialized cortical structures facilitating efficient skills learning. (2) The self-organized playing of children to form intuitive theories and models about the world. (3) Structured exploration strategies based on various forms of intrinsic motivation and long-lasting temporal correlations in motor commands. (4) Imitation Learning. (5) Uncertainty-aware planning of motor commands in imagined models of a non-deterministic world. Consequently, this work continues a long history of ideas and research efforts that take inspiration from nature to build more competent AI agents. These efforts culminated in research fields such as hierarchical reinforcement learning, developmental robotics, intrinsically motivated reinforcement learning, and representation learning. This work builds on the ideas that were advanced in these fields. It combines them with model-free and model-based reinforcement learning methods to solve challenging robotic manipulation tasks from scratch. Empirical studies are carried out to support the hypothesis that nature-inspired inductive biases might be an essential building block in designing more competent AI agents

    Automated Reasoning

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    This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book
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