Breadth and Depth of Knowledge in Expert versus Novice Athletes

Questions about knowledge in expert sport are not only of applied significance: they also take us to the heart of foundational and heavily-disputed issues in the cognitive sciences. To a first (rough and far from uncontroversial) approximation, we can think of expert ‘knowledge’ as whatever it is that grounds or is applied in (more or less) effective decision-making, especially when in a competitive situation a performer follows one course of action out of a range of possibilities. In these research areas, studies of motor expertise have for many years actively contributed to broader debates in philosophy and psychology (Abernethy, Burgess-Limerick, & Parks, 1994; Williams, Davids, & Williams, 1999). When we navigate the world flexibly and more or less successfully, how much is this due to a capacity to represent it? In considering alternative options, or planning future actions, we seem to transcend our present environment in some way: what is the balance or relation here between highly-tuned bodily dispositions and background knowledge of the world and its patterns? What changes in these regards as we gain experience and adapt to more complex and challenging environments? Is know-how fundamentally different in kind from ordinary factual knowledge of the world, or knowledge-that? And if expertise in a domain does involve or depend on a knowledge base that is somehow more organized or deeper than that of novices, how is this knowledge selectively and appropriately deployed, often under severe time constraints

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Standard model of the mind and perceptual control theory : a theoretical comparison between two layouts for cognitive architectures

Research on artificial intelligence (AI) has often focused on techniques for implementing specialized aspects of intelligence without regard for the full picture of intelligence and cognition. However, a need for more general, human-like AI systems has also been recognized. In this thesis, our aim was to study potential starting points for a comprehensive, general and truly human-like cognitive architecture. We performed a comparative theoretical analysis on two existing layouts, the standard model of the mind and perceptual control theory (PCT), based on functional criteria gathered from literature. While our results indicate that the PCT model is more comprehensive than the standard model on a theoretical level, finding out the true benefits and challenges of the models and their suitability as foundations for human-like AI systems requires practical evaluation. Additional research is needed to fill current gaps in both models, create their computational implementations, and design practical evaluation methods suitable for them

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Situating Vocational Learning and Teaching Using Digital Technologies - A Mapping Review of Current Research Literature

Context: The ongoing change of work life by digital technologies requires vocational education and training (VET) to adapt constantly. This "digital transformation" of work life gives therefore rise to the question how to advance the use of digital technologies in VET. A possible answer may be found by considering that VET should be transferable to work life. This goal may be achieved by coupling educational activities with examples of work situations. Such situated education may be accomplished by using digital technologies. Until five years ago this mainly consisted in using digital photos, videos, and the internet for educational scaffolding or learning tasks. In research this situated digital VET taxonomy is currently expanding. Hence, the use of digital technologies in VET may be advanced by considering current research literature on situated digital VET.Method: Here, we have searched and reviewed scientific publications on situated digital VET published in the past five years. In the peer-reviewed publications that we had selected, we first identified which digital technologies were used for situated VET and which educational activities were coupled with work situation examples. Subsequently, we identified the categories to which the publications could be grouped together by analyzing the content of their full texts. Results: Situated digital VET was accomplished in about half of the reviewed publications by a digital video on a work situation, and in almost half of the publications by a work situation presented in a 3D virtual environment. Digital videos on work situations mostly served all types of learning tasks and rather rarely educational scaffolding. Work situations presented in 3D virtual environments mostly served cognitive or behavioral learning tasks and never educational scaffolding. Situated digital VET was moreover accomplished by using the digital representation of a work situation that either had occurred previously or that was immediately taking place. Conclusions: Our findings suggest that retrospectively and immediately situated digital VET may be the two categories of an up-to-date basic taxonomy of situated digital VET. Hence, an important question to investigate for advancing the use of digital technologies in VET is the following: Which of the two identified types of situated digital VET can facilitate which kind of vocational learning? Based on the reviewed publications we are not able to give any answers to this. Hence, there is a massive need to investigate which kind of vocational learning can be facilitated by retrospectively, and which by immediately situated digital VET.