Boundary interactions: Resolving interdisciplinary collaboration challenges using digitized embodied performances

Little is known about the collaborative learning processes of interdisciplinary teams designing technology-enabled immersive learning systems. In this conceptual paper, we reflect on the role of digitally captured embodied performances as boundary objects within our heterogeneous two-team collective of learning scientists and computer scientists as we design an embodied, animated virtual tutor embedded in a physically immersive mathematics learning system. Beyond just a communicative resource, we demonstrate how these digitized, embodied performances constitute a powerful mode for both inter- and intra-team learning and innovation. Our work illustrates the utility of mobilizing the material conditions of learning

Embodiment and designing learning environments

There is increasing recognition amongst learning sciences researchers of the critical role that the body plays in thinking and reasoning across contexts and across disciplines. This workshop brings ideas of embodied learning and embodied cognition to the design of instructional environments that engage learners in new ways of moving within, and acting upon, the physical world. Using data and artifacts from participants' research and designs as a starting point, this workshop focuses on strategies for how to effectively leverage embodiment in learning activities in both technology and non-technology environments. Methodologies for studying/assessing the body's role in learning are also addressed

Hands on - hands off: on hitting your thumb with a virtual hammer

In a wired world even the most physically embodied craft skills are affected by computer facilitated communication. To consider how different sorts of space – both real and virtual – influence the learning of craft skills this paper presents three types of space – the ‘real’ space of a jewellery workshop, an online ‘wiki’ space for learning how to make a folding knife mediated by face to face interaction and an online discussion group about French Horn making. Some features common to the learning of any craft skill are discussed as well as some current ideas about the influence of networked communication on the way people relate to each other. Conclusions are drawn about the relationships between different types of learner, different types of skill and different types of learning space which demonstrate that while there may be no substitute for face to face contact in learning the most embodied craft skills, even in real-world settings a significant proportion of learning depends on social interaction which may be reproduced online. Keywords: Craft learning; Apprenticeship; Communities of Practice; Online Networks</p

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Learning, Capital-Embodied Technology and Aggregate Fluctuations

Recent evidence suggests that agents’ expectations may have played a role in several cycli¬cal episodes such as the U.S. "new economy" boom in the late 1990s, the real estate boom in Japan in the 1980s and the real estate boom in the U.S. which ended in 2008. One chal¬lenge in the expectations driven view of fluctuations has been to develop simple one sector models that can give rise to such fluctuations without a compromise on other dimensions. In this paper we propose a simple generalization of the Greenwood et al. (1988) one sec¬tor model and show it can generate fluctuations that arise as a result of agents difficulty to forecast productivity embodied in new capital. The two key assumptions in the model are: (1) the vintage view of capital productivity, whereby each successive vintage has (po¬tentially) different productivity and (2) agents’ imperfect information and learning about this productivity. The model is consistent with second and third moments from U.S. data. Simulations of the model suggest that, (a) noise amplifies fluctuations and (b) pure noise can trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession.News shocks, expectations, growth asymmetry, Bayesian learning, business cy¬cles.

Learning, capital-embodied technology and aggregate fluctuations

Business cycles in the U.S. and G-7 economies are asymmetric: recoveries and expansions tend to be long and gradual and busts tend to be short and sharp. Moreover, this type of asymmetry appears more pronounced in the last two cyclical episodes in the G-7. A large body of work views the last two cyclical U.S. episodes, namely, the``new economy" boom in the late 1990s, and the 2000s housing boom-bust as episodes where over-optimistic beliefs have played a significant role. These episodes have revived interest in expectations driven business cycles models. However, previous work in this area has not addressed the important asymmetry feature of business cycles. This paper takes a step towards addressing this limitation of expectations driven business cycle models. We propose a generalization of the Greenwood et al. (1988) model with vintage capital and learning about capital embodied productivity and show it can deliver fluctuations that are asymmetric as in the U.S. data. Learning, calibrated to match the procyclical forecast precision from the Survey of Professional Forecasters, is crucial for the model's ability to generate asymmetries. Forecast errors generated by the model are shown to: (a) amplify fluctuations, and (b) trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession.News shocks, expectations, growth asymmetry, Bayesian learning, business cycles

Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation

This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search

When Economic Growth is Less than Exponential

This paper argues that growth theory needs a more general notion of “regularity” than that of exponential growth. We suggest that paths along which the rate of decline of the growth rate is proportional to the growth rate itself deserve attention. This opens up for considering a richer set of parameter combinations than in standard growth models. And it avoids the usual oversimplistic dichotomy of either exponential growth or stagnation. Allowing zero population growth in three different growth models (the Jones R&D-based model, a learning-by-doing model, and an embodied technical change model) serve as illustrations that a continuum of “regular” growth processes fill the whole range between exponential growth and complete stagnation.quasi-arithmetic growth; regular growth; semi-endogenous growth; knife-edge restrictions; learning by doing; embodied technical change

A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits

Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents’ training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans