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

Extending, broadening and rethinking existing research on transfer of training

Research on transfer of training has a long history, with thousands of empirical studies since the 1950s investigating whether, and under which conditions, knowledge and skills acquired during training are subsequently used in the work environment (see reviews by Baldwin and Ford, 1988, Blume et al., 2010 and Burke and Hutchins, 2007). The generation of such an abundance of research can be linked to organisations’ fundamental and ongoing concern to ensure that their employees possess the necessary knowledge and skills from their employer to maintain a competitive advantage and thrive economically. Training and development is, however, extremely costly to organisations, which has created the need to determine the effectiveness of training, and the conditions under which transfer of training is optimal. A recent overview of “what really matters” for successful transfer of training (Grossman & Salas, 2011), aimed at a training and development readership, summarized the most influential variables emerging from this vast body of research. Based on the expectation that the list of factors which may contribute to influence transfer could always be extended and that it would be impractical to incorporate every single factor in research designs, the authors recommended a shift in future research towards deeper investigations of the conditions under which selected variables are more or less influential in their relationship with training. This Special Issue contributes to this important research agenda and extends it further through the inclusion of a diverse collection of conceptual contributions and reviews, from several scientific disciplines, a plurality of theoretical perspectives and a range of methodological approaches. Expanding the theoretical grounding underpinning empirical work on transfer of training and scrutinizing existing conceptualizations of the notion of transfer is timely in light of widespread concerns from organisations about minimal return on investment in training, and repeated evidence in the transfer of training literature of an enduring “transfer problem”. The aim of this article is to explore the value of extending, broadening and rethinking existing research on transfer of training. The benefits of extending research on transfer of training is considered first, through examining how the contributions of this Special Issue add to the existing literature on transfer of training, and the implications of the new insights for addressing the “transfer problem”. How transfer of training research could be broadened, thus enriched, through incorporating ideas from recent literature on transfer of learning is considered next. Finally, proposals to rethink transfer as boundary crossing from an activity theory perspective are scrutinized for their potential to better understand the learning that takes place at the boundaries of training and work environments. The article concludes by elaborating on the conceptual value of a refocus on ‘transfer of learning from training’ within a perspective of adaptive learning, and a call for cross-fertilisation with the extensive theory grounded literatures on transfer of learning and boundary crossing

Recurrent backpropagation and the dynamical approach to adaptive neural computation

Error backpropagation in feedforward neural network models is a popular learning algorithm that has its roots in nonlinear estimation and optimization. It is being used routinely to calculate error gradients in nonlinear systems with hundreds of thousands of parameters. However, the classical architecture for backpropagation has severe restrictions. The extension of backpropagation to networks with recurrent connections will be reviewed. It is now possible to efficiently compute the error gradients for networks that have temporal dynamics, which opens applications to a host of problems in systems identification and control

Special issue: Innovations in farming systems approaches: Introduction

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Engineering ambient visual sensors

Visual sensors are an indispensable prerequisite for those AmI environments that require a surveillance component. One practical issue concerns maximizing the operational longevity of such sensors as the operational lifetime of an AmI environment itself is dependent on that of its constituent components. In this paper, the intelligent agent paradigm is considered as a basis for managing a camera collective such that the conflicting demands of power usage optimization and system performance are reconciled