A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

Teaching by leading and managing learning environments

The aim of this chapter is to promote an understanding of how different environments or settings within which students are asked or required to learn - such as large groups, small groups and laboratory and practice settings – have an impact on how they approach their learning and hence on the design and delivery of teaching. It provides an overview of underpinning principles and concepts before exploring their application in practice. The focus is on face-to-face teaching and learning.

Marginal Activity Access Cost (MAAC): a new indicator for sustainable Land Use/Transport (LUT) planning

The paper presents the ‘Marginal Activity Access Cost’, an accessibility indicator providing estimation in monetary terms of the impacts on mobility and on the environment of locating a single new activity in a specific zone of the urban area. In the first part of this paper, the new indicator is presented and compared to other accessibility indicators proposed in literature. In the second part, the MAAC is validated through an application to the urban area of Rome. The paper concludes with brief remarks on using the proposed accessibility indicator as index of performance for sustainable spatial planning.

Perceptual user interface framework for immersive information retrieval environments (An experimental framework for testing and rapid iteration)

The use of perceptual inputs is an emerging area within HCI that suggests a developing Perceptual User Interface (PUI) that may prove advantageous for those involved in mobile serious games and immersive social network environments. Since there are a large variety of input devices, software platforms, possible interactions, and myriad ways to combine all of the above elements in pursuit of a PUI, we propose in this paper a basic experimental framework that will be able to standardize study of the wide range of interactive applications for testing efficacy in learning or information retrieval and also suggest improvements to emerging PUIs by enabling quick iteration. This rapid iteration will start to define a targeted range of interactions that will be intuitive and comfortable as perceptual inputs, and enhance learning and information retention in comparison to traditional GUI systems. The work focuses on the planning of the technical development of two scenarios, and the first steps in developing a framework to evaluate these and other PUIs for efficacy and pedagogy.