What is Missing for Traditional Design-Centric Engineering Education to Better Prepare Newly Graduated Engineers for the Global Era?

This paper presents an analysis of the capacity of design centric methodologies to prepare engineering students to succeed in the market. Gaps are brainstormed and analyzed with reference to their importance. Reasons that may lead the newly graduated engineers not to succeed right from the beginning of their professional lives have also been evaluated. A comparison among the two subjects above was prepared, reviewed and analyzed. The influence of multidisciplinary, multicultural and complex environmental influences created in the current global business era is taken into account. The industry requirements in terms of what they expect to 'receive' from their engineers are evaluated and compared to the remaining of the study above. An innovative approach to current engineering education that utilizes traditional design-centric methodologies is then proposed, aggregating new disciplines to supplement the traditional engineering education. The solution encompasses the inclusion of disciplines from Human Sciences and Emotional Intelligence fields willing to better prepare the engineer of tomorrow to work in a multidisciplinary, globalized, complex and team working environment. A pilot implementation of such an approach is reviewed and conclusions are drawn from this educational project.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.