Giovanni Ferrin

Approaching Social Robots Through Playfulness and Doing-It-Yourself: Children in Action

This work reports on a pilot study devoted to investigate whether the direct experience of building a robot by children enables them to obtain a more effective and complex learning of what a robot is. The study consists of an experiment carried out with eighteen pupils of the same age, attending a secondary school in Udine (Italy). The experiment was aimed to allow children to build up a simple robot, and in this experience, the children were supported by two researchers and by one of their teachers. The results show that this concrete experience activated in the children affective, emotional, physical, and social dimensions and brought them to the development of a more sophisticate conceptualization of robots. The learning by doing approach was quite effective also in strengthening the children’s social behavior and improving their mechanical knowledge and manual abilities.

Children’s Knowledge and Imaginary About Robots

The aim of this paper is to investigate on children’s knowledge and imaginary about robots. To do so, we administered to 704 children from 17 classes of 8 elementary and secondary schools, a survey with close and open questions about their conceptualization of robots. To carry out this study we took as point of reference the theoretical framework of social representations. The main results are that children evaluate toys, robots and human-beings as significantly different on all the characteristics considered. More than toys, robots have mechanical movements, they move, are more intelligent than toys but they do not keep company to them. By contrast, human beings are perceived by children starting from their corporeity: they eat and sleep, move by themselves, are intelligent and speak, keep eye-contact and company. However, children complain about the fact that human beings do not play with them. The imaginary about robots that children receive from media is characterized by anthropomorphic shapes, bodies and by human-like cognitions, feelings and behavior. The more examples of visual products with robots children are able to evoke, the higher they evaluate robots on all human-like characteristics (e.g. it looks into my eyes). Hence, the tension between imaginary and knowledge can be confounding because the human-like features of fictional robots are more advanced than those reachable by the factual ones.

Data fusion and abductive inference for metaphor resolution: a bridging discussion

Since the 1980s, metaphor has been recognized as a pervasively diffused phenomenon in communication, absolutely not restricted to rhetoric and linguistic phenomena, involving structured concepts, relations, and matching ‘rules’. Metaphor resolution, that is metaphor understanding, as well as metaphor creation, has become an issue in automated processing and understanding of natural language as well as of mixed visual communication. It can be showed as a process of structure finding and mapping procedure between conceptual denotation–connotation structures necessary for interpretation. Creative abduction is then showed to be the pattern inference required to work out structure-mappings in corresponding nodes as present in metaphors. In this paper, we review some key issues (definitions, typologies, theoretical problems) involving the concept of ‘metaphor’ and survey some definitions and concepts emerging in contemporary debate on abductive inference. Finally, we argue that metaphor understanding process can be recognized as a fusion tractable problem, allowing the exploitation of frameworks and algorithms of such domain.

Revisiting the role of abductive inference in fusion domain

Abductive inference plays a very important role in the fusion domain, seen as a “conceptual framework” driving the conjecturing and hypothesizing process mainly aimed to discover relationships. Unfortunately, despite the recent gained attention in several fields as philosophy of science and logic, and the ongoing debate, in the fusion domain almost only one kind of abductive inference is taken under consideration with the aim of developing successful tools to be deployed within a fusion process, namely “Inference to the Best Explanation”. The issues faced in the domain of intelligence and information fusion, dealing with the real world, imply processes which follow the sequences of scientific discovery and proof. Therefore a survey of some key concepts around abductive inference in the domain of epistmology can reveal some principles to be directly applied to the Fusion domain.