Pepper4Museum: Towards a Human-like Museum Guide

With the recent advances in technology, new ways to engage visitors in a museum have been proposed. Relevant examples range from the simple use of mobile apps and interactive displays to virtual and augmented reality settings. Recently social robots have been used as a solution to engage visitors in museum tours, due to their ability to interact with humans naturally and familiarly. In this paper, we present our preliminary work on the use of a social robot, Pepper in this case, as an innovative approach to engaging people during museum visiting tours. To this aim, we endowed Pepper with a vision module that allows it to perceive the visitor and the artwork he is looking at, as well as estimating his age and gender. These data are used to provide the visitor with recommendations about artworks the user might like to see during the visit. We tested the proposed approach in our research lab and preliminary experiments show its feasibility

PeppeRecycle: Improving Children’s Attitude Toward Recycling by Playing with a Social Robot

In this paper, we investigate the use of a social robot as an engaging interface of a serious game intended to make children more aware and well disposed towards waste recycle. The game has been designed as a competition between the robot Pepper and a child. During the game, the robot simultaneously challenges and teaches the child how to recycle waste materials. To endow the robot with the capability to play as a game opponent in a real-world context, it is equipped with an image recognition module based on a Convolutional Neural Network to detect and classify the waste material as a child would do, i.e. by simply looking at it. A formal experiment involving 51 primary school students is carried out to evaluate the effectiveness of the game in terms of different factors such as the interaction with the robot, the users’ cognitive and affective dimensions towards ecological sustainability, and the propensity to recycle. The obtained results are encouraging and draw promising scenarios for educational robotics in changing children’s attitudes toward recycling. Indeed Pepper turns out to be positively evaluated by children as a trustful and believable companion and this allows children to be concentrated on the “memorization” task during the game. Moreover, the use of real objects as waste items during the game turns out to be a successful approach not only for perceived learning effectiveness but also for the children’s engagement

Introducing Reactivity in Adaptive Hypertext Generation

Interaction with an adaptive hypertext can be seen as a form of "goal-oriented" dialogue, where the user asks for information through a set of predefined queries and the system answers by ensuring that the global communicative goal of the information process is achieved through a sequence of dialogue sections (hypermedia nodes). This is a planning problem, in which the system tries to develop a coherent dialogue with the user, based on assumptions in the user model that are refined through link selection. Therefore, planning has to be reactive in order to relate link selection to activation of a process that allows generating and executing a "relevant" plan for that particular interaction context. In this paper I briefly describe an approach to dynamic hypermedia planning where these issues are taken into account. Submission to other conferences None Eligibility criteria I'm a third-year Ph.D student in AI in Medicine. Author address Berardina De Carolis Dipartimento di Inf..

Generating Mixed-Initiative Hypertexts: a Reactive Approach

Interaction with an adaptive hypertext can be seen as a form of “goal-oriented ” dialogue, where the user asks for information through a set of predetined queries and the system answers ensuring that the global communicative goal of the information process is achieved through a sequence of dialogue sections (hypermedia nodes). The system establishes what to say to the user at every turn of the dialogue based on the user model settings and on the interaction history. Planning on demmd the information content of a hypertext node that responds to a particular link selection in a particular context requires a ‘reactive’ approach; this differs from common hypertext planning in that it applies local adjustment criteria to an overall plan and, in mixed-initiative situations, tries to tit together the system’s and the user’s points of view

Adapting News and Advertisements to Groups

This chapter deals with adaptation of background information and ­advertisements, displayed in an environment, to the interests of the group of people present. According to research on computational advertising, it is important to develop methods for finding the “best match” between user interests in a given context and available advertisements. Accordingly, after providing an overview of the most popular group recommender approaches, this chapter looks at new issues that arise when considering group modeling in pervasive advertising conveyed through digital displays. The chapter first discusses general issues concerning group recommender systems, with particular emphasis on the acquisition of user preferences and interests. A system called GAIN (Group Adaptive Information and News) is then presented. This was developed with the aim of tailoring the display of background information and advertisements to groups of people