Browsing by Author "Montemanni, Roberto"
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- Artificial intelligence for healthcare and rescuing technology: technical developments and thoughts about employment impactsPublication . Montemanni, Roberto; Guzzi, Jerome; Giusti, AlessandroIntroduction: To evaluate the overall impact of Artificial Intelligence (AI) and Robotics on employment and work organization is complicated by the fact that these technologies are expected to revolutionize many application fields, which are very different from each other. In this paper, we consider two specific applications emerging from recent research projects: one applies AI and Robotics technologies to the healthcare sector, and one to Search and Rescue in wilderness areas. We generalize from these case studies to speculate on how this kind of innovative applications, that are likely to become increasingly common and widespread, might impact employment and work organization in general. Objectives: To understand how innovative applications might impact employment and work organization in general and specifically on healthcare and social services. Methods: Two recent research developments based on the use of Artificial Intelligence (AI) in the fields of healthcare and rescuing, respectively, are discussed. Therefore, our research work and main results have been achieved within a Swiss National Science Foundation project and a simplified view of the innovative classification component of the architecture is presented. Results: AI and Robotics technologies have specific application on healthcare and social services and demand new professional skills to manage those new methods. Conclusions: We conclude that, depending on the application field, a reduction in the workforce required to carry out tasks that will be taken over by automation might be counterbalanced by either a drastic increase in demand (healthcare services), or a shift in the required competences/skills (search and rescue); in both cases, we can expect a positive societal impact, also motivated by an increased standard of service.
- International Conference on Tourism and Social Support TechnologiesPublication . Santos, Fernando Miguel Soares Mamede dos; Café, Afonso Pedro Ribeiro; Carvalho, Ana Branca; Guedes, Anabela; Pereira, Andreia; Oliveira, Ângela; Silva, Carla; Lemos, Carlota; Seabra, Cláudia; Gomes, Cristina Azevedo; Mota, David; Fidalgo, Filipe; Sousa, João; Vidal, João; Pinho, José Carlos; Lousado, José Paulo; Pereira, José; Gambardella, Luca Maria; Pato, Lúcia; Shafik, Mahmoud; Brito, Manuel; Ferrer, María Belén; Martins, Nayra; Dionísio, Nuno; Pinho, Nuno; Santos, Paula; Duarte, Paulo; Rito, Pedro; Rocha, Pedro; Silva, Pedro; Gomes, Raquel; Montemanni, Roberto; Antunes, Sandra; Sotomayor, Silvia Feliu Álvarez de; Chou, Xiaochen
- Monte Carlo sampling for the tourist trip design problemPublication . Chou, Xiaochen; Gambardella, Luca Maria; Montemanni, RobertoIntroduction: The Tourist Trip Design Problem is a variant of a route-planning problem for tourists interested in multiple points of interest. Each point of interest has different availability, and a certain satisfaction score can be achieved when it is visited. Objectives: The objective is to select a subset of points of interests to visit within a given time budget, in such a way that the satisfaction score of the tourist is maximized and the total travel time is minimized. Methods: In our proposed model, the calculation of the availability of a POI is based on the waiting time and / or the weather forecast. However, research shows that most tourists prefer to travel within a crowded and limited area of very attractive POIs for safety reasons and because they feel more in control. Results: In this work we demonstrate that the existing model of the Probabilistic Orienteering Problem fits a probabilistic variant of this problem and that Monte Carlo Sampling techniques can be used inside a heurist solver to efficiently provide solutions. Conclusions: In this work we demonstrate the existing model of the Probabilistic Orienteering Problem fits the stochastic Tourist Trip Design Problem. We proposed a way to solve the problem by using Monte Carlo Sampling techniques inside a heuristic solver and discussed several possible improvements on the model. Further extension of the model will be developed for solving more practical problems.