The growing prevalence of interactions between humans and machines, coupled with the rapid development of intelligent and human-like features in technology, necessitates considering the potential implications that an increasingly inter-personal interaction style might have on human behavior. Particularly, since human–human interactions are fundamentally affected by politeness rules, several researchers are investigating if such social norms have some implications also within human–machine interactions. This paper reviews scientific works dealing with politeness issues within human–machine interactions by considering a variety of artificial intelligence systems, such as smart devices, robots, digital assistants, and self-driving cars. This paper aims to analyze scientific results to answer the questions of why technological devices should behave politely toward humans, but above all, why human beings should be polite toward a technological device. As a result of the analysis, this paper wants to outline future research directions for the design of more effective, socially competent, acceptable, and trustworthy intelligent systems.

The increasing popularity of cruise tourism has led to the need for effective planning and management strategies to enhance the city tour experience for cruise passengers. This paper presents a deep reinforcement learning (DRL)-based planner specifically designed to optimize city tours for cruise passengers. By leveraging the power of DRL, the proposed planner aims to maximize the number of visited attractions while considering constraints such as time availability, attraction capacities, and travel distances. The planner offers an intelligent and personalized approach to city tour planning, enhancing the overall satisfaction of cruise passengers and minimizing the negative impacts on the city’s infrastructure. An experimental evaluation was conducted considering Naples’s fourteen most attractive points of interest. Results show that, with 30 state variables and more than $19\ast {10}^{12}$ possible states to be explored, the DRL-based planner converges to an optimal solution after only 20,000 learning steps.

The paper proposes a new methodological approach for evaluating the comfort condition using the concept of explainable post occupancy to make the user aware of the environmental state in which (s)he works. Such an approach was implemented on a humanoid robot with social capabilities that aims to enforce human engagement to follow recommendations. The humanoid robot helps the user to position the sensors correctly to acquire environmental measures corresponding to the temperature, humidity, noise level, and illuminance. The distribution of the last parameter due to its high variability is also retrieved by the simulation software Dialux. Using the post occupancy evaluation method, the robot also proposes a questionnaire to the user for collecting his/her preferences and sensations. In the end, the robot explains to the user the difference between the suggested values by the technical standards and the real measures comparing the results with his/her preferences and perceptions. Finally, it provides a new classification into four clusters: true positive, true negative, false positive, and false negative. This study shows that the user is able to improve her/his condition based on the explanation given by the robot.

Sepsis is a critical medical condition characterized by a highly variable and rapidly evolving clinical course, often necessitating early intervention and tailored treatment plans to improve patient outcomes. Due to its complexity and heterogeneity, understanding the progression of sepsis across different patient populations remains a significant challenge. In this study, we exploit a sophisticated analytical framework based on k-means multivariate longitudinal clustering to capture the diverse trajectories of sepsis. We do so by analyzing multiple clinical parameters tracked over time, providing a nuanced view of disease progression. By incorporating Dynamic Time Warping (DTW) as the distance metric, the proposed method effectively accounts for temporal misalignments and variability in the rate of disease progression, an essential capability given the unpredictable and heterogeneous nature of sepsis. This integration enhances the model’s ability to detect distinct temporal patterns and phenotypic subgroups that may remain undetected using conventional analytical approaches. By leveraging sepsis-related electronic health records (EHRs), which provide rich time-series data on laboratory results along with patient demographics and underlying health conditions, the proposed method reveals distinct sepsis phenotypes that reflect variations in disease progression. We perform several experiments varying the number of clusters and clinical variable combinations, evaluating the clustering performances using Silhouette score, Caliski-Harabasz Index, and Davies-Bouldin Index, as reference quality metrics. Our results confirm the prognostic role of the Thrombin-Antigen complex and the Prothrombin Time–International Normalized Ratio for septic patients. Furthermore, to evaluate the relevance of subjects’ stratification, the Adjusted Rand Index metric is used to quantify the survival prediction capability of our longitudinal clustering method, considering the 28-day death feature as the target variable. The same metric demonstrates that our proposal outperforms other longitudinal clustering algorithms available in the literature.