Future Trends of Virtual, Augmented Reality, and Games for Health

Serious game is now a multi-billion dollar industry and is still growing steadily in many sectors. As a major subset of serious games, designing and developing Virtual Reality (VR), Augmented Reality (AR), and serious games or adopting off-the-shelf games to support medical education, rehabilitation, or promote health has become a promising frontier in the healthcare sector since 2004, because games technology is inexpensive, widely available, fun and entertaining for people of all ages, with various health conditions and different sensory, motor, and cognitive capabilities. In this chapter, we provide the reader an overview of the book with a perspective of future trends of VR, AR simulation and serious games for healthcare

Utilising Assured Multi-Agent Reinforcement Learning within safety-critical scenarios

Multi-agent reinforcement learning allows a team of agents to learn how to work together to solve complex decision-making problems in a shared environment. However, this learning process utilises stochastic mechanisms, meaning that its use in safety-critical domains can be problematic. To overcome this issue, we propose an Assured Multi-Agent Reinforcement Learning (AMARL) approach that uses a model checking technique called quantitative verification to provide formal guarantees of agent compliance with safety, performance, and other non-functional requirements during and after the reinforcement learning process. We demonstrate the applicability of our AMARL approach in three different patrolling navigation domains in which multi-agent systems must learn to visit key areas by using different types of reinforcement learning algorithms (temporal difference learning, game theory, and direct policy search). Furthermore, we compare the effectiveness of these algorithms when used in combination with and without our approach. Our extensive experiments with both homogeneous and heterogeneous multi-agent systems of different sizes show that the use of AMARL leads to safety requirements being consistently satisfied and to better overall results than standard reinforcement learning

Toward Robotic Socially Believable Behaving Systems Volume I - “Modeling Emotions”

When it comes to modeling emotions, contextual instances cannot be neglected. The concept of context is, to a certain extent, a complex one, since it includes cultural, social, physical, and individual features that shape human interactional exchanges. This second volume accounts for contexts, in particular, social contexts and social signals that must be interpreted to correctly and successfully decode the semantic and emotional meaning of interactional exchanges. To this aim, several experts, from different scientific domains, are describing behaviors to be adopted or interpreted for, as well as mathematical algorithms to model contextual instances and relative information communication technology (ICT) interfaces for developing robotic socially believable applications. In this regard, the volume presents the recent research works on robotics approaching the domestic spheres and recent research efforts for allowing robotic systems of automaton levels of intelligence, where “intelligent” is the system’s ability to implement a natural interaction with human. The implementation of such context-aware situated ICT systems should contribute to improve the quality of life of the end users through: (1) The development of shared digital data repositories and annotation standards for benchmarking; (2) new methods for data processing and data flow coordination through synchronization, temporal organization, and optimization of new encoding features (identified through human behavioral analyses); and (3) computational models synthesizing the human ability to rule individual choices, perception, and actions. The final goal would be to produce machines equipped with human-level automaton intelligence. The editors would like to thank the contributors and the International Scientific Committee of reviewers listed below for their rigorous and invaluable scientific revisions, dedication, and priceless selection process. Thanks are also due to the Springer-Verlag for their excellent support during the development phase of this research book