Design Fiction Diegetic Prototyping: A Research Framework for Visualizing Service Innovations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose: This paper presents a design fiction diegetic prototyping methodology and research framework for investigating service innovations that reflect future uses of new and emerging technologies. Design/methodology/approach: Drawing on speculative fiction, we propose a methodology that positions service innovations within a six-stage research development framework. We begin by reviewing and critiquing designerly approaches that have traditionally been associated with service innovations and futures literature. In presenting our framework, we provide an example of its application to the Internet of Things (IoT), illustrating the central tenets proposed and key issues identified. Findings: The research framework advances a methodology for visualizing future experiential service innovations, considering how realism may be integrated into a designerly approach. Research limitations/implications: Design fiction diegetic prototyping enables researchers to express a range of ‘what if’ or ‘what can it be’ research questions within service innovation contexts. However, the process encompasses degrees of subjectivity and relies on knowledge, judgment and projection. Practical implications: The paper presents an approach to devising future service scenarios incorporating new and emergent technologies in service contexts. The proposed framework may be used as part of a range of research designs, including qualitative, quantitative and mixed method investigations. Originality: Operationalizing an approach that generates and visualizes service futures from an experiential perspective contributes to the advancement of techniques that enables the exploration of new possibilities for service innovation research

An educational game to teach children about air quality using augmented reality and tangible interaction with sensors

Air pollution is known to be one of the main causes of injuries to the respiratory system and even premature death. Gases, particles, and biological compounds affect not only the air we breathe outdoors, but also indoors. Children are highly affected by the poor quality of the air they breathe because their organs and immune systems are still in the developmental stages. To contribute to raising children’s awareness to these concerns, this article presents the design, implementation, and experimental validation of an serious augmented reality game for children to playfully learn about air quality by interacting with physical sensor nodes. The game presents visual representations of the pollutants measured by the sensor node, rendering tangible the invisible. Causal knowledge is elicited by stimulating the children to expose real-life objects (e.g., candles) to the sensor node. The playful experience is amplified by letting children play in pairs. The game was evaluated using the Wizard of Oz method in a sample of 27 children aged between 7 and 11 years. The results show that the proposed game, in addition to improving children’s knowledge about indoor air pollution, is also perceived by them as easy to use and a useful learning tool that they would like to continue using, even in other educational contexts.info:eu-repo/semantics/publishedVersio

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/

IMPLEMENTATION OF A LOCALIZATION-ORIENTED HRI FOR WALKING ROBOTS IN THE ROBOCUP ENVIRONMENT

This paper presents the design and implementation of a human–robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human–robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularized software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information. </jats:p

Our ten years of work on transparet box business simulation

Traditional business games are of the so-called black-box type (BBBS=Black box business simulator); that is to say, the internal structure which generates the results of the simulation after decision-making is not known. As a result, the player normally operates by trial and error and bases his decisions on the symptoms of the problem (the observed behaviors of the system's variables) and not on the real causes of the problem (the system's structure). Since 1988 José A.D. Machuca has insisted that the business games based on System Dynamics models should be Transparent-box business simulators (TBBSs). That means that, during the game, the user has access to the structure of the underlying model and is able to relate it to the observed behaviors. The hypothesis is that such transparency would facilitate causal reflection and favor systemic learning of business problems. In 1990, the G.I.D.E.A.O. Research Group took action on this idea and centered one of its lines of research on this matter, with three main objectives: a) Creation of TBBSs, b) Introduction of TBBSs in undergraduate and graduate Management courses as well as in executive training, c) Experimentation in controlled environments in order to test the hypothesis mentioned in the above paragraph. Now, ten years after the birth of the idea, we would like to share in this paper the results obtained during that period

The matrix revisited: A critical assessment of virtual reality technologies for modeling, simulation, and training

A convergence of affordable hardware, current events, and decades of research have advanced virtual reality (VR) from the research lab into the commercial marketplace. Since its inception in the 1960s, and over the next three decades, the technology was portrayed as a rarely used, high-end novelty for special applications. Despite the high cost, applications have expanded into defense, education, manufacturing, and medicine. The promise of VR for entertainment arose in the early 1990\u27s and by 2016 several consumer VR platforms were released. With VR now accessible in the home and the isolationist lifestyle adopted due to the COVID-19 global pandemic, VR is now viewed as a potential tool to enhance remote education. Drawing upon over 17 years of experience across numerous VR applications, this dissertation examines the optimal use of VR technologies in the areas of visualization, simulation, training, education, art, and entertainment. It will be demonstrated that VR is well suited for education and training applications, with modest advantages in simulation. Using this context, the case is made that VR can play a pivotal role in the future of education and training in a globally connected world