RAMARL: Robustness Analysis with Multi-Agent Reinforcement Learning - Robust Reasoning in Autonomous Cyber-Physical Systems

A key driver to offering smart services is an infrastructure of Cyber-Physical systems (CPS)s. By definition, CPSs are intertwined physical and computational components that integrate physical behaviour with computation. The reason is to autonomously execute a task or a set of tasks providing a service or a list of end-users services. In real-life applications, CPSs operate in dynamically changing surroundings characterized by unexpected or unpredictable situations. Such operations involve complex interactions between multiple intelligent agents in a highly non-stationary environment. For safety reasons, a CPS should withstand a certain amount of disruption and exert the operations in a stable and robust manner when performing complex tasks. Recent advances in reinforcement learning have proven suitable for enabling multi-agents to robustly adapt to their environment, yet they often depend on a massive amount of training data and experiences. In these cases, robustness analysis outlines necessary components and specifications in a framework, ensuring reliable and stable behaviour while considering the dynamicity of the environment. This paper presents a combination of multi-agent reinforcement learning with robustness analysis shaping a cyber-physical system infrastructure that reasons robustly in a dynamically changing environment. The combination strengthens the reinforcement learning, increasing the reliability and flexibility of the system by applying robustness analysis. Robustness analysis identifies vulnerability issues when the system interacts within a dynamically changing environment. Based on this identification, when incorporated into the system, robustness analysis suggests robust solutions and actions rather than optimal ones provided by reinforcement learning alone. Results from the combination show that this infrastructure can enable reliable operations with the flexibility to adapt to the changing environment dynamics.publishedVersio

Fit-Twin: A Digital Twin of a User with Wearables and Context as Input for Health Promotion

Digital health contributes to health promotion by empowering the user with the holistic view of their health. Health promotion is to enable the user to take control over their health. The availability of wearables has contributed to the shift in healthcare, that is more connected, predictive, and proactive. Proactive in healthcare is to predict and prevent a situation, beforehand. This shift in healthcare puts the user in charge of most healthrelated decisions. Innovative technologies like AI already contribute to the cause by applying reasoning and negotiation to the collected health data to provide timely interventions to the user. The availability of realtime data from sensors that the user wears all the time allows more opportunities with new health insights. One such prospect is the use of digital twins, which provides personalization and precision. Digital twins also allow risk-free modelling for more accurate outcomes. A user digital twin is not just a virtual replica, but it combines all the factors that can impact the user. The context of the user is a prominent factor in healthcare. The paper establishes the need for digital twins in health promotion. In this paper, a Fit-twin is presented that mimics a user with wearables and the user context as input. The Fit-twin is implemented using Azure digital twins, Fitbit charge, and local context API. This allows one-way communication between the user and the Fit-twin. The outcome is a user digital twin that can be used for health promotion by applying predictive capabilities

A Framework for AI-enabled Proactive mHealth with Automated Decision-making for a User’s Context

Health promotion is to enable people to take control over their health. Digital health with mHealth empowers users to establish proactive health, ubiquitously. The users shall have increased control over their health to improve their life by being proactive. To develop proactive health with the principles of prediction, prevention, and ubiquitous health, artificial intelligence with mHealth can play a pivotal role. There are various challenges for establishing proactive mHealth. For example, the system must be adaptive and provide timely interventions by considering the uniqueness of the user. The context of the user is also highly relevant for proactive mHealth. The context provides parameters as input along with information to formulate the current state of the user. Automated decision-making is significant with user-level decision-making as it enables decisions to promote well-being by technological means without human involvement. This paper presents a design framework of AI-enabled proactive mHealth that includes automated decision-making with predictive analytics, Just-in-time adaptive interventions and a P5 approach to mHealth. The significance of user-level decision-making for automated decision-making is presented. Furthermore, the paper provides a holistic view of the user's context with profile and characteristics. The paper also discusses the need for multiple parameters as inputs, and the identification of sources e.g., wearables, sensors, and other resources, with the challenges in the implementation of the framework. Finally, a proof-of-concept based on the framework provides design and implementation steps, architecture, goals, and feedback process. The framework shall provide the basis for the further development of AI-enabled proactive mHealth

Data Analysis Techniques for Smart Nudging

Nudge principles and techniques are significant in communications, marketing, and groups’ motivation to improve personal health, wealth, and well-being. We make numerous decisions in online situations. People’s health and well-being have garnered widespread interest and concern in this wearable’s age. Smart nudging is defined as “digital nudging, where the guidance of user behavior is tailored to be relevant to the current situation of each user”. Emerging digital devices such as smartwatches, smart bands, and smartphones will continuously capture and analyze your activity and health-related data from individuals and communities in their everyday environment. Providing context-aware nudges in these digital health devices will help individuals identify and self-manage their health and physical activity. This study aims to provide data analysis techniques for smart nudging and examine it susability in developing a smart nudging system to provide context-based nudges that are more likely to succeed

Data collection and analysis methods for smart nudging to promote physical activity: Protocol for a mixed methods study

New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people’s health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing and enhancing their health. In this protocol paper, we describe how we plan to investigate what motivates people to engage in physical activity (PA), what influences them to accept nudges, and how participant motivation for PA may be impacted by technology use

PT som andrespråkspedagogisk verktøy i barnehage og skole

I artikkelen argumenteres det for et sterkere fokus på utviklingsperspektiv i språklig kartlegging og opplæring av barn og unge som lærer norsk som andrespråk, og for at det er behov for et kartleggingsverktøy med klare og presise kriterier for vurdering av grammatiske strukturer. Med utgangspunkt i Prosessbarhetsteorien beskrives en stadiemodell som kan fungere som et verktøy for lærere for å følge barn og unges andrespråksutvikling over tid. I artikkelen vises hvordan en gruppe lærere i barnehage og skole har brukt stadiemodellen, og hvilke erfaringer de har gjort seg i dette arbeidet. Resultatet viser at lærerne finner modellen hensiktsmessig for å få øye på hvordan andrespråket gradvis vokser fram i forutsigbare stadier.acceptedVersio

Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments

Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, require making valid and solid decisions using one or a combination of robust reasoning strategies, algorithms, and robustness analysis. However, in dynamic environments, data can be incomplete, skewed, contradictory, and redundant impacting the reasoning. Basing decisions on these data can lead to inconsistent, irrational, and unreasonable cyber-physical systems' movements, adversely impacting the system’s reliability and integrity. This paper presents the assessment of robust reasoning for autonomous cyber-physical systems in dynamic environments. In this work, robust reasoning is considered as 1) the capability of drawing conclusions with available data by applying classical and non-classical reasoning strategies and algorithms and 2) act and react robustly and safely in dynamic environments by employing robustness analysis to provide options on possible actions and evaluate alternative decisions. The result of the research shows that different common existing strategies, algorithms and analyses can be provided together with a comparison of their applicabilities, benefits, and drawbacks in the context of cyber-physical systems operating in dynamically changing environments. The conclusion is that robust reasoning in cyber-physical systems can handle dynamic environments. Moreover, combining these strategies and algorithms with robustness analysis can support achieving robust behavior in autonomous cyber-physical systems while operating in dynamically changing environments

Comparative effectiveness of drugs used to constrict the patent ductus arteriosus: a secondary analysis of the PDA-TOLERATE trial (NCT01958320).

ObjectiveTo evaluate the effectiveness of drugs used to constrict patent ductus arteriosus (PDA) in newborns < 28 weeks.MethodsWe performed a secondary analysis of the multi-center PDA-TOLERATE trial (NCT01958320). Infants with moderate-to-large PDAs were randomized 1:1 at 8.1 ± 2.1 days to either Drug treatment (n = 104) or Conservative management (n = 98). Drug treatments were assigned by center rather than within center (acetaminophen: 5 centers, 27 infants; ibuprofen: 7 centers, 38 infants; indomethacin: 7 centers, 39 infants).ResultsIndomethacin produced the greatest constriction (compared with spontaneous constriction during Conservative management): RR (95% CI) = 3.21 (2.05-5.01)), followed by ibuprofen = 2.03 (1.05-3.91), and acetaminophen = 1.33 (0.55-3.24). The initial rate of acetaminophen-induced constriction was 27%. Infants with persistent moderate-to-large PDA after acetaminophen were treated with indomethacin. The final rate of constriction after acetaminophen ± indomethacin was 60% (similar to the rate in infants receiving indomethacin-alone (62%)).ConclusionIndomethacin was more effective than acetaminophen in producing ductus constriction