High-level Decision Research Articles

GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: https://github.com/LTU-RAI/GRID-FAST.

The Body Knows Better: Sensorimotor signals reveal the interplay between implicit and explicit Sense of Agency in the human mind

Sense of Agency (SoA) is the feeling of control over our actions. SoA has been suggested to arise from both implicit sensorimotor integration as well as higher-level decision processes. SoA is typically measured by collecting participants' subjective judgments, conflating both implicit and explicit processing. Consequently, the interplay between implicit sensorimotor processing and explicit agency judgments is not well understood. Here, we evaluated in one exploratory and one preregistered experiment (N = 60), using a machine learning approach, the relation between a well-known mechanism of implicit sensorimotor adaptation and explicit SoA judgments. Specifically, we examined whether subjective judgments of SoA and sensorimotor conflicts could be inferred from hand kinematics in a sensorimotor task using a virtual hand (VH). In both experiments participants performed a hand movement and viewed a virtual hand making a movement that could either be synchronous with their action or include a parametric temporal delay. After each movement, participants judged whether their actual movement was congruent with the movement they observed. Our results demonstrated that sensorimotor conflicts could be inferred from implicit motor kinematics on a trial by trial basis. Moreover, detection of sensorimotor conflicts from machine learning models of kinematic data provided more accurate classification of sensorimotor congruence than participants' explicit judgments. These results were replicated in a second, preregistered, experiment. These findings show evidence of diverging implicit and explicit processing for SoA and suggest that the brain holds high-quality information on sensorimotor conflicts that is not fully utilized in the inference of conscious agency.

No Country for Sick Men: The Political Determinants of Health Policy in Poland

Context: The objective of this article is to explain the political factors determining the relatively weak performance of the Polish health care (HC) sector. This can be treated as a critical case for several reasons. First, the Poles are among the most unsatisfied patients in the European Union, with one of the lowest life expectancy levels. Second, Poland spends one of the lowest shares of gross domestic product on HC-related expenditures among OECD countries. Third, the country is facing medical personnel shortages. Methods: The analysis is based on the mixed-methods approach. The authors rely on quantitative data outsourced from a survey, which is supplemented by the semistructured, in-depth interviews with selected key HC stakeholders representing patients' advocacy groups, medical personnel organizations, and high-level decision-makers. Findings: The Polish HC system remains weak due to the postcommunist legacy in terms of organization, a short-term approach by politicians, and weak decision-making processes. Conclusions: The HC policy inertia in Poland is determined by a group of interrelated political factors that effectively block the development of any positive reform.

Leveraging belief uncertainty for informed decision making in software product line evolution

Software Product Lines (SPL) are not static software artifacts, but they evolve over time. The planning, realization, and release of a SPL requires many high-level decisions involving many different stakeholders with different expertise. Taking their opinions into account to make the right decisions is not trivial. Currently there are no mechanisms to assist stakeholders in the decision making process in an informed manner. In this paper, we propose the use of belief uncertainty in conjunction with feature models to assist in the evolution of SPLs by explicitly quantifying opinions and their associated uncertainty. We present a novel approach in which subjective logic is used to represent the opinions of stakeholders in three evolution scenarios, namely feature model evolution, next release problem and variability reduction. We apply our approach to the evolution of the Xiaomi MiBand SmartWatch SPL over the time period from July 2014 to October 2023. We present an implementation of our approach and evaluate its scalability.

A model of dyadic merging interactions explains human drivers' behavior from control inputs to decisions.

Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

A dual-driven fusion model of evaluating the performance and carbon emissions for recycled waste pavement

Solid waste recycling is an essential approach for the sustainable transition of transportation infrastructure development. In this study, a holistic model for recycled waste pavement was developed, achieving a breakthrough in eco-efficiency-based pavement material design. Using this model, we can not only individually assess the technical feasibility of the pavement material and its carbon emissions, but also realize the unified dimensional quantification of multidimensional parameters based on decision-making expectations using a metrics fusion system, thus achieving high-level sustainability decisions for pavement schemes. The proposed model was validated by evaluating the comprehensive properties of steel slag pavements to determine a suitable and durable pavement solution. This study provides a decarbonization strategy for the transportation sector considering waste-recycled pavement design, which may promote the development of more resilient transportation infrastructure and significantly contribute to achieving carbon neutrality and mitigating climate change.•A model for solid waste pavement was established based on synthesized assessing feasibility and carbon emissions.•The field performance and climate system impacts of pavements with solid waste materials were assessed.•It was demonstrated that recycling steel slag for pavement construction can reduce carbon emissions by >50 %.•Our results contribute to achieving sustainable transportation infrastructure systems to mitigate climate issues.•Our methodology can be used not only for road construction but also in the civil engineering field.

Digital Technology in Healthcare—An Ethical Overview

This overview of ethical aspects of artificial intelligence (AI) in healthcare is organized according to major types of medical activities. It discusses important ethical problems in (1) diagnostic uses of AI such as image interpretation and text interpretation, (2) the use of AI as decision support or even as decision-maker, (3) its use for treatment, for instance in robotic surgery, exoskeletons, virtual psychotherapy and therapeutic robots (care robots), and (4) the use of AI for healthcare documentation and communication. Summing up, we emphasize two major challenges in the introduction of AI that should concern not only healthcare professionals but also higher level decision-makers such as politicians and hospital directors. The first of these is the need to retain human contacts between patients and healthcare, in particular contacts that are conducive to patient health and well-being. The second is the need to ensure that the introduction of AI in healthcare does not create or exacerbate injustices in healthcare. This can happen if prevalent human biases are encoded in AI tools. It can also occur if people with less money have to use inexpensive AI tools such as virtual psychotherapists whereas the well-to-do have access to a human psychotherapist. On the other hand, AI tools can be used to provide better healthcare to all who need it, and they can also be programmed to avoid biases and other mistakes that humans are prone to. The article concludes with brief summaries of the articles in the special issue on AI in healthcare that it introduces.

Simultaneous sizing and energy management of multi-energy Virtual Power Plants operating in regulated energy markets

This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1 % and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit.

Level of Adoption of Innovation in Terrarium Cultivation among the Zoomer Generation

The increasingly rapid development of technology provides changes in several aspects. Technological advances in this era apart from providing many benefits also provide challenges. One project that can accommodate the zoomer generation in following technological developments is the use of terrarium cultivation. Terrariums fulfill the 3 R principles, namely Reduce, Reuse, Recycle. By planting a terrarium, it means saving water, saving land, and reducing air pollution (Reduce), also reusing unused containers such as used jars (Reuse), and using animal waste as compost (Recycle). The facts in the field are that students as the zoomer generation have received training and socialization on terrarium cultivation, but the level of adoption (acceptance) of students regarding socialization and training on terrarium cultivation varies, therefore a study was carried out to what extent the level of adoption of the results of training and socialization in the application of cultivation terrarium for the zoomer generation. The data collection method used was a survey method with research subjects of the zoomer generation, namely 54 active students. The data collected is categorized tabulatedly and then analyzed descriptively qualitatively. This data analysis uses measurements of observation indicators using a "rating scale". The results of the research show that the level of adoption of zoomer generation innovations in terrarium cultivation, if seen from the latest average, the adoption level is in the very high category. Differences in the level of adoption of terrarium cultivation innovations in the zoomer generation with a very high level of knowledge, a very high level of persuasion/interest, a very high level of decision, a very high level of implementation and a very high level of confirmation. If you look at the overall score, it is in the very high category.

Towards an effective approach for composition of model transformations

Model Driven Engineering (MDE) adoption in the industry suffers from many technical and non-technical problems. One of the significant technical problems lies in the difficulty of building complex transformations from the composition of small and reusable transformations. Another problem resides in developing transformations from scratch in case they are missing. In this paper, we present an approach to how to handle these issues. The approach allows composing reusable transformations to build more complex ones by providing a catalog of prebuilt transformations targeting common architectures, frameworks, and design patterns. To give guidance and simplify the task of developing new transformations, we describe a platform description model of an entire system or a part of it in two views: a UML profile and a set of transformations. We also present three transformation types, each of which handles different abstraction design concerns. Generic transformations are small and reusable to build complex transformations, system-independent transformations are reusable and implement high-level design decisions, and system-specific transformations are not reusable and implement all design decisions needed for a given system. The approach is implemented as a plugin for a UML modeling tool and validated by developing a system that simulates the behavior of a gas station through model transformations built from the composition of reusable transformations.

Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

This paper reviews the current state of research in data analytics and machine learning techniques, focusing on their applications in process industrial manufacturing, particularly in control and optimization. Key areas for future research include selection and transfer learning for process monitoring, addressing time-varying characteristics, and enhancing data-driven optimal control with domain-specific knowledge. Additionally, the paper explores reinforcement learning techniques and robust optimization, including distributional robust optimization, for high-level decision-making. Emphasizing the importance of historical knowledge of plants and processes, this paper aims to identify knowledge gaps and pave the way for future research in data-driven strategies for process industries, with a particular emphasis on energy efficiency and optimization.

Cortical responses to looming sources are explained away by the auditory periphery

A wealth of behavioral evidence indicate that sounds with increasing intensity (i.e. appear to be looming towards the listener) are processed with increased attentional and physiological resources compared to receding sounds. However, the neurophysiological mechanism responsible for such cognitive amplification remains elusive. Here, we show that the large differences seen between cortical responses to looming and receding sounds are in fact almost entirely explained away by nonlinear encoding at the level of the auditory periphery. We collected electroencephalography (EEG) data during an oddball paradigm to elicit mismatch negativity (MMN) and others Event Related Potentials (EPRs), in response to deviant stimuli with both dynamic (looming and receding) and constant level (flat) differences to the standard in the same participants. We then combined a computational model of the auditory periphery with generative EEG methods (temporal response functions, TRFs) to model the single-participant ERPs responses to flat deviants, and used them to predict the effect of the same mechanism on looming and receding stimuli. The flat model explained 45% variance of the looming response, and 33% of the receding response. This provide striking evidence that difference wave responses to looming and receding sounds result from the same cortical mechanism that generate responses to constant-level deviants: all such differences are the sole consequence of their particular physical morphology getting amplified and integrated by peripheral auditory mechanisms. Thus, not all effects seen cortically proceed from top-down modulations by high-level decision variables, but can rather be performed early and efficiently by feed-forward peripheral mechanisms that evolved precisely to sparing subsequent networks with the necessity to implement such mechanisms.

Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety

The application of autonomous driving system (ADS) technology can significantly reduce potential accidents involving vulnerable road users (VRUs) due to driver error. This paper proposes a novel hierarchical deep reinforcement learning (DRL) framework for high-performance collision avoidance, which enables the automated driving agent to perform collision avoidance maneuvers while maintaining appropriate speeds and acceptable social distancing. The novelty of the DRL method proposed here is its ability to accommodate dynamic obstacle avoidance, which is necessary as pedestrians are moving dynamically in their interactions with nearby ADSs. This is an improvement over existing DRL frameworks that have only been developed and demonstrated for stationary obstacle avoidance problems. The hybrid A* path searching algorithm is first applied to calculate a pre-defined path marked by waypoints, and a low-level path-following controller is used under cases where no VRUs are detected. Upon detection of any VRUs, however, a high-level DRL collision avoidance controller is activated to prompt the vehicle to either decelerate or change its trajectory to prevent potential collisions. The CARLA simulator is used to train the proposed DRL collision avoidance controller, and virtual raw sensor data are utilized to enhance the realism of the simulations. The model-in-the-loop (MIL) methodology is utilized to assess the efficacy of the proposed DRL ADS routine. In comparison to the traditional DRL end-to-end approach, which combines high-level decision making with low-level control, the proposed hierarchical DRL agents demonstrate superior performance.

Boardroom blind spots: why HR exclusion puts corporate reputations at risk

PurposeThis study aims to explore the ramifications of a lack of human resources (HR) representation in corporate boardrooms.Design/methodology/approachThe author draws upon her personal and professional experience as a director of human resources and as a board member for a corporation.FindingsThere is a fundamental misunderstanding of the strategic value that HR brings to the table. To thrive in the modern business environment, organisations must acknowledge this strategic value and actively involve HR professionals in high-level decision-making processes.Originality/valueThis study makes a strong and compelling case for HR’s involvement in high-level decision-making in the boardroom.

Impact of the Local Dynamics on Exit Choice Behaviour in Evacuation Model

This study investigated the interplay between exit selection models and local pedestrian movement patterns within floor field frameworks. Specifically, this investigation analysed the performance of a multinomial logit exit choice model, incorporating both expected utility theory and cumulative prospect theory frameworks when coupled with three distinct local-level pedestrian movement models (FF-Von Neumann, FF-Moore, and NSFF). The expected utility theory framework considers the deterministic component as a linear relationship, while the cumulative prospect theory framework further considers the decision-maker’s risky attitudes by transforming objective terms into subjective terms using a power value function. The core objective was to comprehend how local movement dynamics, as represented by the floor field models, influence decision-making during exit selection. Comparative analyses revealed intriguing variations between the three local models, despite their shared expected utility theory-based exit choice framework. These discrepancies stemmed from the diverse pedestrian trajectory behaviours generated by each model. Consequently, these local dynamics impacted the decision-maker’s assessment of critical factors, such as the number of evacuees close to the decision-maker (NCDM) and the number of evacuees close to an exit (NCE), which the exit choice model incorporates. These assessments, in turn, significantly affected higher-level decision-making. The integration of the three models with the multinomial logit exit choice model, using either cumulative prospect theory and expected utility theory frameworks, further strengthened the observed bilateral relationship. While the specific nature of this relationship varied depending on the chosen framework and its implementation details, these consistent findings demonstrate the robustness of the results. This reinforced the influence of local-level pedestrian dynamics on higher-level exit selection, highlighting the importance of accurate crowd dynamics modelling, especially when advanced exit choice models consider local movement factors.

Enable Flexibilisation in FAIRWork’s Democratic AI-based Decision Support System by Applying Conceptual Models Using ADOxx

Decision-making in complex production environments is challenging as the information and knowledge requirements must be constantly observed since the ecosystems they operate in are continuously changing. Artificial intelligence (AI) can tackle complexity in decision-making by making machines more intelligent. But reacting to changing or new problems and related decision processes to facilitate the understanding of the involved humans is an equally important problem. Therefore, decision support systems are required to assist complex decisions and enable flexibility to support the decision-makers. Within this scope, we will introduce the Democratic AI-based Decision Support System (DAI-DSS), which is designed and implemented within the EU-funded FAIRWork project, considering both human and machine actors during decision-making. The FAIRWork project proposes a model-based approach to both express high-level decision scenarios and formally describe the decision processes, which are then used as input for configuring the decision support system to meet concrete decision problems.

Bi-objective model for tactical planning in corn supply chain considering CO2 balance

This paper addresses the Brazilian corn production logistic supply chain with an optimisation model considering carbon emission from transportation and carbon sequestration from corn production. The focus is on the higher-level decision planning, which includes the best regions for corn cultivation, location and sizing of silos to store grains, transportation modes with intermodality, considering several resource constraints to meet the demands of domestic market and export. Two conflicting objectives are considered: the logistics costs of the supply chain and the balance of CO 2 . Accurate data are used to evaluate the model and the Pareto optimal solutions are produced in scenarios potentially feasible in practice. The solutions showed relevant information to guide decisions about investment in logistic infrastructure. We demonstrated that cleaner transportation routes lead to solutions that have larger carbon stocks with 3–5% increase in costs by only choosing more suitable regions for corn cultivation and transportation modes.

HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning

Multi-agent deep reinforcement learning (MADRL) has shown remarkable advancements in the past decade. However, most current MADRL models focus on task-specific short-horizon problems involving a small number of agents, limiting their applicability to long-horizon planning in complex environments. Hierarchical multi-agent models offer a promising solution by organizing agents into different levels, effectively addressing tasks with varying planning horizons. However, these models often face constraints related to the number of agents or levels of hierarchies. This paper introduces HiSOMA, a novel hierarchical multi-agent model designed to handle long-horizon, multi-agent, multi-task decision-making problems. The top-level controller, FALCON, is modeled as a class of self-organizing neural networks (SONN), designed to learn high-level decision rules as internal cognitive codes to modulate middle-level controllers in a fast and incremental manner. The middle-level controllers, MADRL models, in turn receive modulatory signals from the higher level and regulate bottom-level controllers, which learn individual action policies generating primitive actions and interacting directly with the environment. Extensive experiments across different levels of the hierarchical model demonstrate HiSOMA’s efficiency in tackling challenging long-horizon problems, surpassing a number of non-hierarchical MADRL approaches. Moreover, its modular design allows for extension into deeper hierarchies and application to more complex tasks with heterogeneous controllers. Demonstration videos and codes can be found on our project web page: https://smu-ncc.github.io.

ReMKiT1D - A framework for building reactive multi-fluid models of the tokamak scrape-off layer with coupled electron kinetics in 1D

In this manuscript we present the recently developed flexible framework for building both fluid and electron kinetic models of the tokamak Scrape-Off Layer in 1D - ReMKiT1D (Reactive Multi-fluid and Kinetic Transport in 1D). The framework can handle systems of non-linear ODEs, various 1D PDEs arising in fluid modelling, as well as PDEs arising from the treatment of the electron kinetic equation. As such, the framework allows for flexibility in fluid models of the Scrape-Off Layer while allowing the easy addition of kinetic electron effects. We focus on presenting both the high-level design decisions that allow for model flexibility, as well as the most important implementation aspects. A significant number of verification and performance tests are presented, as well as a step-by-step walkthrough of a simple example for setting up models using the Python interface. Program summaryProgram title: ReMKiT1DCPC Library link to program files:https://doi.org/10.17632/j47ym66xzj.1Developer's repository link:https://github.com/ukaea/ReMKiT1D and https://github.com/ukaea/ReMKiT1D-PythonLicensing provisions: GPLv3Programming language: Fortran, PythonSupplementary material:https://doi.org/10.14468/fdq7-z869Nature of problem: The flexible generation and modification of 1D models pertaining to multi-fluid simulations of the tokamak Scrape-Off Layer (SOL) with electron kinetics and reaction support. This would then allow both for rapid iteration on reduced models as well as the evaluation of kinetic electron effects in equilibria and during transients, following the formalism previously developed for SOL-KiT [1]. The framework was not only envisioned as the successor to SOL-KiT, but a tool that would allow users to construct their own models coupled with electron kinetics capabilities.Solution method: The framework is written heavily utilizing Object-Oriented design principles, in particular using an extended version of the puppeteer pattern as presented by Rouson et al. [2], as well as the heavy use of the strategy pattern/dependency injection. The Fortran code is MPI parallel and utilizes the PETSc library for implicit time-stepping. MPI parallelization is extended to distribution function Legendre harmonics, allowing for improved strong scaling. Initialization of the Fortran framework is done using JSON configuration files generated by an accompanying Python interface, and data analysis is standardized using widely used data formats such as HDF5.Additional comments including restrictions and unusual features: The present manuscript focuses on the design and high-level implementation of the framework, as well as the demonstration of the workflow and various verification and performance benchmarking tests. Some details are avoided for the sake of brevity at various points, and these are meant to be available as part of the general code documentation or tutorials offered on the main repositories.

Listening to Cairo: British radio monitoring and intelligence gathering, c. 1953-1967

ABSTRACT This article examines the BBC’s radio monitoring service, its relationship with Whitehall, and the CIA’s Foreign Broadcasting Information Service (FBIS). Radio monitoring was invaluable for analysing broadcasting in states, such as in Egypt, where the media became state controlled. Listening to radio broadcasts provided invaluable open-source intelligence concerning internal economic and political developments that might have been difficult to collect by more orthodox mechanisms. This article focuses on Anglo-American monitoring of Egyptian ‘political’ broadcasts by Sawt al-Arab (Voice of the Arabs) that were widely listened to across the Middle East and East Africa. Egyptian transmissions were characterised by their hostility to Britain’s allies and interests in Iraq, Jordan, and the Persian Gulf, which explains why they were examined in detail by the Joint Intelligence Committee. The evidence shows radio monitoring reports were invaluable for officials on the ground and in Whitehall, but the impact of intelligence analysis of Egypt’s intentions on high level decision making in Whitehall is contestable.