Autonomous Decision-making Process Research Articles

Data driven digital twin system for the cross-domain vehicle

The Cross-Domain Vehicle (CDV) is a sophisticated marine equipment capable of performing tasks across different domains. This paper aims to develop a data-driven digital twin system for the CDV to achieve precise motion control and data monitoring during its operation. Leveraging the kinematic physical characteristics of the CDV, the digital twin system constructs a virtual counterpart of the vehicle, establishing a mapping relationship between the physical and virtual entities. The use of a Long Short-Term Memory (LSTM) network model is proposed for forecasting the motion state of the CDV within the digital twin framework, supporting the vehicle's autonomous motion decision-making process. Additionally, an innovative model predictive control strategy is employed to achieve surface speed tracking control for the CDV, leveraging predictive data within the digital twin system. Utilizing the digital twin system to optimize control parameters, better underwater depth control effects can be achieved for the CDV. Finally, through the system implementation and experiment verification, the effectiveness of the digital twin system is successfully demonstrated, providing reliable results for both surface speed tracking control and underwater depth control.

Autonomous Vehicle Safety through the SIFT Method: A Conceptual Analysis

This study aims to provide a conceptual analysis of the dynamic transformations occurring in an autonomous vehicle (AV), placing a specific emphasis on the safety implications for pedestrians and passengers. AV, also known as self-driving automobiles, are positioned as potential disruptors in the contemporary transportation landscape, offering heightened safety and improved traffic efficiency. Despite these promises, the intricate nature of road scenarios and the looming specter of misinformation pose challenges that can compromise the efficacy of AV decision-making. A crucial aspect of the proposed verification process is the incorporation of the stop, investigate the source, find better coverage, trace claims, quotes, and media to the original context (SIFT) method. The SIFT method, originally designed to combat misinformation, emerges as a valuable mechanism for enhancing AV safety by ensuring the accuracy and reliability of information influencing autonomous decision-making processes.

Watchful waiting: public relations strategies to minimize and manage a fake news crisis

PurposeFake news stories have become a central element of crises that corporate public relations practitioners have confronted. Whether such stories are rumors, outright lies or deliberate attempts to discredit corporations, they have the same impact and require specific strategies for public relations practitioners to effectively respond. The purpose of this study is to uncover strategies to manage crises that arise from fake news and if and how these strategies differ for other corporate crises.Design/methodology/approachIn this multi-method study of 21 in-depth interviews and a 8-person focus group with senior-level corporate public relations practitioners, authors explored decision-making strategies for responding to fake news crises. Transcripts of interviews and the focus group were thematically analyzed.FindingsResults reveal insights regarding how public relations practitioners determine if and when to respond to fake news crises in corporations; what response strategies public relations practitioners have the autonomy to employ for fake news crises in corporations, and how public relations practitioners control media narratives during fake news crises in corporations.Practical implicationsThe findings guide public relations practitioners to craft an autonomous decision-making process and effective online listening strategies—establishing a watchful waiting approach—and determine if the fake news issue is a passing moment or movement swirling into a crisis.Originality/valueFew studies have examined the perspectives of crisis communication experts about minimizing and managing fake news crises. The study identifies opportunities for future research focused on crises originating from fake news and disinformation.

Dynamic task allocation in multi autonomous underwater vehicle confrontational games with multi-objective evaluation model and particle swarm optimization algorithm

In this paper, a non-zero-sum game model based on a multi-objective evaluation model was adopted to solve the dynamic task allocation problem of underwater multiple Autonomous Underwater Vehicle (multi-AUV). By introducing a functional coordination mechanism, the high degree of coordination in practical confrontation scenarios was reflected, and the autonomous decision-making and task allocation process of multiple AUVs were clarified. Secondly, a multi-objective evaluation model including survival value, strike income, and ammunition loss was constructed, and weighted processing was performed on the multiple objectives using the Analytic Hierarchy Process (AHP) method, obtaining the benefits of each non-zero-sum strategy in dynamic games. In addition, a particle swarm optimization (PSO) algorithm that combines the theory of good point sets theory (G) and speed control factors (S) for improvement, called GSPSO, was used to find the optimal strategy in the game and allocate tasks. Finally, simulation analysis showed that the collaborative system with functional coordination mechanisms significantly improved the combat capabilities of underwater multiple AUVs. The multi-objective evaluation model combined with AHP can correctly and comprehensively evaluate the advantages and disadvantages of each strategy and correctly respond to changing confrontation tasks and decision preferences. The proposed algorithm improved the convergence speed and global search ability by enhancing the diversity of the population and the effectiveness of iterative solutions, ensuring real-time decision-making and task allocation in complex high-dimensional dynamic games.

Local knowledge matters: understanding the decision-making processes of communities under climate change in Suriname

IntroductionTraditionally, local communities have relied on practical observations accumulated over extended periods to inform their decision-making. This knowledge is now recognized as a viable solution for communities to adapt to climate change effectively. The impact of climate change brings an extra layer of complexity to local communities' detection- and decision-making processes, which needs to be better comprehended.MethodologyOur study builds on the foundation of conflict resolution and examines the knowledge systems and corresponding decision-making processes of local communities living in urban, rural, and tropical forest regions of Suriname, South America.ResultsThe mixed-method study showed that the autonomous decision-making processes of these communities are guided by their knowledge systems, intertwined with values and interests. Forest communities in remote locations rely solely on their robust knowledge base for crafting adaptation solutions, while urban and rural communities near the administrative centers develop adaptation strategies primarily considering their access to social networks and relative power.DiscussionThe study highlights local knowledge as the primary determinant for the direction communities take in adaptation, with tradeoffs becoming evident as communities navigate the broader social context. The recognition and integration of this knowledge emerge as a critical factor in enhancing climate change adaptation at the local level.

Why Do Retail Customers Adopt Artificial Intelligence (AI) Based Autonomous Decision-Making Systems?

Advancements in Artificial Intelligence (AI) have led to the development of autonomous decision-making processes, allowing customers to delegate decisions and tasks. Such technologies have the potential to alter the retailing landscape. Grounded in the unified theory of acceptance and use of technology and Hofstede's cultural theory, this article investigates customers’ adoption of AI-based autonomous decision-making processes by analyzing 454 customer responses using covariance-based structural equation modeling. The results reveal that effort expectancy, performance expectancy, facilitating conditions, and social influence are positively associated with customers’ adoption of autonomous decision-making processes. Collectivism strengthened the positive association of social influence with customer attitude, whereas uncertainty avoidance dampened the associations of performance expectancy, effort expectancy, and social influence with attitude. The findings provide useful implications for system developers and managers while providing future researchers with directions to further explore autonomous decision-making processes.

Agent-based Decision Making and Control of Manufacturing System Considering the Joint Production, Maintenance, and Quality by Reinforcement Learning

Taking an integrated approach towards production, maintenance, and control in manufacturing systems is crucial due to the profound impact of their interconnections. Investigating these aspects in isolation may lead to infeasible solutions. This research focuses on the real-time and autonomous decision-making process concerning joint production planning, maintenance, and quality problems in a stochastic deteriorating production system with limited maintenance activities. Formulating the problem as a continuous semi-Markov decision process accounts for the complexities of the real production system and the occurrence of events over an uneven and continuous period. While dynamic programming is a common tool for addressing joint optimization problems, it has limitations, such as the curse of dimensionality. In this study, the optimal policy of the decision-maker agent is obtained by the goal-directed machine learning method called (R-SMART) and agent-based modeling. To the author's knowledge, the proposed approach is novel, and there is little research on such an implementation of the joint optimization problem. The quality of the optimal policy is evaluated through heuristic and simulation-optimization methods in various scenarios. The results demonstrate that the proposed RL-based method outperforms others in most scenarios, achieving a stable, integrated optimal policy.

Automated Speed and Lane Change decision-making Model using Support Vector Machine

One of the major obstacles that the auto industry must overcome is the rise of autonomous vehicles. The study of lane-changing is an important part of this problem. Previous studies on autonomous vehicle lane changes have predominantly focused on lane change path planning and path monitoring, with limited attention given to the autonomous vehicle's lane change decision-making process. This paper introduces a novel Lane Change Decision-Making Model for autonomous vehicles using the Support Vector Machine (SVM) method. The suggested model employs real-time sensor data to assess whether or not a lane change is possible, taking into account the proximity of other vehicles (cars, buses, motorbikes), and adjusting speed as necessary to ensure a seamless transition. Researching the various facets of lane changes in autonomous vehicles allows for decision-making that is grounded in utility, safety, and tolerance. The implementation of a support vector machine (SVM) technique with Bayesian parameter optimization is used to deal with the non-linearity and complexity of the process of autonomous lane change decision-making. Finally, we compare the suggested SVM model against a rule-based lane change model using the test data. The SVM-based strategy is shown to improve lane change decision-making in a comprehensive simulation exercise, which in turn improves the safety and efficiency of autonomous driving systems. The experiment also use a real vehicle to gauge the efficacy of the underlying decision-making model.

Teoria racjonalnego wyboru w świetle biopolityki

Both rational choice theory and biopolitics are extremely important paradigms in the social sciences - including political science. It would seem that their basic axioms contradict each other because the influence of biopolitical power microstructures excludes sovereign and autonomous decision-making processes. Thus, the paper adopts the hypothesis that the assumptions of biopolitics falsify the basic axioms of rational choice theory, and therefore we can consider it as the antithesis of rational choice theory. In order to verify the hypothesis, the method of content analysis was applied - with particular emphasis on studies concerning both paradigms and primary sources supported by elements of comparative studies. On the basis of the analyses carried out, it was proved that the assumptions of both paradigms do not have to contradict each other - they may even be complementary. Human actions based on the principle of rationality and self-interest can be justified by the actions of biopolitics, and therefore the hypothesis has been falsified.

Explainable autonomous robots in continuous state space based on graph-structured world model

Autonomous robots using deep reinforcement learning have demonstrated superior capabilities on relatively simple specific tasks, but they often lack high-level, abstract planning capabilities when faced with complex, long-horizon tasks. Even if the autonomous robot successfully achieves long-horizon goals, users find it difficult to trust their decision-making process. To increase user trust in the decision-making process when an autonomous robot executes a long-horizon task, this paper proposes an algorithm that empowers the autonomous agent to explain to users the transition from the current state to the target state in a continuous state space, as well as to explain the errors in user estimates. A framework that uses a graph-based world model is proposed to identify important nodes and reachability between nodes in the decision-making process; based on these nodes and reachability, the model generates the required explanations. To validate our proposed method's ability to generate long-horizon plans and explanations, we conducted experiments in PointMaze environments. Our simulation results confirm the effectiveness of our approach in generating reliable world models for long-horizon tasks. Moreover, our explanations, based on these world models, significantly enhance the user's understanding of the autonomous robotic decision-making process and the system's capabilities and limitations.

Conditions for autonomous reproductive decision-making in prenatal screening: A mixed methods study

BackgroundPregnant women should be able to make autonomous and meaningful decisions about prenatal screening for fetal abnormalities. It remains largely unclear which circumstances facilitate or hinder such a decision-making process. ObjectiveTo investigate what conditions Dutch pregnant women and professional experts consider important for autonomous reproductive decision-making in prenatal screening for fetal abnormalities, and the extent to which, according to women, those conditions are met in practice. MethodsA mixed methods study was conducted in the Netherlands in 2016–2017. A conceptual model was used to interview professional experts (n = 16) and pregnant women (n = 19). Thematic analysis was performed to identify important conditions. Subsequently, a questionnaire assessed the perceived importance of those conditions and the extent to which these were met, in the experience of pregnant women (n = 200). ResultsProfessional experts stressed the importance of information provision, and emphasized a rational decision-making model. Pregnant women differed in what information they felt was needed, and this depended on the screening decision made. Questionnaire findings showed that women prioritized discussion and consensus with partners. Information about test accuracy and miscarriage risk of invasive follow-up testing was also considered important. Two key conditions were not adequately met, in the experience of women: (1) having information about miscarriage risk; (2) not being directed by health professionals in decision-making. ConclusionAccording to women, discussion and consensus with partners was considered a highly important condition for an autonomous and meaningful decision-making process. Access to information about safety of testing and ensuring that women are not being directed in their decision-making by health professionals seem to be areas for improvement in prenatal care practice.

A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance

Integration of distributed energy sources, advanced meshed operation, sensors, automation, and communication networks all contribute to autonomous operations and decision-making processes utilized in the grid. Therefore, smart grid systems require sophisticated supporting structures. Furthermore, rapid detection and identification of disturbances and transients are a necessary first step towards situationally aware smart grid systems. This way, high-level monitoring is achieved and the entire system kept operational. Even though smart grid systems are unavoidably sophisticated, low-complexity algorithms need to be developed for real-time sensing on the edge and online applications to alert stakeholders in the event of an anomaly. In this study, the simplest form of anomaly detection mechanism in the absence of any a priori knowledge, namely, the energy detector (also known as radiometer in the field of wireless communications and signal processing), is investigated as a triggering mechanism, which may include automated alerts and notifications for grid anomalies. In contrast to the mainstream literature, it does not rely on transform domain tools; therefore, utmost design and implementation simplicity are attained. Performance results of the proposed energy detector algorithm are validated by real power system data obtained from the DOE/EPRI National Database of power system events and the Grid Signature Library.

Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.

The coronavirus infection continues to spread rapidly worldwide, having a devastating impact on the health of the global population. To fight against COVID-19, we propose a novel autonomous decision-making process which combines two modules in order to support the decision-maker: (1) Bayesian Networks method–based data-analysis module, which is used to specify the severity of coronavirus symptoms and classify cases as mild, moderate, and severe, and (2) autonomous decision-making module–based association rules mining method. This method allows the autonomous generation of the adequate decision based on the FP-growth algorithm and the distance between objects. To build the Bayesian Network model, we propose a novel data–based method that enables to effectively learn the network’s structure, namely, MIGT-SL algorithm. The experimentations are performed over pre-processed discrete dataset. The proposed algorithm allows to correctly generate 74%, 87.5%, and 100% of the original structure of ALARM, ASIA, and CANCER networks. The proposed Bayesian model performs well in terms of accuracy with 96.15% and 94.77%, respectively, for binary and multi-class classification. The developed decision-making model is evaluated according to its utility in solving the decisional problem, and its accuracy of proposing the adequate decision is about 97.80%.Graphical abstract

Digital freedom and corporate power in social media

ABSTRACT The impact of large digital corporations on our freedom is often lamented but rarely investigated systematically. This paper aims to fill this desideratum by focusing on the power of social media corporations and the freedom of their users. In order to analyze this relationship, I distinguish two forms of freedom and two corresponding forms of power. Social media corporations extend their users’ freedom of choice by providing many new options. This provision, however, comes with the domination by these corporations because it is based on their power of uncontrolled interference. Users could escape this domination through exit. One reason why they do not choose this option is that they would lose the benefits associated with the network effects provided by social media services. A second reason is that the power of social media corporations runs so deep that they are able to manipulate the autonomous decision-making processes of their users. Users are provided with many new avenues for authentic self-presentation on social media. By using them, however, users need to conform to a web interface that is designed by corporations to undermine self-control. Through these two mechanisms, corporations are able to promote an interest in users to present themselves on their platforms. Insofar as this works, they secure the users’ compliance to their domination through subjectivation. This double conjunction of choice and domination, on the one hand, and autonomy and subjectivation, on the other, makes the case that the very power of corporate social media stems from their intrinsic connection to genuine practices of freedom.

Adaptive intelligent autonomous system using artificial somatic markers and Big Five personality traits

This work presents the design of an adaptive, intelligent, autonomous system based on the use of artificial somatic markers and personality traits of the Big Five model. The aim is for the system to be capable of performing decision-making processes autonomously without human intervention, adapting its decision strategy according to the domain conditions and obtained results. For this, the system permanently has one of the five different personality profiles of the Big Five model, and the ability to adapt its personality profile in real time based on artificial somatic activation, that is, to the reactions experienced by the system in the face of perceived stimuli during the autonomous decision-making process. The novelties of this work include the following: design of a general operational framework of an adaptive, intelligent, autonomous system, which integrates domain indicators, artificial somatic markers, personality profiles, domain profiles, and business rules; design of a somatic index function; design of a scheme for the translation of artificial somatic reactions to personality profiles of the Big Five model; and definition of a case study based on stock markets. The promising results show that the system achieves effectiveness and efficiency from the decisions made.

Ancillary mechanism for autonomous decision-making process in asymmetric confrontation: a view from Gomoku

ABSTRACT This paper investigates how agents learn and perform efficient strategies by trying different actions in asymmetric confrontation setting. Firstly, we use Gomoku as an example to analyse the causes and impacts of asymmetric confrontation: the first mover gains higher power than the second mover. We find that the first mover learns how to attack quickly while it is difficult for the second mover to learn how to defend since it cannot win the first mover and always receives negative rewards. As such, the game is stuck at a deadlock in which the first mover cannot make further advances to learn how to defend, and the second mover learns nothing. Secondly, we propose an ancillary mechanism (AM) to add two principles to the agent’s actions to overcome this difficulty. AM is a guidance for the agents to reduce the learning difficulty and to improve their behavioural quality. To the best of our knowledge, this is the first study to define asymmetric confrontation in reinforcement learning and propose approaches to tackle such problems. In the numerical tests, we first conduct a simple human vs AI experiment to calibrate the learning process in asymmetric confrontation. Then, an experiment of 15*15 Gomoku game by letting two agents (with AM and without AM) compete is applied to check the potential of AM. Results show that adding AM can make both the first and the second movers become stronger in almost the same amount of calculation.

Network Selection Based on Evolutionary Game and Deep Reinforcement Learning in Space-Air-Ground Integrated Network

In next generation communication system, space-air-ground integrated network (SAGIN) would be utilized to provide ubiquitous and unlimited wireless connectivity with large coverage, high throughput, and strong resilience. In this integrated network, there are multiple heterogeneous network options to satisfy service requirements, where an efficient network selection strategy is required to improve resource utilization and achieve load balance. In this paper, we propose a system model of network selection in SAGIN and formulate a corresponding evolutionary game. A network selection algorithm based on evolutionary game is proposed to study the autonomous decision-making process of network selection as a supplement. We also propose a deep deterministic policy gradient (DDPG)-based network selection algorithm to handle continuous and high-dimensional action spaces. A particular case is studied for further simulation and analysis. The evolutionary game obtains the selection strategy with the highest payoff at the evolutionary equilibrium point, and the stability of evolutionary equilibrium is proved by varying relative factors. The DDPG-based network selection algorithm obtains the same strategy with the highest reward at convergence at a slower speed. In comprehensive comparison, our proposed algorithms perform better than the repeated stochastic game approach and proximal policy optimization (PPO) algorithm.

Multi-Robot Collaborative Perception With Graph Neural Networks

Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents. To enhance the autonomous robot decision-making process and situational awareness, multi-robot systems have to coordinate their perception capabilities to collect, share, and fuse environment information among the agents in an efficient and meaningful way such to accurately obtain context-appropriate information or gain resilience to sensor noise or failures. In this paper, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience to sensor failures and disturbances. We show that the proposed framework can address multi-view visual perception problems such as monocular depth estimation and semantic segmentation. Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including images corrupted by heavy noise and camera occlusions or failures.

The Grand Model of Bureaucratic Reform in Strengthening Government Innovation: A Review of North Maluku Province, Indonesia

In order to promote government innovation, this article outlines the overall goal of bureaucratic reform in Indonesia as well as how it is being implemented in North Moluccas Province. An exploratory qualitative methodology is used in this article's data analysis, which is complemented by theoretical evaluations of many sources on public administration innovation trends, public service innovation, and government innovation strategies. According to the theoretical review analysis, it indicates how a notion has changed from the most recent trend to the previous trend. This study must be done in order to comprehend the paradigm change in Indonesian economic innovation from conventional models to digital-based innovation. It is crucial to do this study to learn more about Indonesia's regional innovation's paradigm change from traditional models to bureaucratic innovation based on digital technologies. How come? By focusing this research in North Moluccas, it needs a new innovation in island-based administration since its topography comprises of isolated islands that are challenging to link in the public service network. The internal dynamics of Indonesia's Grand Design Bureaucracy from 2010 to 2014, 2015 to 2019, and 2020 to 2024 are related to this change in the trend. A number of conclusions are drawn from the discussion of this article, including the need that the North Maluku Provincial Government compare its accomplishments to those of other regional innovation indexes. By empowering state employees to carry out the digital transformation of public administration and services, North Maluku Province may accomplish public service innovation. The future of civilization, collective intelligence, and creative public service methods will be impacted by these autonomous data-based decision-making processes.

Preferential Roma Representation in the Hungarian Parliament is Obstructed by the Fidesz Government?

In the approach to the 2022 elections in Hungary, ORÖ, the National Roma Self-Government, was supposed to draw up their national minority list but failed to do so before the deadline. This paper argues that this is not just the result of serious political disagreements within the Roma community, but the outcome of different external effects as well. The paper unfolds the two major modes of influence which effect the parliamentary representation of Roma. First, there are institutional constraints hindering the autonomous decision-making processes of the relevant bodies. As the competitive element of elections is pushed down to the level of national self-government, the whole process is more vulnerable to external influences. Second, these institutional weaknesses are complemented by personal bonds and pressures coming from the Fidesz government. Thus, the paper concludes that the system is intended to serve electoral clientelism and as such limits the ‘effectiveness’ of minority representation in Hungary, especially in the case of Roma.