Autonomous Decision Research Articles

Autonomous by Extension: Balancing Patient Autonomy and Vulnerability in Surrogate Decision-making.

The experience of serious illness or injury frequently results in increased vulnerability. In cases in which decision-making capacity is compromised, this vulnerability is compounded by threats to a patient's autonomy. This case report presents an opportunity to explore the elaborately entwined relationship between autonomy and vulnerability in patients with severe impairment who are reliant on surrogate decision-makers. Expanded views of autonomy and vulnerability are applied to the case of a young woman with a severe and life-altering spinal cord injury and iatrogenic loss of decisional capacity to illustrate how one can experience enhanced autonomy despite special vulnerability and can be made less vulnerable through surrogate-mediated autonomy. Also revealed is how attitudes and actions of surrogates can potentiate pathogenic vulnerability and disturb the balance between patient autonomy and vulnerability. Through methodical review and robust deliberation, clinical ethics committees can play a stabilizing role in helping distressed care teams reconcile the two.

Learning Autonomous Navigation in Unmapped and Unknown Environments.

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, we have proposed an innovative autonomous navigation algorithm called PEEMEF-DARC. This algorithm consists of three parts: Double Actors Regularized Critics (DARC), a priority-based excellence experience data collection mechanism, and a multi-source experience fusion strategy mechanism. The algorithm is capable of performing autonomous navigation tasks in unmapped and unknown environments without maps or prior knowledge. This algorithm enables autonomous navigation in unmapped and unknown environments without the need for maps or prior knowledge. Our enhanced algorithm improves the agent's exploration capabilities and utilizes regularization to mitigate the overestimation of state-action values. Additionally, the priority-based excellence experience data collection module and the multi-source experience fusion strategy module significantly reduce training time. Experimental results demonstrate that the proposed method excels in navigating the unmapped and unknown, achieving effective navigation without relying on maps or precise localization.

Adaptation and psychometric assessment of a sexual and reproductive empowerment scale in Arabic among refugee and non-refugee adolescent girls.

Sexual and reproductive empowerment (SRE) is an important determinant of women's and girls' health yet measuring it is complex due to cultural and domain-specific variations. This study describes the process of adapting an SRE scale consisting of four domains (self-efficacy; future orientation; social support; and safety) and testing its psychometric properties among Arabic speaking adolescent girls in Lebanon. An SRE scale developed in a Western context was adapted in four steps: (1) reviewing the scale and selecting culturally appropriate domains for translation to standard Arabic; (2) conducting cognitive interviews with 30 11-17-year-old adolescent girls in Lebanon; (3) administering the scale to 339 refugee adolescent girls who participated in an early marriage intervention; and (4) conducting confirmatory factor analysis (CFA) on the data to assess the scale's psychometric properties. The original model for the 13-item, four-domain adapted scale demonstrated poor fit in CFA. After iteratively removing two items, scale properties were improved, albeit were not optimal. The validity and reliability results for the self-efficacy domain were acceptable. Cognitive interview data revealed that Arab adolescent girls understood self-efficacy in relational terms, recognizing that autonomous decision-making is not necessarily favored but is influenced by parents and family. This study presents an effort to customize an SRE scale for use in studies on the health of adolescent girls in an Arab cultural context. Findings from cognitive interviews highlight the importance of taking into consideration relationality in adolescent sexual and reproductive decision-making. The self-efficacy domain in the adapted scale demonstrates acceptable psychometric properties and is recommended for use in health studies to capture SRE.

Caring for the Unvaccinated During a Pandemic: Connecting Swanson's Theory of Caring.

Background and Purpose: Nurses frequently care for patients who make decisions against medical advice, a challenge that continues during and after the COVID-19 pandemic. This article explores U.S. nurses' experiences caring for unvaccinated COVID-19 patients and the resulting impacts using Swanson's Theory of Caring (STC). Methods: Data were gathered through an online survey collecting nurses' demographics, mental health screening data, and an open-ended question asking about a personal experience providing care to an unvaccinated COVID-19 patient. Researchers analyzed 128 responses using content analysis. STC served as the analytical framework. Results: Nurses' experiences were summarized into two main themes and four subthemes: (a) anger, frustration, fatigue, and resentment (24.2%) and (b) feeling a professional commitment to a nurse's duty (60.9%). Within the professional commitment theme, subthemes include the following: (a) patients can make an autonomous decision (15.6%); (b) feeling concerned for the patient (6.3%); (c) wanting to take action, educate, and vaccinate (29.7%); and (d) feeling moral conflict (9.3%). Results aligned with STC's five caring actions: knowing, doing for, being with, enabling, and maintaining belief. Implications for Practice: Nurses experienced emotional challenges caring for the unvaccinated during the pandemic resulting in psychological sequelae, including compassion fatigue and moral distress, in addition to positive outcomes of resilience and enhanced coping mechanisms. This study underscores the multifaceted nature of nurses' experiences and the emotional, ethical, and professional challenges they encountered when caring for unvaccinated COVID-19 patients. Nurse leaders should address these issues to enhance nurses' wellbeing and reduce compassion fatigue and dissatisfaction.

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond electronic computers, neural networks have been implemented using programmable biomolecules such as DNA; this confers unique advantages, such as greater portability, electricity-free operation, and direct analysis of patterns of biomolecules in solution. Analogous to bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e., neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each additional neuron compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here, we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis for high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with a high signal-to-background ratio and process-weighted inputs. We rewired our modular neurons to demonstrate basic neural network motifs such as cascading, fan-in, and fan-out circuits. Finally, we designed a prototype two-layer microfluidic device to automate the operation of our circuits. We envision that our proposed design will help scale DNA-based neural networks due to its modularity, simplicity of synthesis, and compatibility with various neural network architectures. This will enable portable computing power for applications in portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.

Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.

Internet of Conscious Things: Ontology-Based Social Capabilities for Smart Objects

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach.

Coordinating paths and schedules of groups visiting multiple capacitated locations

We study a situation where a large number of people visit a popular venue (e.g., an art gallery, a mall, a theme park, or an exhibition) where points of interest are located (e.g., paintings, shops, attractions, or pavilions). Visitors have a maximum amount of time available for the overall experience. The points of interest have a limiting capacity and, whenever the turnout of visitors exceeds such capacity, queues and service disruptions occur. Given the maximum time available along with the time spent queuing, a selection of the points of interest may become necessary. Visitors usually act as autonomous decision-makers and do not take into account their interaction with other visitors. This leads to remarkable inefficiencies that could be, to a certain extent, overcome through a coordination of the paths and schedules of the different visitors. The resulting optimization problem is modeled as a Mixed-Integer Linear Program (MILP), where the goal is to minimize a weighted combination of the points of interest not selected and the time spent queuing. Computational results show the benefits that can be achieved by using the model proposed as a tool to support decision-making.

An Adaptive Missing Data Restoration Method for UAV Confrontation Based on Deep Regression Model

Completing missions with autonomous decision-making unmanned aerial vehicles (UAV) is a development direction for future battlefields. UAV make decisions based on battlefield situation information collected by sensors and can quickly and accurately perform complex tasks such as path planning, cooperative reconnaissance, cooperative pursuit and attacks. Obtaining real-time situation information of enemy is the basis for realizing autonomous decision-making of the UAV. However, in practice, due to internal sensor failure or interference of enemy, the acquired situation information is prone to be missing, which affects the training and decision-making of autonomous UAV. In this paper, an adaptive missing situation data restoration method for UAV confrontation is proposed. The UAV confrontation situation data are acquired through JSBSim, an open-source UAV simulation platform. By fusing temporal convolutional network and long short-term memory sequences, we establish a deep regression method for missing data restoration and introduce an adaptive mechanism to reduce the training time of the restoration model in response to dynamic changes in the enemy’s strategy during UAV confrontation. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models under different degrees of data missing conditions. The performance of our method is quantified by five metrics. The performance of our proposed method is better than the other benchmark algorithms. The experimental results show that the proposed method can solve the missing data restoration problem and provide reliable situation data while effectively reducing the training time of the restoration model.

Evolutionary method of digital twin model for building physics mechanism

ABSTRACT Building digital twin (BDT) realizes real-time monitoring, prediction, and optimization control of the building system through the synchronization between the real world and the virtual platform, and improves the efficiency of building operation and maintenance. However, the changes in physical laws caused by the decline and aging of buildings make it difficult for the BDT models to depict the dynamic evolution mechanism of buildings and improve decision-making, which brings great challenges to the prediction and maintenance of buildings. To this end, an evolutionary method of digital twin model for building physics mechanisms is proposed, which integrates the internet of things (IoT), building information physical model (BIPM), and machine learning algorithms to build a self-evolution framework of digital twins for building physics mechanism, and supports high-precision modeling of BDTs. The advanced prediction (AP) model and autonomous decision-making (ADM) model for building physics mechanisms with self-evolution capability are designed, which support the autonomous correction of model parameters and the dynamic updating of the model driven by real-time data, and the ability to learn optimal control actions through interactive trial and error with the environment. The experimental results based on the settlement prediction and control case of underground engineering show that this evolutionary method can dynamically simulate the future evolution trend of the settlement law of underground engineering, and adjust the physical parameters to the ideal range to guide the evolution of underground engineering in a better direction. Compared with the existing methods, it has better prediction accuracy, interpretability, and generalization performance.

Insights into maternal pertussis vaccination counselling: a qualitative study on perspectives and experiences among midwives and gynaecologists in the Netherlands

BackgroundHealthcare professionals (HCPs) play a significant role in the decision-making process of pregnant women on maternal vaccinations. Whereas a high proportion of HCPs discuss maternal vaccinations with pregnant women, confidence in discussing maternal vaccinations is lacking and HCPs experience inadequate training to discuss maternal vaccinations with pregnant women. Furthermore, different practical barriers might influence the consultation process, such as lack of time. More studies on the barriers, as well as facilitators, to discussing maternal vaccinations is needed and will help us to better understand and support HCPs in discussing maternal vaccinations.MethodsThis qualitative study involved semi-structured interviews with fourteen HCPs working as midwives or gynaecologists in the Netherlands. An integrated theoretical approach was used to inform data collection and analysis. Thematic analysis was conducted using inductive and deductive approaches. This study followed the COnsolidated criteria for REporting Qualitative research (COREQ) guidelines.ResultsThe thematic analysis of the data pointed to the following five themes of HCP counselling: the consultation process, attitude, perceived norm, perceived control and improvement ideas. Most HCPs follow a similar approach in maternal pertussis vaccination consultations, beginning by assessing clients’ understanding, providing basic information, and addressing questions. However, consultation timing and prioritization vary among HCPs. Challenges in consultations include client requests for clear advice, with HCPs trained to remain neutral, emphasizing client autonomy in decision-making. Most HCPs acknowledge the importance of their consultations in informing pregnant women about maternal pertussis vaccination.ConclusionsThis study offers a confirmation of the awareness of the pivotal role of HCPs in informing pregnant women about the maternal pertussis vaccination. HCPs stress the importance of neutral counselling, enabling pregnant women to make well-informed decisions independently. Because of upcoming vaccine hesitancy nowadays, HCPs must be equipped with the knowledge and confidence to navigate difficult conversations. Continuous education and training might help to increase HCPs’ confidence in handling difficult consultations. Additionally, making the information materials for pregnant women available in multiple languages and incorporating more visuals to enhance comprehension could support HCPs in reaching a broader group of pregnant women.

Development of a coupled agent-based generation expansion planning tool with a power dispatch model

Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.

Empowering women through a gender-responsive seed multiplication program in Northern Malawi

Limited access to quality seeds remains a major challenge to improving agricultural productivity and resilience to climate change in sub-Saharan Africa, especially in Malawi. Over the past decade, local seed multiplication programs have emerged as a promising solution to enhance farmers’ access to quality seeds in resource-poor regions. Some of such projects have incorporated gender-responsive tools to bridge the gender inequality gap in the seed system. Yet there is a dearth of research examining the impacts of seed multiplication programs on farmers’ livelihoods and in promoting gender equality in smallholder communities. This dearth of research has resulted in the underrepresentation of seed multiplication in agricultural policy. To address this void in the literature, we conducted interviews with 40 participants and three project officers from a gender-responsive seed multiplication initiative launched in 2011/12. Through a feminist political ecology lens, we investigated the program’s impact on gender equality and smallholder farmers’ livelihoods. Our results highlight prominent benefits farmers derived from the program; including livelihood diversification, seed and food security, improvements in women’s household decision-making autonomy, changing gender and social norms, and improvements in women’s leadership opportunities. However, challenges like climate change and constrained market access threaten these gains. Given these insights, we argue that gender-responsive seed initiatives can deliver on gender equality while simultaneously improving smallholder farmers livelihoods. While we advocate for the integration of gender-responsive seed multiplication initiatives into agricultural policy frameworks in Malawi, addressing market accessibility and promoting climate-resilient agricultural practices is crucial to realizing these gains.

Women's decision-making power can influence modern contraceptive use: Evidence from Bangladesh.

It is generally believed that gender inequality and women's lack of decision-making power may restrict women's use of modern contraception, leading to high rates of unwanted pregnancies, abortions, and deaths. Evidence shows that empowered women are more likely to use modern contraception methods, but few studies have investigated this across multiple domains of empowerment. This study examined the associations between women's empowerment and modern contraception use in Bangladesh. Data from a sample of 16,834 married women aged 15-49years from the Bangladesh Demographic and Health Survey 2017-2018 were analysed. Complex survey weight adjusted logistic models were fitted to evaluate the associations after adjusting for clusters, strata, and sampling weights. indicate that just over half of the married women (55.7%) had used modern contraception methods. Women's empowerment was associated with contraceptive use, especially decision-making power. Women who had medium or high autonomy of household decision making were likely to have 20% (AOR=1.20; 95% CI: 1.04-1.39) and 27% (AOR=1.27, 95% CI: 1.11-1.45) increased odds of using modern contraceptives compared to those who scored low in the decision-making domain. The findings demonstrated strong evidence of direct influence of women's decision-making power on modern contraception use. The results also found influence of several socio-demographic factors including area of residence, husband's age, wealth index andmobile phone ownership on the use of modern contraceptives. Future interventions can focus on integrating women's empowerment into family planning programming, with a particular focus on enhancing women's autonomy in decision making.

Intelligent decision-making algorithm for airborne phased array radar search tasks based on a hierarchical strategy framework

To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range (BVR) air combat, a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks. Firstly, an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established. Secondly, the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models. Finally, the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences, to enhance the training effectiveness and improve the algorithm convergence speed. Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.

Understanding Investment Motivation in the Young Generation Using Self-Determination Theory

This study aims to analyze the investment motivation of students using the Self-Determination Theory (SDT) framework. SDT emphasizes three basic psychological needs that influence motivation: competence, relatedness, and autonomy. Data were collected through a survey involving students from various disciplines at Universitas Negeri Makassar (UNM). The findings reveal that fulfilling the need for competence through investment training programs and seminars, fostering relatedness through investment communities and discussion groups, and supporting autonomy in investment decision-making significantly enhance students' investment motivation. The conclusion of this study highlights the importance of institutional support in the form of education, community building, and the provision of investment platforms that empower students to make independent decisions. These findings have practical implications for universities and other educational institutions, suggesting the need for effective strategies to boost financial literacy and encourage investment participation among students. By addressing these psychological needs, educational institutions can play a pivotal role in shaping the investment behaviors of the young generation.

Artificial Punishment Signals for Guiding the Decision-Making Process of an Autonomous System

Somatic markers have been evidenced as determinant factors in human behavior. In particular, the concepts of somatic reward and punishment have been related to the decision-making process; both reward and somatic punishment represent bodily states with positive or negative sensations, respectively. In this research work, we have designed a mechanism to generate artificial somatic punishments in an autonomous system. An autonomous system is understood as a system capable of performing autonomous behavior and decision making. We incorporated this mechanism within a decision model oriented to support decision making on stock markets. Our model focuses on using artificial somatic punishments as a tool to guide the decisions of an autonomous system. To validate our proposal, we defined an experimental scenario using official data from Standard & Poor’s 500 and the Dow Jones index, in which we evaluated the decisions made by the autonomous system based on artificial somatic punishments in a general investment process using 10,000 independent iterations. In the investment process, the autonomous system applied an active investment strategy combined with an artificial somatic index. The results show that this autonomous system presented a higher level of investment decision effectiveness, understood as the achievement of greater wealth over time, as measured by profitability, utility, and Sharpe Ratio indicators, relative to an industry benchmark.

Formation Control of a Multi-Unmanned Surface Vessel System: A Bibliometric Analysis

This study provides an overview of the literature on multi-unmanned surface vessel (multi-USV) systems, addressing the increasing attention on formation control of USVs due to their enhanced task execution ability, efficiency, and robustness in complex marine environments. Despite numerous studies on USVs covering fields, such as autonomous decision making, motion control, perception, and communication technologies, there is a significant lack of systematic literature review and bibliometric analysis specifically focused on a multi-USV system. This study aims to summarize advancements in multi-USV research, highlighting key aspects, including publication trends, influential scholars and papers, research hotspots, challenges, and future opportunities. By reviewing the current state of multi-USV research, this study contributes to the field as a beneficial reference for researchers, practitioners, and policymakers. It will not only highlight the progress made so far but also shed light on the gap that needs to be addressed to advance the field.

From vaccine hesitancy to vaccine motivation: A motivational interviewing based approach to vaccine counselling.

The COVID-19 pandemic highlighted Vaccine Hesitancy (VH) as an accelerating global phenomenon that must be addressed. According to the WHO, thirty to fifty percent of the world's population are VH. Motivational Interviewing (MI) is an evidence-based communication style demonstrated to significantly reduce VH. MI guides people toward change through the expression of empathy and by respecting an individual's autonomy. Healthcare providers (HCPs) are the primary implementors of vaccine policies and the most trusted advisors and influencers of vaccination intention at the individual patient level. Training HCPs in MI is one of the most effective strategies to overcome VH. Many countries are currently implementing HCP training programs and population-based MI interventions to improve vaccine uptake. MI conversations are 'the heart' of vaccine decision-making processes. Understanding individual patient-level drivers of hesitancy allows clinicians to efficiently provide tailored, accurate information that reinforces a person's own motivation and confidence in their own decision. This paper describes a 4-step practical framework designed to support HCPs in their dialogue with vaccine-hesitant patients. (1) Engaging to establish a trustful relationship and safety to freely express opinions, beliefs, and knowledge gaps; (2) Understanding what matters most to the individual; (3) Offering Information to co-build accurate knowledge in order to guide the individual toward vaccine intention (4) Clarifying and Accepting to validate an individual's decision-making autonomy. We believe that our pragmatic approach can contribute to greater acceptability of COVID-19 and other vaccines, and enable rapid deployment of practical MI skills across care systems.

Formation Cooperative Intelligent Tactical Decision Making Based on Bayesian Network Model

This paper proposes a method based on a Bayesian network model to study the intelligent tactical decision making of formation coordination. For the problem of formation coordinated attack target allocation, a coordinated attack target allocation model based on the dominance matrix is constructed, and a threat degree assessment model is constructed by calculating the minimum interception time. For the problem of real-time updating of the battlefield situation in the formation confrontation simulation, real-time communication between the UAV formation on the battlefield is realized, improving the efficiency of communication and target allocation between formations on the battlefield. For the problem of UAV autonomous air combat decision making, on the basis of the analysis of the advantage function calculation of the air combat decision-making model and a Bayesian network model analysis, the network model's nodes and states are determined, and the air combat decision-making model is constructed based on the Bayesian network. Our formation adopts the Bayesian algorithm strategy to fight against the blue side's UAVs, and the formation defeats the blue UAVs through coordinated attack, which proves the reasonableness of coordinated target allocation. An evaluation function is established, and the comprehensive scores of our formation are compared with those of other algorithms, which proves the accuracy and intelligibility of the decision making of the Bayesian network.