Decision Situations Research Articles

MEASURES OF INFLUENCE FOR VIOLATION OF FINANCIAL MONITORING BY BANKS IN UKRAINE

The research examines the organizational principles of building a system of influence measures for violations of financial monitoring by banks in Ukraine, analysis of fines imposed on banks in the field of financial monitoring, and judicial practice of challenging the applied influence measures for the period from 2018 to 2023. This subject of research was chosen due to the impossibility of effective functioning of the national system of financial monitoring without an effective regulation mechanism. An integral part of market regulation is the implementation of influence measures adequate to the committed violation. The application of influence measures and control over the implementation of corrective actions contribute to the strengthening of the national financial monitoring system. The relevance of the study is determined by the heterogeneity of the functioning of financial monitoring systems in different jurisdictions, the involvement of a wide range of reporting entities in this process, the constant development in the field of financial monitoring due to the digital transformation of the provision of financial services. A wide list of influence measures, which the financial regulator has the right to apply to banks according to Ukrainian legislation, was revealed, including a ban on carrying out certain types of operations, removing management from their positions, classifying the bank as problematic or liquidating the bank. However, in practice, the use of influence measures in the form of written warnings and fines prevailed. The National Bank of Ukraine, as a rule, applied fines in the case of problems of banks with the implementation of measures for customer due diligence and poor banking analysis of financial transactions of clients. The ultimate goal of most schemes, which, according to the National Bank of Ukraine, violated the legislation in the field of financial monitoring, was the conversion of non-cash funds into cash. Banks appealed the decision of the financial regulator only in case of significant fines. At the same time, the courts made different decisions in similar situations, which indicates the absence of a unified position of the judicial system regarding the legality of the application of fines to banks for violations in the field of financial monitoring.

Онтолого-орiєнтована мультиагентна система для децентралiзованого керування групою БПЛА

Introduction. Today, UAVs are becoming an increasingly important tool for performing complex tasks in various fields of application, both civil (economic) and military, as they are particularly effective in dynamically uncertain environments with hard-to-reach areas. In addition, technological advances such as blockchain, artificial intelligence (AI) and machine learning have enabled the development of updated and improved UAV systems. To create and deploy a swarm of UAVs, coordinate actions, manage, and exchange data, a model of a multi-agent system (MAC) based on an ontological representation of knowledge is proposed. This model enables a swarm of UAVs to effectively make decisions in various situations while performing assigned tasks. This approach enables the safety, reliability, and efficiency of the tasks of the UAV group. The purpose of the paper is to develop further the theoretical and practical foundations of the integration of the multi-agent system (MAS) based on the ontological representation of knowledge with the UAV network. This involves the development of a MAS architecture and a hierarchical set of ontologies of different levels. The goal is to create a common data description lan guage, define data semantics to ensure data uniqueness and consistency, provide support for decision-making during UAV swarm management, and swarm survivability in the event of aircraft failures or loss. It is necessary to develop algorithms and a method of dividing a complex task into sub-tasks in a swarm of UAVs among all MAS agents. This is to ensure reliable exchange of messages (data) between agents during the joint performance of the assigned task, and the possibility of dynamic redistribution of roles between UAV agents as needed. Methods. During the research, the general theory of intelligent information technologies was applied; agent theory methods in particular intelligent BDI agents; methods of analyzing the performance of wireless data exchange networks; theory of combinatorial optimization for dividing tasks into subtasks; methods of ontological analysis and descriptive logic to create an ontological hierarchical model of the subject area; methods of enriching ontological models from external semantically marked information resources. Results. As a result of the performed scientific research, the MAS architecture was proposed and its main functions were determined for the decentralized control of a swarm of UAVs. A set of agents with assigned roles was formed, who jointly (cooperatively) perform tasks, exchanging messages, and information with each other, which ensures the survivability of the system (in case of a failure or loss of the device, its task must be distributed among other drones). Plans and scenarios of MAS actions for various situations and means of coordinating actions between agents have been developed to perform the mission by a swarm of UAVs. A hierarchical ontological model of the subject area related to the work of the UAV swarm has been created. The algorithms and methods were based on the integration of semantic technologies that support the MAS during the execution of the UAV swarm mission, decision-making, assessment of the dynamic environment, and response to its changes. Conclusions. An original approach, algorithms, and method for improving the system of decentralized control of a group of UAVs were proposed. Expanding the functionality of the system for maintaining the interaction of a swarm of unmanned systems based on MAS artificial intelligence was suggested. This system was based on ontological models. The models describe knowledge of the subject area, processes of UAV swarm operation, scenarios of actions in difficult situations, distribution of roles to agents, principles of planning, and coordination. The proposed MAS is integrated with the UAV swarm software platform, which makes it possible to improve the efficiency of the decentralized control system and adapt UAVs to dynamic changes in the environment. The practical result of the work will be a prototype of a software agent system that interacts with ontologies while performing simple tasks. The economic significance of the work consists of focusing on the creation of new intelligent information technologies, which were based on AI and knowledge of the subject area, and this significantly increases the efficiency of the functioning of modern systems. Keywords: multi-agent system, ontology, formalization of knowledge, UAV, drone, decentralized control, task allocation.

Critical Thinking as a Pedagogical Category: A Comparative Aspect

The article is dedicated to a comparative analysis of critical thinking as a pedagogical category in the works of Ukrainian and foreign researchers. Critical thinking is considered as a necessary component of modern education aimed at developing students' abilities for analysis, synthesis, and making informed decisions in various life situations. Various approaches to the development of critical thinking in education in Ukraine and abroad are analyzed; common and distinctive features in the scientific approaches in the works of Ukrainian and foreign researchers are identified. During the research, methods of comparative analysis, systematic approach, synthesis, and generalization of scientific and pedagogical literature were applied. It is concluded that the development of critical thinking is a key factor in improving the quality of education. It has been proven that the integration of the best foreign practices into Ukrainian educational programs will contribute to the preparation of highly qualified specialists capable of adapting to the conditions of a globalized world. The prospects for further research are outlined, including the development of innovative techniques for the development of critical thinking, taking into account the specifics of Ukrainian education and the needs of modern students.

Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios

This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem’s convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark.

Integrating Aquaculture to Support STEM Education: A Qualitative Assessment to Identify High School Students’ Attitudes, Interests, and Experiences

This study explored the impact of an active project-based, aquaculture constructivist-learning program, as perceived by high school students. The purpose of this case study was to discover if participation in the program influenced students’ interest, engagement, and future educational and career aspirations in science, technology, engineering, and mathematics (STEM) when integrating aquaculture in and outside the classroom. Likewise, the study also wanted to explore students’ knowledge about aquaculture and skill development after their participation in the program. The study employed a qualitative methods approach to explore students’ attitudes and experiences. Qualitative data were collected from post-student focus groups at three different public, rural high schools in Kentucky. Other qualitative data included teacher journal reflections (e.g., personal documents) and public newspaper articles (e.g., public documents). Four emergent themes were found: (1) Students show excitement and enthusiasm in the hands-on, aquaculture program; (2) students show attention to detail in the hands-on, aquaculture tasks, it sticks, and are more responsible; (3) students are collaboratively engaged with their peers; and (4) greater interest and confidence in STEM through practical application. Results demonstrated that the program engaged learners in real-world problem-solving and decision-making situations while working collaboratively in small works. It also appears that students gained an important life skill, responsibility, as well as self-confidence in STEM, after participating in the program.

Automation bias in public administration – an interdisciplinary perspective from law and psychology

The objective of this paper is to break down the widely presumed dichotomy, especially in law, between fully automated decisions and human decisions from a psychological and normative perspective. This is particularly relevant as human oversight is seen as an effective means of quality control, including in the current AI Act. The phenomenon of automation bias argues against this assumption. We have investigated this phenomenon of automation bias, as a behavioral effect of and its implications in normative institutional decision-making situations. The phenomenon of automation bias, whereby individuals overly rely on machine-generated decisions or proposals, has far-reaching implications. Excessive reliance may result in a failure to meaningfully engage with the decision at hand, resulting in an inability to detect automation failures, and an overall deterioration in decision quality, potentially up to a net-negative impact of the decision support system. As legal systems emphasize the role of human decisions in ensuring fairness and quality, this paper critically examines the inadequacies of current EU and national legal frameworks in addressing the risks of automation bias. Contributing a novel perspective, this article integrates psychological, technical, and normative elements to analyze automation bias and its legal implications. Anchoring human decisions within legal principles, it navigates the intersections between AI and human-machine interactions from a normative point of view. An exploration of the AI Act sheds light on potential avenues for improvement. In conclusion, our paper proposes four steps aimed at effectively countering the potential perils posed by automation bias. By linking psychological insights, legal analysis, and technical implications, this paper advocates a holistic approach to evolving legal frameworks in an increasingly automated world.

’Did we do our best?’ Reviewing person-centred compassionate care at the end of life in an acute hospital in the United Kingdom, during the COVID-19 pandemic. An Appreciative Inquiry

ABSTRACT There is an outstanding task for all international healthcare organisations to understand how healthcare services learn from the COVID-19 pandemic. We sought to understand whether the treatment and holistic care received by each person who died with presumed nosocomial COVID-19 in our United Kingdom hospital was provided with kindness and compassion. A multi-professional team carried out a service evaluation underpinned by Appreciative Inquiry (AI) methodology. Our current Learning from Death data collection tool was supplemented by the addition of narrative analysis to augment our qualitative findings. We identified a total of 63/143 patients as dying from presumed nosocomial COVID-19 in our hospital. The deaths occurred in hospital between 15 April 2020 and 29 January 2021. We found evidence of individualised decision-making in complex medical situations, alongside the challenge of managing COVID-19, with appropriate management of deterioration and appropriate symptom control. A Learning from Death programme benefits from a multi-professional approach that utilises the skilled workforce mix that exists in our hospitals. The use of AI methodology and addition of narrative analysis is practical and adds valuable qualitative data to the scrutiny of deaths. Sharing the narrative of those who died during the COVID-19 pandemic has reinforced the evidence that healthcare professionals strive to do their best even in the most challenging circumstances and do so with compassion.

Computer Simulators - Modern Tools For Training Future Officers And Sergets

The purpose of this article was to consider the prerequisites for the emergence, stages and prospects for the development of military computer games in the Republic of Kazakhstan, used in combat training of the armed forces of a number of states, as well as an attempt to conduct a comparative assessment of various military simulators using developed military computer programs. Simulators of military weapons at the present stage play an important role in preparing future reserve officers and sergeants in the Republic of Kazakhstan for real combat operations. They are computer programs or physical devices that help simulate various aspects of military combat activities. One of the main advantages of using computer simulations is the ability to create realistic combat conditions without having to risk lives and resources. Future officers and NCOs can enhance their professional skills through simulations to develop strategic and tactical skills, make real-time decisions and coordinate the actions of their units. In our opinion, the use of military weapons simulators provides an opportunity to gain excellent skills in working with military weapons, reduces training time for beginners, improves reactions and quick decision-making, and of course reduces the risk of injury during training. So, simulators of weapons and military equipment will contribute to the training of future reserve officers and sergeants, helping to develop their skills, leadership qualities and make effective decisions in combat situations. A modern approach effectively complements traditional military training.

Cognitive and mnemonic abilities in a trust game

Individual mnemonic abilities can play an important role in determining individual choices, especially in games that involve numerous decision-making situations in which the consistency of decisions depends on the information set retained by the subjects. In similar contexts it would be useful to distinguish the impact of cognitive abilities from mnemonic abilities on choices. We provide empirical evidence on the importance of providing cumulative information flow in games of the type described above.

A Study on the Accuracy of Micro Expression Based Deception Detection with Hybrid Deep Neural Network Models

This article details a study on enhancing deception detection accuracy by using Hybrid Deep Neural Network (HDNN) models. The research, focusing on fear-related micro-expressions, utilizes a diverse dataset of responses to high-stakes questions. It analyzes facial action units (AUs) and pupil size variations through data preprocessing and feature extraction. The HDNN model outperforms the traditional Convolutional Neural Network (CNN) with a 91% accuracy rate. The findings’ implications for security, law enforcement, psychology, and behavioral treatments are discussed. Ethical considerations of deception detection technology deployment and future research directions, including cross-cultural studies, real-world assessments, ethical guidelines, studies on emotional expression dynamics, “explainable AI” development, and multimodal data integration, are also explored. The study contributes to deception detection knowledge and highlights the potential of machine learning techniques, especially HDNN, in improving decision-making and security in high-stakes situations.

Individual differences in the discounting of combination outcomes in which immediate gains are followed by delayed losses.

The vast majority of studies on discounting have focused on simple delayed outcomes, but most everyday decisions are more complicated. The present experiment focused on one such scenario, an iconic self-control situation in which immediate gains are followed by delayed losses. The same participants were studied in all conditions to permit examination of individual differences in choice behavior using intercorrelations and factor analysis. Consistent with previous research, the hyperboloid model accurately described the form of the discounting function and discounting was not affected by the amount of the delayed loss when it was presented alone. However, replicating other studies, smaller delayed losses were discounted more steeply than larger ones when presented in combination with immediate gains. Exploratory factor analysis revealed two factors, one loading primarily on loss-only conditions and the other loading primarily on conditions involving outcomes that combined gains and losses. These results imply that there are individual differences in how one combines gains and losses and that this characteristic of individual decision making might be an important predictor of decisions in the many everyday choice situations that involve complex outcomes.

The top tier of the Big Five does not predict police decisions in ambiguous and high-pressure situations

The top tier of the Big Five does not predict police decisions in ambiguous and high-pressure situations

Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.

Background: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients' electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on "patient age (at primary diagnosis)" and "date of first diagnosis" were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed.

On the Construct of Subjective Risk Intelligence and Its Relationships with Personality, Emotional Intelligence and Coping Strategies: A Comparison between Adolescents and Adults.

The complexity of today's scenario has made it necessary to investigate the need for individuals to make choices that entail increasing exposure to risk and uncertainty. Among the individual resources that could help people to cope with situations of uncertainty, the new construct of subjective risk intelligence (SRI), known as a person's ability to effectively weigh the pros and cons of a decision in situations where not all the outcomes are foreseen, would seem to play a prominent role. Considering that personality and coping strategies have been shown to be significantly related in previous research, the present study investigates the relationships between subjective risk intelligence, emotional intelligence, personality traits and coping strategies in both adults and adolescents. This cross-sectional study was conducted on 1390 Italian people, divided into two subsamples of 641 adolescents and 749 adults. The results showed that SRI mediated the roles that personality traits and emotional intelligence have in coping strategies differently in the adult sample, in which the mediating role of SRI was found for avoidance coping, and in the adolescent sample, in which SRI influenced all of the antecedents analysed in the study for almost all of the identified coping strategies. In light of these findings, subjective risk intelligence could be activated to deal with uncertain and risky situations, influencing the choice of effective or ineffective strategies in both adults and adolescents.

Stochastic calculus-guided reinforcement learning: A probabilistic framework for optimal decision-making

Stochastic Calculus-guided Reinforcement learning (SCRL) is a new way to make decisions in situations where things are uncertain. It uses mathematical principles to make better choices and improve decision-making in complex situations. SCRL works better than traditional Stochastic Reinforcement Learning (SRL) methods. In tests, SCRL showed that it can adapt and perform well. It was better than the SRL methods. SCRL had a lower dispersion value of 63.49 compared to SRL's 65.96. This means SCRL had less variation in its results. SCRL also had lower risks than SRL in the short- and long-term. SCRL's short-term risk value was 0.64, and its long-term risk value was 0.78. SRL's short-term risk value was much higher at 18.64, and its long-term risk value was 10.41. Lower risk values are better because they mean less chance of something going wrong. Overall, SCRL is a better way to make decisions when things are uncertain. It uses math to make smarter choices and has less risk than other methods. Also, different metrics, viz training rewards, learning progress, and rolling averages between SRL and SCRL, were assessed, and the study found that SCRL outperforms well compared to SRL. This makes SCRL very useful for real-world situations where decisions must be made carefully.•By leveraging mathematical principles derived from stochastic calculus, SCRL offers a robust framework for making informed choices and enhancing performance in complex scenarios.•In comparison to traditional SRL methods, SCRL demonstrates superior adaptability and efficacy, as evidenced by empirical tests.

DEVELOPMENT OF ECOLOGICAL EDUCATIONAL MECHANISMS IN HIGHER EDUCATION AS A SOCIAL NEED AND NECESSITY

In the article, the importance to improve the environmental education and culture of future specialists studying in higher educational institutions, to ensure their environmental competence, and to develop the skills of making rational decisions in environmental life situations was justified. In this regard, education, environmental education, concepts of mechanisms, identification of mechanisms of environmental education as components, perceptionof the environment, conscious attitude towards nature, sublimation mechanisms were analyzed in detail.

Медицинская реабилитация: исследование проблем этического и правового характера

Medical rehabilitation is currently in an active phase of its development. This relevant area of domestic medicine is essential for human health. It helps patients recover from long-term illnesses, effects of injuries and diseases of the musculoskeletal system, peripheral nervous system and has a huge impact on the prognosis and outcome of treatment in general. This publication provides a comprehensive analysis of the ethical aspects of medical rehabilitation with an emphasis on legal definitions in medicine, which will improve understanding and regulation of relationship between rehabilitation, prevention and treatment. The research includes the study of domestic and international regulatory legal acts concerning medical rehabilitation, history of the specialty, the formulations that laid the foundation for the concept further development, consolidating an integrated approach to the issue of medical rehabilitation, as well as review of the problem-associated scientific papers. Explaining the principles of medical rehabilitation will help doctors avoid legal risks associated with their professional activity and serve as a guideline for taking ethically sound decisions in difficult clinical situations, whereas patients will get a full picture of their rights within the framework of medical rehabilitation.

Precision Targeting Companion: Empowering Marksmen with Smart Rifle Scopes

In the realm of modern warfare, the ability to swiftly identify and engage adversaries with precision is indispensable. This paper outlines our objective to contribute to this paradigm shift by undertaking a comprehensive exploration, and design, of accessories crucial for rifles, including smart scopes, auto- matic triggers, and intelligent magazines. Moreover, this paper introduces a groundbreaking innovation - the Precision Targeting Companion, an intuitive system integrated into rifle scopes, aimed at elevating the effectiveness and accuracy of marksmanship in dynamic combat scenarios. At its core, this system is a testament to the fusion of advanced technology with human ingenuity. By harnessing the power of cutting-edge image processing and predictive analytic, the Precision Targeting Companion offers marksmen a seamless and intuitive interface for detecting potential threats and predicting bullet trajectories in real-time. Yet, beyond its computational prowess, this system embodies a deeper understanding of the challenges faced by soldiers and law enforcement officers on the front-lines. It is a trusted companion, empowering users with the confidence to make split-second decisions in high-pressure situations. Key features of the Precision Targeting Companion include: 1. Swift identification of threats: Leveraging sophisticated image processing algorithms, the system swiftly identifies adver- saries within the user’s line of sight, providing crucial situational awareness. 2. Predictive guidance: Through intricate calculations and analysis, the system predicts bullet trajectories, enabling marksmen to make informed decisions and engage targets with precision. 3. Intuitive assistance: The system’s user-friendly interface overlays visual cues on detected targets and cross-hair alignment, offering intuitive guidance and facilitating seamless target tracking. In essence, the Precision Targeting Companion represents more than just a technological advancement; it is a testament to the enduring partnership between man and machine, working in harmony to safeguard lives and achieve mission success on the battlefield. Keywords: Precision Targeting Companion, Smart Rifle Scopes, Human-Centric Design, Tactical Engagement, Threat Detection, Predictive Guidance.

TraumaFlow-development of a workflow-based clinical decision support system for the management of severe trauma cases.

The treatment of severely injured patients in the resuscitation room of an emergency department requires numerous critical decisions, often under immense time pressure, which places very high demands on the facility and the interdisciplinary team. Computer-based cognitive aids are a valuable tool, especially in education and training of medical professionals. For the management of polytrauma cases, TraumaFlow, a workflow management-based clinical decision support system, was developed. The system supports the registration and coordination of activities in the resuscitation room and actively recommends diagnosis and treatment actions. Based on medical guidelines, a resuscitation room algorithm was developed according to the cABCDE scheme. The algorithm was then modeled using the process description language BPMN 2.0 and implemented in a workflow management system. In addition, a web-based user interface that provides assistance functions was developed. An evaluation study was conducted with 11 final-year medical students and three residents to assess the applicability of TraumaFlow in a case-based training scenario. TraumaFlow significantly improved guideline-based decision-making, provided more complete therapy, and reduced treatment errors. The system was shown to be beneficial not only for the education of low- and medium-experienced users but also for the training of highly experienced physicians. 92% of the participants felt more confident with computer-aided decision support and considered TraumaFlow useful for the training of polytrauma treatment. In addition, 62% acknowledged a higher training effect. TraumaFlow enables real-time decision support for the treatment of polytrauma patients. It improves guideline-based decision-making in complex and critical situations and reduces treatment errors. Supporting functions, such as the automatic treatment documentation and the calculation of medical scores, enable the trauma team to focus on the primary task. TraumaFlow was developed to support the training of medical students and experienced professionals. Each training session is documented and can be objectively and qualitatively evaluated.

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.