Autonomous Decision-making Systems Research Articles

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

IoT and fuzzy logic integration for improved substrate environment management in mushroom cultivation

The current era witnesses the advent of the Internet of Things (IoT), a transformative force giving rise to various technological innovations, most notably connected objects. This paradigm shift catalyzes the widespread adoption of autonomous decision-making systems, particularly in sectors like agriculture, where the aim is to amplify productivity. As a result of the agricultural domain, mushrooms have concurrently emerged as a significant component of daily diets, offering additional vitamins and flavor. Despite their popularity, cultivating mushrooms in open environments poses challenges in maintaining optimal environmental conditions, prompting numerous research efforts. Recognizing the inconsistencies in existing approaches to safeguard vital parameters in mushroom farms, this paper introduces an innovative system utilizing intelligent sensors whose real-time records are managed based on the fuzzy sets concept. These sensors, encompassing the Capacitive Soil Moisture Sensor v1.2, DHT22 Sensor, Light Dependent Resistor (LDR sensor), and Passive Infrared Receiver (PIR sensor), collectively capture essential data for decision-making in mushroom farming. Employing fuzzy logic, the system addresses pivotal aspects such as substrate watering, environmental control, light management, and pest detection. Through experimental results, it becomes evident that the proposed system not only exemplifies the potential of IoT technologies in agriculture but also offers a comprehensive and efficient approach to real-time decision-making. By aggregating sensor data, the system proves instrumental in enhancing the quality and yield of mushroom crops, showcasing a promising trajectory for sustainable and technologically driven agricultural practices.

Reliability-based leading edge erosion maintenance strategy selection framework

Leading edge erosion has become one of the most prevailing failure modes of wind turbines. Its effects can evolve from an aerodynamic modification of the properties of the blade to a potential structural failure of the leading edge. The first produces a reduction of energy production and the second can produce a catastrophic failure of the blade. Considering the uncertainties and constraints involved in the design of optimal operation and maintenance (O&M) strategies for offshore assets and the influence of site-specific parameters on the dynamics of this particular failure mode, the task becomes complex. In this study, a framework to evaluate the influence of different maintenance strategies considering uncertainties in weather, material behaviour and repair success is presented. Monte Carlo Simulation (MCS) is used alongside a computational framework for Leading Edge Erosion (LEE) degradation to evaluate the lifetime cost distribution and probability of failure of the chosen maintenance strategies. The use of the framework is demonstrated in a case study considering a 5-MW offshore wind turbine located in the north of Germany. The influence of the modification of the maintenance interval or time between repairs and the comparison with maintenance activities executed only during months with milder weather is analysed in terms of cost and reliability. A Pareto front plot considering the probability of failure and the median of the cost is used to jointly compare strategies considering both aspects to provide a tool for risk-informed maintenance selection. Finally, the potential benefits of condition-based maintenance and autonomous decision-making systems are discussed. The case of study shows the benefits of repairs during summer months and the importance of the relation risk/O&M cost for different maintenance strategies.

Nonlinear Transient Switching Filter for Automatic Buffer Window Adjustment in Short-term Ship Response Prediction

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution.

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.

Human involvement in autonomous decision-making systems. Lessons learned from three case studies in aviation, social care and road vehicles

This paper draws on three case studies to examine some of the challenges and tensions involved in the use of Autonomous Decision-Making Systems (ADMS). In particular, the paper highlights: (i) challenges around the shifting “locale” of the decision, and the associated consequences for stakeholders; (ii) potential implications for stakeholders from regulation such as the General Data Protection Regulation (GDPR); (iii) the different values that stakeholder groups bring to the “decision” question; (iv) how complex pre-existing webs of stakeholders and decision-making authorities may be disrupted or disempowered by the use of an automated system and the lack of evaluation of possible consequences; (v) how ADMS for non-technical users can lead to circumvention of the boundaries of intended system use. We illustrate these challenges through case studies in three domains: adult social care, aviation, and vehicle driver monitoring systems. The paper closes with recommendations for both practice and policy in the deployment of ADMS.

Autonomous collision avoidance system for unmanned vessels: algorithms and software.

Ship collision is one of the most substantial concerns in the global maritime transportation industry. Hence, navigation safety is considerably cited topic in maritime literature. Recently, Unmanned Navigation (UN) technology is gradually becoming more widely used across in the field of marine robotics. The paper investigates the problem of navigation safety in the movement control of Unmanned Vessels (UVs). The object of the study is the process of avoiding collisions of UVs. The subject of the research is the algorithms of the autonomous decision-making system and software for preventing vessel collisions during UN. The intent of this article is to improve the safety of UN by developing new Decision-Making algorithms for autonomous collision prevention of UVs in real time, taking into account the International Rules for the Prevention of Collisions at Sea, 1972 (COLREGs-72) and the recommendations of the Federal agency for sea and inland water transport of the Russian Federation (Rosmorrechflot).In this article, the fundamental concept and the key functions set of the Autonomous Collision Avoidance System (ACAS) are carried out for UVs which are marine transport vehicles capable of sensing its environment and operating without human involvement. Along this line of research, this work focuses on the development of a software algorithm for determining the most dangerous obstacle located within a radius of 12 miles (recommendations of Rosmorrechflot) around an UV based on the principle of vessels collision avoidance geometry, collision risk assessment and the characteristics of obstacles. Moreover, the proposed algorithms can prevent the collision and address the issues of real-time collision avoidance for UVs. The simulation results also demonstrate the promising application of the proposed algorithms in studying the UN safety. Nonetheless, this study provides a way forward to conduct a new information decision-making system design for UVs collision avoidance. This is currently under development, and will be proposed later.

Java-Powered AI: Implementing Intelligent Systems with Code

The fusion of Artificial Intelligence (AI) and Java programming offers a powerful synergy, enabling developers to create intelligent systems and applications with efficiency, robustness, and scalability. This paper explores the amalgamation of Java's versatility and AI's cognitive capabilities, presenting various techniques, libraries, and methodologies that leverage Java's strengths in building AI-driven solutions. The paper commences with an overview of AI concepts and the landscape of Java's role in AI development. It delves into fundamental AI algorithms, such as machine learning, natural language processing (NLP), computer vision, and reinforcement learning, elucidating their implementation in Java through frameworks like Deeplearning, Weka, and Apache OpenNLP. Furthermore, it discusses the utilization of Java in crafting intelligent agents and exploring techniques for creating autonomous decision-making systems, expert systems, and heuristic-driven algorithms. It highlights the integration of Java with AI-enabled tools, emphasizing the importance of data preprocessing, feature engineering, and model deployment. Moreover, the paper examines the challenges and opportunities in Java-based AI development, addressing concerns related to performance optimization, compatibility with diverse data sources, and the interoperability of AI modules. Finally, the paper concludes with a glimpse into the future of Java-powered AI, envisioning advancements in Java libraries, frameworks, and methodologies that will foster the creation of more sophisticated, intelligent systems.

Bio-inspired artificial somatic index for reflecting the travel experience of passenger agents under a flexible transportation scenario

This work analyzes the implementation of an artificial mechanism inspired by a biological somatic marker that ables a passenger agent to both, react to changes in the service, as well as keep said reactions as a memory for future decisions. An artificial mental model was designed, and the passenger agent was implemented as an autonomous decision-making system, where both, the choice of the transport operator and the evaluation of the received service were fully delegated to the system. The evaluation of the service experience is not only based on rational aspects (such as the cost of the trip) but also on subjective aspects related to the satisfaction level derived from the passenger's experience. The experimental scenario considered 10,000 trip requests simulated within an artificial map that emulates characteristics that are usually present in a city, such as vehicular congestion, the unsafety of certain streets, or the benefits of an area with tourist interest. The results show that the option to travel under a transport operator with a touristic profile is a trend. Unlike current cases in the industry, this research work explores the scenario where the passenger can have as a client a trip profile with memory, differentiated from other clients, and can receive more than one trip proposal for the same trip request, according to the different conditions that the passenger is looking for.

A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty

Ship autonomous collision avoidance has attracted increasing attention in recent years. However, more attention is paid to the scenario in which the target ship keeps its course and speed. Less attention is paid to the development of a collision avoidance decision-making system under the uncertainty of the target ship's movement in a complex multi-ship encounter situation. Based on the idea of model predictive control (MPC), this paper proposes an autonomous ship collision avoidance decision-making system (CADMS) suitable for the uncertainty of ship motion. The CADMS includes four modules: collision risk analysis module, control and execution module, ship trajectory prediction module and collision avoidance decision-making model. The proposed model can be implemented in the collision avoidance decision-making system for safe navigation or it can be included in the ship autonomous navigation process. The decision-making model is achieved from a risk identification-motion prediction-ship control-scheme implementation perspective, and the dynamic and uncertainty features of the ship action (i.e., alter course or change speed) are considered in the modelling process. The real-time rolling update of ship collision avoidance decisions is realised based on the time series rolling method. Four scenarios are designed to demonstrate the collision avoidance decision-making system's performance. The results show that the proposed collision avoidance decision-making system is a reasonable and effective system for collision avoidance, particularly in multi-ship encounter situations of target ships suddenly altering course or change speed.

A novel maritime autonomous navigation decision-making system: Modeling, integration, and real ship trial

Maritime Autonomous Surface Ships (MASS) have obtained wide attention in recent years, which is relied on their attractive socio-economic benefits and potential to improve safety. One of the main characteristics of MASS is that it can use perception information to replace Officers on Watch (OOW) to make different navigation decisions through expert and intelligent systems, so as to make navigation more intelligent, reliable and safer, which is mainly based on the Maritime Autonomous Navigation Decision-making System (MANDS). However, the state-of-the-art study lacks a systematic description and research on MANDS, and most of them mainly focus on solving a single navigation decision-making task, which is not aligned with the meaning of autonomous navigation. Therefore, this study develops a novel MANDS and installs it on a 300TEU intelligent container ship to successfully carry out real ship trials of autonomous navigation. In this paper, the definition of autonomous navigation is first proposed, which mainly contains four different navigation tasks, namely “Keep route”, “Route Optimization”, “Collision Avoidance”, “Return Back”. A well-designed architecture for MANDS is presented which includes corresponding decision-making models and communication interfaces with external systems. Furthermore, in order to realize the switching and deployment between different navigation tasks in the process of autonomous navigation, a novel hierarchical decision-making mechanism is proposed and analyzed, and finally the MANDS is integrated with perception system and ship control system respectively. Before the real ship trial, this system is tested and verified in a simulation-based environment which is composed of the electronic chart platform and ship motion simulator, we also design a scenario library for the testing work. At last, real ship trials of autonomous navigation for MANDS are carried out, which can convincingly prove the performance of MANDS. This study proposes a relative complete solution for autonomous navigation, including system modelling, integration and real ship trial. These findings offer new insight into the research of ship autonomous navigation and promote the improvement of ship intelligence and informatization level.

Ten years after ImageNet: a 360° perspective on artificial intelligence.

It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved-provided we have enough high-quality labelled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modelling has come to the fore. The rise of attention networks, self-supervised learning, generative modelling and graph neural networks has widened the application space of AI. Deep learning has also propelled the return of reinforcement learning as a core building block of autonomous decision-making systems. The possible harms made possible by new AI technologies have raised socio-technical issues such as transparency, fairness and accountability. The dominance of AI by Big Tech who control talent, computing resources, and most importantly, data may lead to an extreme AI divide. Despite the recent dramatic and unexpected success in AI-driven conversational agents, progress in much-heralded flagship projects like self-driving vehicles remains elusive. Care must be taken to moderate the rhetoric surrounding the field and align engineering progress with scientific principles.

Technologies Driving the Shift to Smart Farming: A Review

As today’s agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment, and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading farming practices by shifting toward smart farming (SF)—utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify suitable technologies and best practices for a particular application. This article provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers’ contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, this article discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

An Autonomous Uncrewed Aircraft System Performing Targeted Atmospheric Observation for Cloud Seeding Operations

This paper presents the results of a 3-week-long field deployment of an autonomous uncrewed aircraft system for targeted observation of early-convective storm systems in the U.S. Great Plains with application to cloud seeding operations. Due to reduced operational costs and requirements, autonomous small uncrewed aircraft systems present an appealing alternative to traditional crewed aircraft. The objective of the system is to gather and ultimately act upon in situ atmospheric data that are inaccessible via remote sensing techniques. Utilizing a combination of remote and in situ weather data, a dispersed autonomous decision-making system works integrally with a human operator to investigate early-convective storms for subregions which have favorable conditions for cloud seeding. The autonomy framework enables one operator to interface with multiple aircraft, which is demonstrated by performing complex sensing and seeding maneuvers with a team of two aircraft. Results from nine flights totaling over 8 hours of flight time are presented and discussed. Although the release of actual cloud seeding material was not performed during the campaign, this study demonstrates the utility and feasibility of small uncrewed aircraft systems for use in airborne cloud seeding operations.

Emergent Intelligence in Smart Ecosystems: Conflicts Resolution by Reaching Consensus in Resource Management

A new emergent intelligence approach to the design of smart ecosystems, based on the complexity science principles, is introduced and discussed. The smart ecosystem for resource management is defined as a system of autonomous decision-making multi-agent systems capable to allocate resources, plan orders for resources, and to optimize, coordinate, monitor, and control the execution of plans in real time. The emergent intelligence enables software agents to collectively resolve conflicts arising in resource management decisions by reaching a consensus through a process of detecting conflicts and negotiation for finding trade-offs. The key feature of the proposed approach is the ontological model of the enterprise and the method of collective decision-making by software agents that compete or cooperate with each other on the virtual market of the digital ecosystem. Emergent intelligent systems do not require extensive training using a large quantity of data, like conventional artificial intelligence/machine learning systems. The developed model, method, and tool were applied for managing the resources of a factory workshop, a group of small satellites, and some other applications. A comparison of the developed and traditional tools is given. The new metric for measuring the adaptability of emergent intelligence is introduced. The performance of the new model and method are validated by constructing and evaluating large-scale resource management solutions for commercial clients. As demonstrated, the essential benefit is the high adaptability and efficiency of the resource management systems when operating under complex and dynamic market conditions.

An adaptive decision-making system supported on user preference predictions for human–robot interactive communication

Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this context, a social robot that adapts its behaviour to different users and proactively suggests their favourite activities may produce a more successful interaction. In this work, we describe how the autonomous decision-making system embedded in our social robot Mini can produce a personalised interactive communication experience by considering the preferences of the user the robot interacts with. We compared the performance of Top Label as Class and Ranking by Pairwise Comparison, two promising algorithms in the area, to find the one that best predicts the user preferences. Although both algorithms provide robust results in preference prediction, we decided to integrate Ranking by Pairwise Comparison since it provides better estimations. The method proposed in this contribution allows the autonomous decision-making system of the robot to work on different modes, balancing activity exploration with the selection of the favourite entertaining activities. The operation of the preference learning system is shown in three real case studies where the decision-making system works differently depending on the user the robot is facing. Then, we conducted a human–robot interaction experiment to investigate whether the robot users perceive the personalised selection of activities more appropriate than selecting the activities at random. The results show how the study participants found the personalised activity selection more appropriate, improving their likeability towards the robot and how intelligent they perceive the system. query Please check the edit made in the article title.

Artificial intelligence and decision problems: The need for an ethical context

<p>Computers process information and make decisions. Until recently, the decisions they made were not complex, but due to the incessant technological advances that are taking place, systems based on artificial intelligence are achieving levels of competence in decision-making that in many contexts equal or surpass those of humans. These are autonomous decision-making systems that, although they can increase the capacity and efficiency of people in their fields of action, they could also replace them, something that is of concern to society as a whole. Avoiding dysfunctions in these systems is a priority social, scientific and technological objective, which requires theoretical models that include all the richness and variety of decision problems, that precisely define the elements that characterize them and that address the ethical principles that should guide their operation. This article describes each of these aspects in separate sections.</p>

Artificial intelligence and decision problems: The need for an ethical context

<p>Computers process information and make decisions. Until recently, the decisions they made were not complex, but due to the incessant technological advances that are taking place, systems based on artificial intelligence are achieving levels of competence in decision-making that in many contexts equal or surpass those of humans. These are autonomous decision-making systems that, although they can increase the capacity and efficiency of people in their fields of action, they could also replace them, something that is of concern to society as a whole. Avoiding dysfunctions in these systems is a priority social, scientific and technological objective, which requires theoretical models that include all the richness and variety of decision problems, that precisely define the elements that characterize them and that address the ethical principles that should guide their operation. This article describes each of these aspects in separate sections.</p>

Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations.

The application of artificial intelligence (AI) not only yields in advantages for healthcare but raises several ethical questions. Extant research on ethical considerations of AI in digital health is quite sparse and a holistic overview is lacking. A systematic literature review searching across 853 peer-reviewed journals and conferences yielded in 50 relevant articles categorized in five major ethical principles: beneficence, non-maleficence, autonomy, justice, and explicability. The ethical landscape of AI in digital health is portrayed including a snapshot guiding future development. The status quo highlights potential areas with little empirical but required research. Less explored areas with remaining ethical questions are validated and guide scholars' efforts by outlining an overview of addressed ethical principles and intensity of studies including correlations. Practitioners understand novel questions AI raises eventually leading to properly regulated implementations and further comprehend that society is on its way from supporting technologies to autonomous decision-making systems.

Integration of ROS and Tecnomatix for the development of digital twins based decision-making systems for smart factories

Digital twins employs simulation in conjunction with virtual environments and a variety of data coming from different plant equipment and physical systems to continuously update the digital models of the world in a feedback loop scheme to facilitate the decision-making processes. The heterogeneity of existing hardware and software requires the development of software architectures able to deal with the information exchange due to the integration and interaction of several system components and autonomous decision-making systems. In this work we propose the design and construction of a software architecture that integrates a manufacturing process simulator with the well-known robot operating system (ROS-Robot Operating System) to easily interchange information with an autonomous decision-making system. The proposal is tested with the simulator Tecnomatix and the free distribution ROS Melodic. We present an instance of software architecture for a typical complex case study of manufacturing plants and demonstrate its easy integration with an autonomous decision-making system based on the reinforcement- learning paradigm.