Machine Autonomy Research Articles

Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)

Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly changing character of their resource supply, efficient scheduling solutions must be developed to oversee these resources and meet the objectives of both cloud service providers and cloud customers. Hence an algorithm called Task Scheduling Optimization based Genetic Algorithm (TSOGA) has been proposed to dynamically allocate the resources in pursuit of scheduling the tasks in cloud data centers. The proposed module initially focuses on task scheduling process, followed by optimized running time of task execution. For data centres with dynamic resource allocation, the goal of TSOGA is to efficiently assign jobs to resources while minimizing execution time and optimizing resource utilization. Thus, to manage the data centres while achieving high levels of efficiency in resource allocation, we constructed a virtual node for our research. Incorporation of Genetic Algorithm is to determine an ideal or nearly ideal schedule for carrying out tasks using the available resources while taking into account a variety of restrictions and goals, such as minimizing execution and waiting time of task during dynamic scheduling process and efficient resource utilization.

The Use of Sensors in Complement to Industry 4.0 Technologies to Optimize Production Operations An analysis of the industry technologies and model for their relations with optimized production

Industry 4.0 is the revolution of the industry where all machines and their systems are combined together through complex networks in order to achieve higher degree of machine autonomy. Now there are 9 major technological advancements that marks i4.0’s advent. Out of these, 3 (Sensor, AI, AR/VR) have been taken as the most easily industry assimilable and thus their influence on the efficiency of manufacturing industry has been studied in the paper and an Informal model has been presented showing how they positively effect lean production in a modern industry and how their complexity cost and Advancements have a moderating impact on the industry and lean production. Keywords: Industry 4.0, AR and VR, AI, Sensors, Lean Automation, Lean Optimization, Cyber-Physical systems

Proxy responsibility: addressing responsibility gaps in human-machine decision making on the resort to force

ABSTRACT The integration of AI in resort-to-force decision making gives rise to substantial threats and problems. One significant challenge is that incorporating a machine into the decision-making process can result in responsibility gaps for decisions informed or made by the machine. Ethically, a situation in which lethal violence can be employed without a responsible subject to blame for any wrongdoing is unacceptable. But how can responsibility be attributed if a machine is involved in the decision-making process on war and peace? To address this question, I introduce the concept of ‘proxy responsibility’. I contend that since we cannot ascribe moral responsibility to the AI artefact itself, we must identify responsibility relations in the structures in which AI decision making operates. A dynamic and contextual concept of responsibility positions AI in the broader decision-making process of the political, military, and economic system, and helps to unfold different responsibility layers among the involved actors. I argue that the more we move in the direction of machine autonomy, the denser the web of proxy responsibility relations in the environment of AI must become to address the aforementioned gaps in responsibility.

Research on human-computer interaction and user experience of smart home system

This article aims to present a perceptive summary of cutting-edge home automation systems, exploring basic topics including human-computer interaction, communication technologies, networking protocols, and the fascinating science underlying the industry's most well-known applicationthe robotic vacuum cleaner. Put succinctly, using intelligent sweeping robots in smart homes signifies a significant advancement in machine autonomy and artificial intelligence. Furthermore, integrating renewable energy and grid management solutions with smart home systems will be fueled by the pursuit of sustainability and energy efficiency. Ultimately, exploring the intelligent home system's core has shed light on technology and its revolutionary effects on our day-to-day existence. As we stand on the edge of a better future, the convergence of connection, communication, and interaction invites us to embrace innovation and push what is possible in our personal lives.

Ready or Not? Examining Acceptance and Fears of Robots in the Labor Market: A Survey of a Polish Sample

The purpose of the study is to assess attitudes toward robots among a Polish sample (N = 1044) using a series of questions focused on their perceptions within the labor market. Based on previous research, higher concerns toward and lower acceptance of robots were predicted for women, people performing manual and manual work, and people who are not familiar with robotics. The hypotheses were only partially confirmed. Orientation in the field of robotics is conducive to greater acceptance of the presence of robots in trust works professions. Unexpectedly, it turned out that people who declared performing physical work, compared to people performing other types of tasks, have a more affirmative attitude to the participation of robots in customer service occupations and to accept the autonomy of the robot to a higher degree. The results also showed that women are more concerned about the increased presence of robots in the labour market and less accepting of the replacement of humans by robots and the greater autonomy of intelligent machines. In addition, the analysis revealed that people with more knowledge in the field of robotics declare greater acceptance of the autonomous work of robots and in terms of replacing people with robots in the work environment, they also have fewer concerns about the market situation compared to those who do not consider themselves knowledgeable in this area.

Towards Human-machine Collaboration in Autonomous Material Handling on Construction Sites

In the contemporary construction industry, the shortage of skilled labor has prompted the exploration of automation as a remedy and machine autonomy as a potential solution to the environmental conditions at the site. This research explores the balance between human oversight and the independent decision-making capabilities of robots for material delivery on construction sites. Using a scenario-based approach, autonomy in construction robotics is evaluated across four human-machine interaction cases with spatial and temporal dimensions. The expected outcomes revolve around improved safety through better human-machine communication and establishing an interoperable data model to enhance robot autonomy. This aims to automate tasks in challenging construction settings, addressing the shortage of skilled workers and expanding opportunities for individuals capable of meeting the physical demands of these environments.

Doctor Who?: Norms, Care, and Autonomy in the Attitudes of Medical Students Towards AI Pre and Post ChatGPT

This study adopts the combined TAM-TPB model to investigate attitudes and expectations of machines at a pre-career stage. We study how future doctors (medical students) expect to interact with future AI machinery, what AI usage norms will develop, and beliefs about human and machine autonomy. Semi-structured interviews were conducted. Wave one occurred six-months prior to the public release of ChatGPT; wave two occurred in the 6 months following. Three themes emerged: AI is tomorrow, wishing for the AI ouvrier, and human contrasts. Two differences were noted pre- versus post-ChatGPT: (1) participants began to view machinery instead of themselves as the controller of knowledge and (2) participants expressed increased self-confidence if collaborating with a machine. Results and implications for human-machine communication theory are discussed.

From Automation to Autonomy: Human Machine Relations in the Age of Artificial Intelligence

The shift from automation to autonomy marks a new chapter in human-machine relations, especially in the context of the expanding and diversifying applications of Artificial Intelligence (AI). As machines gain capabilities that resemble autonomous agency, the boundary between human and machine autonomy blurs, challenging traditional concepts of agency, control, and independence. This special issue examines the multidisciplinary perspectives on autonomy in the digital age, addressing the complexities of attributing autonomy to machines and AI systems. Philosophical, sociological, and technical approaches converge to explore how emerging forms of machine autonomy impacts human agency, freedom, and decision-making, with applications spanning from autonomous vehicles to digital assistants and military drones. Central to this discourse is the growing tension between viewing autonomy as a positive attribute and the concerns about diminishing human authority in the face of increasingly independent technologies. By framing autonomy as a gradual, relational, and attributional concept, the essays of this special issue aim to foster an integrated understanding of autonomy as both an individual and collective construct, reflecting the highly complex and quickly evolving nature of current societal, ethical, and technological challenges. Through contributions from diverse fields, the issue offers theoretical insights and empirical findings to better understand how AI systems reshape human-machine interactions and redefine autonomy within modern sociotechnical landscapes.

Wired to Offend: Cancel Culture Meets Generative Artificial Intelligence

Cancel culture has been limited to individuals and corporations being cancelled. However, AI is also getting cancelled, challenging our understanding of machine autonomy and cancel culture. This paper investigates how AI cancellation is discussed on X (Twitter) using a recent AI cancellation incident involving Nothing, Forever, an AI-generated parody of Seinfeld. Qualitative content analysis is performed on 734 relevant posts to extract the main categories dominating the discourse, including emotional expression, attitudes toward AI, and discussion of human versus AI qualities. The results provide insights regarding how people perceive the autonomy of AI and who is responsible for AI output.

“Their Is No Such Thing as a Machine That Acts Outside of Relations With Humans”

Starting with her early works on “Talk with Machines” (1986, republished in 2021) and her books Plans and Situated Actions: The Problem of Human-Machine Communication (1987) and Human-Machine Reconfigurations (2007a), Lucy Suchman not only opened up a new domain of scientific interest in humans and technology, but also showed how the scope of human machine relations needs to be reconceptualized. With her most recent works (2023a, 2023b), she not only widens the perspective on the contexts for machine usage, particularly by the military, but she also gives insights on how to conceptualize AI in terms of its ontological status and its agency. Discussing the relevance of the concept of autonomy for relations between humans and machines, Lucy Suchman clearly positions herself in the debate and demonstrates how we need to reconfigure and address so-called machine autonomy.

Autonomous AI: what does autonomy mean in relation to persons or machines?

ABSTRACT Artificially intelligent machines are increasingly capable of accomplishing tasks that have until now been considered exclusively human abilities. One such task is the ability to invent. In 2021, an Australian court became the first court in the world to recognise an Artificially Intelligent system as the inventor in a patent application, raising the question of whether AI systems could be considered the responsible actor of their actions, instead of the human using the AI. This paper examines the question of autonomy: how the concept of autonomy underpins the granting of legal rights to humans, and whether it is justified to grant legal rights to AI systems that are autonomous. We propose that there are two distinct and separate concepts of autonomy, Person Autonomy and Machine Autonomy, and that this distinction can guide how the law should treat AI systems.

FREE-ENERGY MODEL OF SENSE OF AGENCY FOR HUMAN-MACHINE INTERFACE DESIGN BASED ON COMPARATOR MODEL

AbstractSense of agency is the sense that one is causing an action. The increase in machine or system autonomy leads to an increase in the loss of sense of agency for the operation causing the loss of pleasure in the operation or sense of responsibility for the consequences of operations. Designing a sense of agency is necessary, especially in the context of machine autonomy. This calls for the control of the sense of agency, which requires the construction of a model to predict the sense of agency and establishing a design methodology to manipulate the factors of sense of agency. We propose the mathematical model that predicts the sense of agency in a human-machine system based on the comparator model and free-energy principle and what to design to enhance the sense of agency. Proposed model explains the effects of prediction error, prediction uncertainty, and observation uncertainty for body, machine, and environment feedback on the sense of agency. The model generally reveals the interaction effect between prediction error and prediction uncertainty and between prediction error and observation uncertainty. The model prediction can be widely applied as a design guide for enhancing sense of agency of human-machine interfaces.

Machine autonomy for rehabilitation of elderly people: A trade-off between machine intelligence and consumer trust

This is an exploratory study with the objective to understand elderly people’s expected behavior to accept and use machine autonomy controlled by ambient intelligence. It was conducted on a proposed theoretical framework, the expected trust model for machine autonomy (ETM4MA). A detailed survey was collected among elderly people living at home with their family members in a developing country, Bangladesh. Based on the results obtained from structural equation modeling (SEM) on the sample, it was observed that elderly people’s acceptance of automation systems driven by ambient intelligence as a substitute for caregiving support directly from human beings was linked to their concerns about emotional belongingness and social interaction as well as their ability to use the technology. Thus, in developing trust in this machine autonomy, their expectations around personal ability and the feeling of caring are two important issues for elderly people. To form the behavioral intention to accept this machine autonomy, trust plays a crucial role; however, this might vary depending on the differences in personality and behavioral attitude.

Weaponised Artificial Intelligence and Chinese Practices of Human–Machine Interaction

Abstract Weaponised artificial intelligence (AI) and the prospective development of lethal autonomous weapon systems (LAWS) are topics that have sparked international debate on retaining human control over the use of force. This article unpacks China’s understanding of human–machine interaction to find that it encompasses many shades of grey. Specifically, despite repeatedly supporting a legal ban on LAWS, China simultaneously promotes a narrow understanding of these systems that intends to exclude such systems from what it deems “beneficial” uses of AI. We offer understandings of this ambivalent position by investigating how it is constituted through Chinese actors’ competing practices in the areas of economy, science and technology, defence, and diplomacy. Such practices produce normative understandings of human control and machine autonomy that pull China’s position on LAWS in different directions. We contribute to the scholarship bounded by norm research and international practice theories in examining how normativity originates in and emerges from diverse domestic contexts within competing practices. We also aim to provide insights into possible approaches whereby to achieve consensus in debates on regulating LAWS, which at the time of writing have reached a stalemate.

Collaborative Work with Highly Automated Marine Navigation Systems

In navigation applications, Artificial Intelligence (AI) can improve efficiency and decision making. It is not clear, however, how designers should account for human cooperation when integrating AI systems in navigation work. In a novel empirical study, we examine the transition in the maritime domain towards higher levels of machine autonomy. Our method involved interviewing technology designers (n = 9) and navigators aboard two partially automated ferries (n = 5), as well as collecting field observations aboard one of the ferries. The results indicated a discrepancy between how designers construed human-AI collaboration compared to navigators’ own accounts in the field. Navigators reflected upon their role as one of ‘backup,’ defined by ad-hoc control takeovers from the automation. Designers positioned navigators ‘in the loop’ of a larger control system but discounted the role of in-situ skills and heuristic decision making in all but the most controlled takeover actions. The discrepancy shed light on how integration of AI systems may be better aligned to human cooperation in navigation. This included designing AI systems that render computational activities more visible and that incorporate social cues that articulate human work in its natural setting. Positioned within the field of AI alignment research, the main contribution is a formulation of human-AI interaction design insights for future navigation and control room work.

Traded Control of Human–Machine Systems for Sequential Decision-Making Based on Reinforcement Learning

Sequential decision-making (SDM) is a common type of decision-making problem with sequential and multistage characteristics. Among them, the learning and updating of policy are the main challenges in solving SDM problems. Unlike previous machine autonomy driven by artificial intelligence alone, we improve the control performance of SDM tasks by combining human intelligence and machine intelligence. Specifically, this article presents a paradigm of a human–machine traded control systems based on reinforcement learning methods to optimize the solution process of sequential decision problems. By designing the idea of autonomous boundary and credibility assessment, we enable humans and machines at the decision-making level of the systems to collaborate more effectively. And the arbitration in the human–machine traded control systems introduces the Bayesian neural network and the dropout mechanism to consider the uncertainty and security constraints. Finally, experiments involving machine traded control, human traded control were implemented. The preliminary experimental results of this article show that our traded control method improves decision-making performance and verifies the effectiveness for SDM problems.

From captain to button-presser: operators’ perspectives on navigating highly automated ferries

Teaming with Artificial Intelligence (AI) is changing the way seafarers work. We show that a new kind of seafaring is emerging, characterized by cooperation with AI systems. In this format of seafaring, navigation tasks are controlled automatically while human operators manage the automation, always “in the loop” and ready to take over control if necessary. Ideally, this arrangement sees improvements in overall system performance and safety. However, little is known today about how this format of work will unfold in real-world operations. We investigate this topic by interviewing operators (n = 5) on ferries recently outfitted with state-of-the-art automated navigation technology. We used a mixed-methods approach to analyze the case study interviews, combining quantitative text analysis with Grounded Theory qualitative analysis techniques. The results show that operators perceived a shift towards a backup role coincident with increasing agency of machine autonomy. This role shift was characterized by “button-pressing” to start the machine autonomy and subsequently intervening to stop it when things go wrong. We observed that this shift led to boredom, deskilling, stretched resources, and compromised organizational harmony – effects running counter to the intention of improved system performance and safety. Synthesizing the findings, we present (1) effects across three operational dimensions: (i) tasks, (ii) human-computer interaction, (iii) organization; and (2) a definition of collaborative (human-AI) seafaring. By identifying issues in the early implementation of highly automated ship navigation, we hope to guide designers of Maritime Autonomous Surface Ships (MASSs) away from potential pitfalls and towards development more in tune with real-world demands of collaborative work.

Spiking Neural Networks and Their Applications: A Review.

The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.

Risk Determination versus Risk Perception: A New Model of Reality for Human–Machine Autonomy

We review the progress in developing a science of interdependence applied to the determinations and perceptions of risk for autonomous human–machine systems based on a case study of the Department of Defense’s (DoD) faulty determination of risk in a drone strike in Afghanistan; the DoD’s assessment was rushed, suppressing alternative risk perceptions. We begin by contrasting the lack of success found in a case study from the commercial sphere (Facebook’s use of machine intelligence to find and categorize “hate speech”). Then, after the DoD case study, we draw a comparison with the Department of Energy’s (DOE) mismanagement of its military nuclear wastes that created health risks to the public, DOE employees, and the environment. The DOE recovered by defending its risk determinations and challenging risk perceptions in public. We apply this process to autonomous human–machine systems. The result from this review is a major discovery about the costly suppression of risk perceptions to best determine actual risks, whether for the military, business, or politics. For autonomous systems, we conclude that the determinations of actual risks need to be limited in scope as much as feasible; and that a process of free and open debate needs to be adopted that challenges the risk perceptions arising in situations facing uncertainty as the best, and possibly the only, path forward to a solution.

Bezpieczeństwo użytkowania bezzałogowych maszyn roboczych

The transition of automated machine systems towards autonomous operation is progressing fast in various industrial sectors. The following paper presents the current situation in the regulations that normalize and support the development of working machines for construction purposes. A review of the existing ISO and IEC standards alongside own work focusing on autonomous machines, as well as a review of different activities in international industry groups regarding automation and autonomy of machines have been presented. Three different approaches to the concept of safety have been identified for different working conditions. The paper also tackles issues of on-board machine communication. By analysing different systems and their basic requirements, several optimization possibilities have been identified in relation to combining different data sources. It has been shown that the combination of different systems at the machine level as well as the connection between different machines can have a major impact on the performance of both the entire system as well as on the machine itself.