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International audience ; The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. Nowadays, this type of technology is used in non-military contexts mainly for civil and environment protection: search & rescue teams, fire fighters, police officers , environmental scientific studies, etc. Although the technology for operating a single UAV is now mature, additional efforts are still necessary for using UAVs in fleets (or swarms). This position paper presents the ASIMUT project (Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques). The challenges of this project consist of handling several fleets of UAVs including communication, networking and positioning aspects. This motivates the development of novel multilevel cooperation algorithms which is an area that has not been widely explored , especially when autonomy is an additional challenge. Moreover, we will provide techniques to optimize communications for multilevel swarms. Finally, we will develop distributed and localized mobility management algorithms that will cope with conflicting objectives such as connectiv-ity maintenance and geographical area coverage.

Introduction. The emerging digital society demonstrates the problem of justifying the value, worth and price of digital products and services, which requires consideration of the specificity of value in this society. Theoretical analysis. Following the financial and art market, the boundary between "value" and "worth" is blurred in the digital society. Value, including the value of an individual, becomes variable and is conditioned by the presence of unique attributes and unique experiences that obtain market value. At the same time, under standard digital protocols and technologies, the true uniqueness of an individual experience can only be achieved through the creation of customized digital ecosystems in the form of robotic swarms that support human life activities. A set of digital devices that have sufficient resources to continuously self-renew and form a closed environment around the individual that provides a unique experience can be called a chrysalic swarm. The chrysalic swarm creates an opportunity for the individual to escape from the logic of mutual competition dictated by society. Conclusion. Studying the value problematization in the digital society of swarm intelligence opens a theoretical perspective on the problem of the crisis of social elites in the context of the fulfillment of this scenario of society development.

AbstractAI-based, autonomous weapon systems (AWS) have the potential of weapons of mass destruction and thereby massively add to the intensifying dialectic of fear between ground and space and the pervasive mass human vulnerability of being tracked and targeted from above. Nevertheless, the dangerous effects of the proliferation of AWS have not been and still are not widely acknowledged. On the one hand, the capabilities and effects of AWS are downplayed by the military and the arms industry staging these systems as precise and clean. Recently, it is also argued that they can be built on the basis of a 'responsible' or 'trustworthy' artificial intelligence (AI). On the other hand, inadequate sociotechnical imaginaries of AI as a conscious, evil super-intelligence circulated by Hollywood blockbuster films such as 'Terminator' or 'Ex Machina' dominate the public discourse. Their massive overstatement of the power of the technology and also their focus on often irrelevant imaginaries such as the 'Terminator' hinders a realistic understanding of the AI's capabilities. Against this background, arms control advocates develop new imaginaries to show the loss of 'meaningful human control' (Sharkey 2016) and its problematic consequences. In October 2023, the deployment of autonomous military in the battlefield has already been officially confirmed by an Ukrainian drone company (Hambling 2023).

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication. Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of "smart" machines not in terms of artificial intelligence but in terms of artificial communication. To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an algorithm—which is not random and is completely controlled, although not by the processes of the human mind. Esposito investigates this by examining the use of algorithms in different areas of social life. She explores the proliferation of lists (and lists of lists) online, explaining that the web works on the basis of lists to produce further lists; the use of visualization; digital profiling and algorithmic individualization, which personalize a mass medium with playlists and recommendations; and the implications of the "right to be forgotten." Finally, she considers how photographs today seem to be used to escape the present rather than to preserve a memory.

This scoping review aims to explore the various swarm technologies and how they have been used in the object classification domain with the desire to motivate the design of a generic swarm intelligence ontology based on the components of various swarm technologies. We used the PRISMA-ScR as a guide to our scoping review protocol. We conducted a search across thirteen databases and a random search as well on the internet for articles. We performed screening of all the articles by title to remove duplicates, we further on did a screening by the year of publication to ensure that all articles to be considered were published between 2012 and 2022 and we then did abstract or text synthesis. Our search query retrieved 3224 potential articles from the thirteen databases and 10 articles from a random search on the internet making a total of 3234 articles identified. Deduplication and screening were done on the identified articles and 287 articles which satisfied our inclusion criteria remained. We grouped the articles into three categories namely year of publication, swarm technology and swarm application. The year of publication showed a linear trend line which is an indication of growth in the swarm intelligence domain. Of the six categories of aims we identified we voluntarily chose to ignore articles where the aim was not specified. We noticed that 64.9% of articles were aimed at either modifying or improving. The swarm technology category indicated that 58.54% of the included articles were based on the Particle Swarm Optimization either independently or as part of a hybrid algorithm. 83.97% of the articles used classification as their swarm application. Interesting to note was the appearance of feature selection and optimization in this category. This scoping review gave an overview of how swarm technologies have been used in the object classification domain. Further research can be done by bringing and using existing algorithms in the development of generic swarm intelligence inspired ontologies.

"Argues that what makes AI socially relevant and useful is not intelligence at all but something even more human: communication. If machines are going to improve their ability to address ever more important human issues, it will not be because they have learned to think like people, but because we have learned to communicate with them"--

AbstractArtificial intelligence (AI) algorithms govern in subtle yet fundamental ways the way we live and are transforming our societies. The promise of efficient, low‐cost, or "neutral" solutions harnessing the potential of big data has led public bodies to adopt algorithmic systems in the provision of public services. As AI algorithms have permeated high‐stakes aspects of our public existence—from hiring and education decisions to the governmental use of enforcement powers (policing) or liberty‐restricting decisions (bail and sentencing)—this necessarily raises important accountability questions: What accountability challenges do AI algorithmic systems bring with them, and how can we safeguard accountability in algorithmic decision‐making? Drawing on a decidedly public administration perspective, and given the current challenges that have thus far become manifest in the field, we critically reflect on and map out in a conceptually guided manner the implications of these systems, and the limitations they pose, for public accountability.

Artificial intelligence (AI) algorithms govern in subtle yet fundamental ways the way we live and are transforming our societies. The promise of efficient, low‐cost, or "neutral" solutions harnessing the potential of big data has led public bodies to adopt algorithmic systems in the provision of public services. As AI algorithms have permeated high‐stakes aspects of our public existence—from hiring and education decisions to the governmental use of enforcement powers (policing) or liberty‐restricting decisions (bail and sentencing)—this necessarily raises important accountability questions: What accountability challenges do AI algorithmic systems bring with them, and how can we safeguard accountability in algorithmic decision‐making? Drawing on a decidedly public administration perspective, and given the current challenges that have thus far become manifest in the field, we critically reflect on and map out in a conceptually guided manner the implications of these systems, and the limitations they pose, for public accountability.

The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) are well-known nature-inspired algorithms. ACO is a metaheuristic search algorithm that takes inspiration from the behavior of real ants. In contrast, GWO is a grey wolf population-based heuristic algorithm. The important procedure in optimization is exploration and exploitation. ACO has excellent global and local search capabilities, and the exploration process is performed better than the exploitation process. In the case of regular, GWO is a greatly competitive algorithm compared to other common meta-heuristic algorithms, as it has super performance in the exploitation phase. This study proposed hybrid ACO and GWO algorithms. This hybridization is to acquire the balance between exploitation and exploration in optimization Swarm Intelligence algorithm—comprehensive examination using CEC 2014 benchmark functions. Detail investigations indicate that ACO-GWO could find solutions to unimodal, multi-modal, and hybrid problems in evaluation functions. The results show that the ACO-GWO algorithm outperforms its predecessors in several benchmark function cases. In addition, the proposed ACO-GWO algorithm could achieve an exploitation-exploration balance. Even though ACO-GWO has one disadvantage: since ACO-GWO directly combines two algorithms (ACO and GWO) with two different agents, it has superior demands on computational complexity. Doi: 10.28991/ESJ-2024-08-04-023 Full Text: PDF