Autonomous AI agents discover services, negotiate, and settle payments. However, open agent markets lack reliable means to verify delivery, constrain delegated authority, and make identity renewal economically consequential. To address these failures...
The advancement of data-driven materials science is currently constrained by a fundamental bottleneck: the vast majority of historical experimental data remains locked within the unstructured text and rasterized figures of the legacy scientific liter...
The somatic marker hypothesis proposes that when a person faces a decision scenario, many thoughts arise and different “physical consequences” are fleetingly observable. It is generally accepted that affective dimension influences cogniti...
The maritime transportation sector is undergoing a profound shift with the emergence of autonomous vessels powered by real-time artificial intelligence (AI) agents. This article investigates the pivotal role of these agents in enhancing the safety, e...
In modern training, entertainment and education applications, behavior trees (BTs) have already become a fantastic alternative to finite state machines (FSMs) in modeling and controlling autonomous agents. However, it is expensive and inefficient to...
The concept of self-healing materials and the development of encapsulated curing agents represent a cutting-edge approach to enhancing the longevity and reducing the maintenance costs of cementitious structures. This systematic literature review aims...
Recent advancements in artificial intelligence have enabled reinforcement learning (RL) agents to exceed human-level performance in various gaming tasks. However, despite the state-of-the-art performance demonstrated by model-free RL algorithms, they...
This exploratory proof-of-concept study investigated the simultaneous deployment of autonomous, persona-driven Artificial Intelligence (AI) agents across multiple social media platforms using the ElizaOS framework. We developed three platform-specifi...
For a better understanding of the nature of complex systems modeling, computer simulations and the analysis of the resulting data are major tools which can be applied. In this paper, we study a statistical modeling problem of data coming from a simul...
Monitoring of mechanical structures is a Big Data challenge and includes Structural Health Monitoring (SHM) and Non-destructive Testing (NDT). The sensor data produced by common measuring techniques, e.g., guided wave propagation analysis, is charact...
Background: Rule-based multi-agent system (MAS) architectures for healthcare coordination rely on hardcoded decision trees that cannot generalise to novel clinical scenarios or self-correct reasoning errors. These limitations are acute in surgical co...
Agents and agent-based systems are becoming essential in the development of various fields, such as artificial intelligence, ubiquitous computing, ambient intelligence, autonomous computing, and intelligent robotics. The concept of autonomous agents,...
The maritime domain is experiencing significant transformation, driven by the integration of autonomous technologies. Autonomous ships and smart maritime systems depend on the sophisticated interplay of artificial intelligence, sensor infrastructures...
The present research explores how autonomous vehicle voice agent (AVVA) design influences autonomous vehicle passenger (AVP) intentions to adopt autonomous vehicles. An online experiment (N = 158) examined the role of gender stereotypes in response t...
Tower cranes are indispensable to building construction, but routine lifting still depends on the coordinated judgement of crane operators and signalpersons, which constrains automation and introduces human-factor variability. This study develops and...
Language plays a prominent role in the activities of human beings and other intelligent creatures. One of the most important functions of languages is communication. Inspired by this, we attempt to develop a novel language for cooperation between art...
When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of auto...
Agentic Artificial Intelligence (Agentic AI) has emerged as a new paradigm that extends conventional artificial intelligence by integrating autonomous reasoning, planning, memory, tool use, and adaptive decision-making into goal-directed systems. Alt...
Agentic, autonomous and explainable AI (XAI) is growing in smart agriculture, but its design, evaluation and integration have not been systematically mapped. Following PRISMA 2020, we searched Web of Science Core Collection, Scopus and IEEE Xplore th...
Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitati...
Centralised architectures in contemporary manufacturing systems impose structural constraints on resilience, scalability, and operational transparency that existing approaches have failed to resolve. This work reports the development and empirical va...
Agent-based systems built on large language models (LLMs) increasingly rely on complex internal reasoning processes, tool interactions, and memory mechanisms. However, the internal decision-making dynamics of such agents remain difficult to observe,...
Conventional signature-based defenses no longer protect the heterogeneous, large-scale infrastructures that the Internet of Things (IoT) now constitutes. Large language models (LLMs) and agentic artificial intelligence (AI)—systems that autonom...
Reliable multi-agent trajectory prediction is essential for autonomous vehicles to anticipate the future motion of surrounding agents in complex road environments. However, many existing methods encode motion dynamics implicitly from coordinate seque...
Prognostics and Health Management (PHM), including fault diagnosis and Remaining Useful Life (RUL) prediction, is critical for ensuring the reliability and efficiency of industrial equipment. However, traditional AI-based methods require extensive ex...
Intelligent traffic management systems have become one of the main applications of Intelligent Transportation Systems (ITS). There is a growing interest in Reinforcement Learning (RL) based control methods in ITS applications such as autonomous drivi...
Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI mode...
The development of sensor technology and deep learning has significantly improved the reliability and practicality of automatic driving technology. In an autonomous driving system, agent trajectory prediction is a complex challenge, which includes th...
Transportation and logistics systems are increasingly moving beyond predictive artificial intelligence toward systems capable of selecting, coordinating, and initiating operational decisions. The objective of this review is to systematically characte...
This paper addresses the problem of detecting multiple static and mobile targets by an autonomous mobile agent acting under uncertainty. It is assumed that the agent is able to detect targets at different distances and that the detection includes err...
While autonomous driving systems and intelligent transportation infrastructures become increasingly software-defined and network-connected, ensuring their cybersecurity has become a critical component of traffic safety. Large language models (LLMs) h...
Existing autonomous trading systems rely on collaborative or single-agent analysis, lacking structured mechanisms for adversarial deliberation across opposing market perspectives. We propose Consensus-Gated Execution (CGX), a multi-agent architecture...
The rapid advancement of connected and autonomous vehicles (CAVs) has the potential to revolutionize road transportation, promising significant improvements in safety, efficiency, and sustainability. However, traditional CAV architectures are predomi...
A key research area in autonomous driving is how to model the driver’s decision-making behavior, due to the fact it is significant for a self-driving vehicles considering their traffic safety and efficiency. However, the uncertain characteristi...
This study presents the first investigation into the problem of autonomous vehicle (AV) merging into existing platoons, proposing a multi-agent deep reinforcement learning (MA-DRL)-based cooperative control framework. The developed MA-DRL architectur...
The paper introduces a proposal of an Autonomous Navigation System for Unmanned Surface Vessels. The system architecture is presented with a special emphasis on collision avoidance and maneuver auto-negotiation. For the purpose of maneuver auto-negot...
This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strateg...
The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Determi...
Usually, the design of an Autonomous Vehicle (AV) does not take into account traffic rules and so the adoption of these rules can bring some challenges, e.g., how to come up with a Digital Highway Code which captures the proper behaviour of an AV aga...
This article focuses on the development of a stable pedestrian crash avoidance mitigation system for autonomous vehicles (AVs). Previous works have only used simple AV–pedestrian models, which do not reflect the actual interaction and risk stat...
Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual...
As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to s...
Autonomous Mobile Robots (AMRs) are increasingly important in Industry 4.0 intralogistics but creating path planning systems that adapt to dynamic and uncertain Flexible Manufacturing Systems (FMS), especially managing conflicts among multiple AMRs w...
Autonomous surface vehicles (ASVs) have been widely applied in ocean engineering due to their small size, low cost, and high mobility. However, more relevant encirclement control methods with many-to-one are simple and do not consider the system dyna...
This study aims to develop a Secured Fiscal Credits Model to address the challenges of managing Italy’s “Superbonus 110%” tax credit. Using a decentralised governance approach, our research objective is to provide a feasible system...
Information is a core concept in modern economics, yet its definition and empirical specification is elusive. One reason is the intellectual grip of the Shannon paradigm which marginalizes semantic information. However, a precise concept of economic...
Trajectory prediction is a critical component of autonomous driving, intelligent transportation systems, and human–robot interactions, particularly in complex environments like intersections, where diverse road constraints and multi-agent inter...
Autonomous vehicle control has undergone remarkable developments in recent years, especially in maneuvering at the limits of traction. These developments promise improved maneuverability and safety, but they also highlight a constant challenge: trans...
Enhancing the safety of passengers by venturing beyond the limits of a human driver is one of the main ideas behind autonomous vehicles. While drifting is mostly witnessed in motorsports as an advanced driving technique, it could provide many possibi...
Technological advancement of vehicle platforms exposes opportunities for new attack paths and vulnerabilities. Static cyber defenses can help mitigate certain attacks, but those attacks must generally be known ahead of time, and the cyber defenses mu...
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