ActoDatA (Actor Data Analysis) is an actor-based software library for the development of distributed data mining applications. It provides a multi-agent architecture with a set of predefined and configurable agents performing the typical tasks of dat...
Modern cyber threats demand coordinated defensive strategies that extend beyond centralized security mechanisms. However, existing multi-agent platforms exhibit critical limitations in explicit communication and real-time coordination for cyberdefens...
The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the het...
Information architecture may shape harmful conduct in dynamic multi-agent systems, yet its relationship to objectively measured misrepresentation remains unclear. We examine seller-only communication and market-wide public history in a repeated hidde...
Vital sign monitoring outside the clinical environment based on wearable sensors ensures better support in assessing a patient’s health condition, and in case of health deterioration, automatic alerts can be sent to the care providers. In everyday li...
Retrieval-Augmented Generation (RAG) gives language models access to external text and image collections, but it does not make the decisions taken on that content reproducible. Classical Belief–Desire–Intention (BDI) agents provide explic...
Multi-agent large language model (LLM) systems built on LangGraph-style frameworks share an unencrypted state store with no field-level read access control, so one agent can read another’s private fields. The 2026 OWASP Agentic Top 10 and CVE-2...
Language learners increasingly rely on intelligent digital tools to supplement their learning experiences, yet existing chatbots often provide limited support, lacking adaptability, personalization, or domain-specific intelligence. This study introdu...
A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms...
We study big-data hybrid-data-processing lambda architecture, which consolidates low-latency real-time frameworks with high-throughput Hadoop-batch frameworks over a massively distributed setup. In particular, real-time and batch-processing engines a...
God agents, i.e., agents that accumulate excessive responsibilities, have become an increasingly common problem in modern multi-agent systems and agent frameworks. As business logic grows, interaction scenarios become more complex, and agents are exp...
Cyber wargaming serves as a core tool for simulating cyber confrontations and supporting operational decision verification. Existing multi-agent methods face issues such as the absence of cross-level mechanisms and poor adaptability to dynamic enviro...
In this paper, we propose an enhanced version of the distributed attentional actor architecture (eDA3-X) for model-free reinforcement learning. This architecture is designed to facilitate the interpretability of learned coordinated behaviors in multi...
Large Language Models have rapidly advanced, enabling composite multi-agent systems capable of sophisticated text-based collaboration. In domains that demand coordinated reasoning, teams of agents can combine complementary strengths to produce better...
Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network funct...
In this paper, we propose a multi-agent-based architecture for a Unity3D simulation of dynamic agrirobot-fleet-coordination methods. The architecture is based on a Robot Operating System (ROS) and Agrobots-SIM package that extends the existing packag...
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...
Existing multi-agent systems for automated writing assessment (AWA) suffer from three structural limitations: uniform agent weighting, fixed-depth computation, and lack of empirical auditing. To resolve these challenges, we propose CAMA, a Confidence...
In recent years, the smart home field has gained wide attention for its broad application prospects. However, families using smart home systems must usually adopt various heterogeneous smart devices, including sensors and devices, which makes it more...
This research presents the architecture, implementation, and validation of a novel Agentic Large Model (ALM) framework for in silico patient simulation, synergistically combining pharmacokinetic modeling with autonomous clinical reasoning. The multi-...
Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI...
Autonomous network management requires balancing rapid incident resolution with strict operational safety. This paper introduces a production-inspired, end-to-end multi-agent orchestration architecture for network fault diagnosis and resolution. Buil...
Automated Machine Learning (AutoML) reduces the technical requirements for applying machine learning, yet existing systems still demand technical expertise, optimize inefficiently, and rarely support conversational use. We present a conversational Au...
This paper presents PLAM (Plovdiv Air Monitoring)—a regional multi-agent platform for air quality monitoring, semantic reasoning, and forecasting. The platform uses a hybrid architecture that combines two types of intelligent agents: classic BD...
Large language model (LLM)-based artificial intelligence is increasingly used in ethically consequential human decision-making, yet fully autonomous machine ethics remains unrealistic, motivating architectures that support rather than replace human e...
Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feedi...
Modelling constitutes a disciplined transformation process through which heterogeneous, unstructured evidence is translated into structured representations that support reasoning and decision-making. The integration of generative artificial intellige...
Network Intrusion Detection Systems (NIDSs) are critical to ensuring the resilience of modern digital infrastructures. Although traditionally deployed in large-scale corporate environments, the expanding threat landscape requires the integration of r...
Smart-city infrastructure is increasingly operated through cyber–physical systems that combine embedded sensing, edge computing, communication networks, automation platforms, and digital-twin environments. The emerging industrial metaverse exte...
Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce ca...
This paper presents a systematic, data-driven literature review of research on Artificial Intelligence (AI) agents based on the top 100 Google Scholar publications related to the search terms “AI agent” and “AI agents”. The ra...
Natural landscape morphology emerges from continuous, reciprocal interactions between biological agents and their physical environment. Despite its broad application across diverse scientific fields, agent-based modeling remains underexplored in the...
This paper investigates the observability of discrete-time two-time-scale multi-agent systems with heterogeneous features under leader–follower architecture. First, a singular perturbation difference model for the discussed system is establishe...
A major challenge in cognitive science and AI has been to understand how intelligent autonomous agents might acquire and predict the behavioral and mental states of other agents in the course of complex social interactions. How does such an agent mod...
The increasing integration of inverter-based distributed energy resources (DERs) significantly reduces power system inertia, posing critical challenges to transient stability. Traditional fault ride-through strategies, relying on passive and localize...
Intelligent Tutoring Systems are increasingly used in higher education to support personalized learning and academic monitoring in large-scale digital environments. However, existing systems are predominantly based on static architecture and rigid ru...
Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has emerged due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other tradi...
As global climate change intensifies, assessing product carbon footprints serves as a foundational step for quantifying greenhouse gas emissions throughout a product’s lifecycle, forming the basis for achieving sustainability and emission reduc...
Large Language Models with Retrieval-Augmented Generation are considered to be modern, chat-native interfaces to enterprise knowledge. However, deploying such systems safely requires precautions more advanced than input filtering. Numerous LLM-relate...
Ambient Assisted Living (AAL) environments aim to enable older people to remain active and lead an autonomous and independent life for as long as possible. Among the technologies that can be incorporated into these settings, socially assistive robots...
Unmanned aerial vehicles (UAVs), commonly known as drones, are being seen as the most promising type of autonomous vehicles in the context of intelligent transportation system (ITS) technology. A key enabling factor for the current development of ITS...
This paper presents an adaptive optimal consensus tracking control scheme for canonical nonlinear multi-agent systems (MASs) with unknown dynamics, employing an actor–critic reinforcement learning (RL) framework. The scheme integrates a sliding...
We present Liquid Adaptive AI as a theoretical framework and mathematical basis for artificial intelligence systems capable of continuous structural adaptation and autonomous capability development. This work explores the conceptual boundaries of ada...
This article presents ELA Tutor, a generative AI-based Intelligent Tutoring System (ITS) built on a Multi-Agent System (MAS) architecture and implemented using the n8n platform to support personalized learning processes in higher education. The propo...
Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs). Today’s growing complexity and volatility cannot be handled by monolithic digit...
Industrial PLC programming faces persistent difficulties: lengthy development cycles, low fault tolerance, and cross-platform incompatibility among vendors. While LLMs show promise for automated code generation, their direct application is hindered b...
The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theor...
Although Cyber-Physical Systems (CPS) provides a paradigm to accommodate frequent changes in manufacturing sector, modeling and managing operations of CPS are challenging issues due to the complex interactions between entities in the system. Developm...
Traditional Web Application Firewalls (WAFs) rely on static thresholds to detect automated threats. While effective against simple scripts, these deterministic rules struggle with “Ambiguous Traffic”—sophisticated bots that mimic hu...
Empathetic dialogue systems built upon large language models overwhelmingly adopt a monolithic inference paradigm that processes emotion perception, causal reasoning, memory retrieval, and response planning within a single forward pass without archit...
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