Formulating Situation Awareness for Multi-agent Systems

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm. Index Terms— Real-time computer control, Mobile robot, Multi-agent system, Path planning algorithms.

The future of air combat relies on humans controlling large teams of unmanned combat air vehicles (UCAVs) within a dynamic battle environment. Under the DARPA Mixed Initiative Control of Automata (MICA) program, we have been challenged to design a system that empowers a human operator to control teams of up to thirty UCAVs. To address these challenges we are designing an interaction system that defines and provides adequate situation and automation awareness without overloading human operators to the point where their performance degrades gracelessly. The proposed mixed initiative system is situated within a complex and highly dynamic information space that could easily overload the multi-tasking human operators. Dozens of system parameters could be updated thousands of times during a typical mission so it is neither feasible nor prudent for human operators to maintain complete situation and automation awareness. This interaction system will provide appropriately abstracted situation awareness and notification capability that includes: general mission monitoring and automation awareness; task specific information requirements; and user initiated information requests. Our approach involves defining adequate situation awareness as a function of mission phase, human operator role, and abstracted information required to oversee tasked UCAV.

Situation awareness (SA) is an important constituent in human information processing and essential in pilots' decision-making processes. Acquiring and maintaining appropriate levels of SA is critical in aviation environments as it affects all decisions and actions taking place in flights and air traffic control. This paper provides an overview of recent measurement models and approaches to establishing and enhancing SA in aviation environments. Many aspects of SA are examined including the classification of SA techniques into six categories, and different theoretical SA models from individual, to shared or team, and to distributed or system levels. Quantitative and qualitative perspectives pertaining to SA methods and issues of SA for unmanned vehicles are also addressed. Furthermore, future research directions regarding SA assessment approaches are raised to deal with shortcomings of the existing state-of-the-art methods in the literature.

Situation awareness (SA) is a construct closely linked to decision making and performance. Identifying SA requirements for specific jobs is a necessary first step in accurately assessing SA, developing training programs to enhance SA skills, and evaluating the impact of new technology on SA. This research identified the SA requirements for infantry small unit leaders. Six highly experienced infantry subject matter experts were given semi-structured, in-depth interviews. The resulting SA requirements hierarchy contained 7 primary goals and multiple subgoals. Situation awareness requirements are job and mission specific. The methods used here may be adapted to identify SA requirements in other domains characterized by small, cohesive teams operating in dynamic environments and as the basis for developing SA metrics in these domains.

The paper proposes a multi-agent decision-making system designed to manage exceptional situations in robotic production processes by recognizing human emotions in speech. The importance of human-robot collaboration was highlighted, with a focus on automatically detecting human operators’ emotions as potential warning signals for technical or safety issues. This approach contributes to improving the safety and efficiency of the production process by enabling agents to quickly adjust system settings according to the emotions expressed by operators in speech, for example, in cases of stress or frustration. The paper details the structural and functional scheme of the system. It presents the mathematical model and shows how deep learning algorithms implemented through hybrid neural networks (CNN and RNN) enable the system to accurately recognize and classify emotional patterns in an audio signal. The technology uses Python libraries such as TensorFlow and Keras to analyze acoustic characteristics such as tone, intensity, rhythm, and timbre of the voice. These parameters are used to identify emotions and initiate proactive or automatic corrective actions in robotic processes. Thus, the multi-agent system provides an excellent level of human-robot interaction, optimizing both productivity and safety in complex and dynamic industrial environments.

Awareness is a concept that has been frequently studied in the context of Computer Supported Cooperative Work. However, other fields of computer science can benefit from this concept. Recent research in the multi-agent systems field has highlighted the relevance of complex interaction models such as multi-party communication and context awareness for simulation and adaptive systems. In this article, we present a generic interaction model that enables to use these different models in a standardized way. Emerging as a first-order abstraction, the environment, in the sense of a common medium for the agents, is a suitable paradigm to support the agents' awareness. We present an operational model, called Environment as Active Support of Interaction, to take into account all the agents that can be interested in a communication. This model is then extended for the regulation of multiagent systems interactions. Priority policies are given to manage the rules governing the context (un-)awareness of the agents. We also present a new AUML connector to create protocols that take into account the agent awareness to implement proactive behaviour, and several communication scenarios are proposed to show practical applications of this model.

BackgroundAccurate situation awareness has been identified as a critical component of effective deteriorating patient response systems and an essential patient safety skill for nursing practice. However, situation awareness has been defined and theorised from multiple perspectives to explain how individuals, teams and systems maintain awareness in dynamic task environments. AimOur aim was to critically analyse the different approaches taken to the study of situation awareness in healthcare and explore the implications for nursing practice and research as it relates to clinical deterioration in ward contexts. MethodsWe undertook a meta-narrative review of the healthcare literature to capture how situation awareness has been defined, theorised and studied in healthcare. Following an initial scoping review, we conducted an extensive search of ten electronic databases and included any theoretical, empirical or critical papers with a primary focus on situation awareness in an inpatient hospital setting. Included papers were collaboratively categorised in accordance with their theoretical framing, research tradition and paradigm with a narrative review presented. ResultsA total of 120 papers were included in this review. Three overarching narratives reflecting philosophical, patient safety and solution focussed framings of situation awareness and seven meta-narratives were identified as follows: individual, team and systems perspectives of situation awareness (meta-narratives 1–3), situation awareness and patient safety (meta-narrative 4), communication tools, technologies and education to support situation awareness (meta-narratives 5–7). We identified a concentration of literature from anaesthesia and operating rooms and a body of research largely located within a cognitive engineering tradition and a positivist research paradigm. Endsley's situation awareness model was applied in over 80% of the papers reviewed. A minority of papers drew on alternative situation awareness theories including constructivist, collaborative and distributed perspectives. ConclusionsNurses have a critical role in identifying and escalating the care of deteriorating patients. There is a need to build on prior studies and reflect on the reality of nurse's work and the constraints imposed on situation awareness by the demands of busy inpatient wards. We suggest that this will require an analysis that complements but goes beyond the dominant cognitive engineering tradition to reflect the complex socio-cultural reality of ward-based teams and to explore how situation awareness emerges in increasingly complex, technologically enabled distributed healthcare systems.

A multi-agent system consists of several computational entities capable of autonomous actions, called agents, which communicate with each other, and have the ability to coordinate their actions and to cooperate. Multi-agent systems received a great interest and attention over time, as they can be seen as a key enabling technology for complex applications, where distributed and processing of data, autonomy, and high degree of interactions in dynamic environments are required at the same time. Therefore, in view of current and future developments of the digitalization of industrial production cycles promoted by Industry 4.0, multi-agent systems are foreseen to play an increasing role for industrial production management and optimization. Because of barriers represented by large presence of legacy systems, in the steel sector agent-based technology is not widely applied yet, and multi-agent systems applications are very few. On the other hand, steel manufacturing industries are complex and dynamic systems whose production processes held a strategic role in the global economy. During last decades, the steel sector has undergone relevant transformations, especially through the massive digitalization and the innovation introduced by Industry 4.0. A further evolution is foreseen in the incoming years to improve the sustainability of the production cycle by improving energy and resource efficiency. Therefore, steel industries must face several challenges on the path toward the factory of the future. In such context multi-agent systems, through their intrinsic properties, such as autonomy, social abilities, reactivity, proactivity, and mobility, can overcome existing drawbacks and barriers, by increasing flexibility, improving resources efficiency, handling production operations, reacting to unpredicted events, optimizing production processes, and supporting legacy systems. In this paper, some applications of multi-agent systems in steel sector are presented to show the advantages and opportunities of agent-based technology.

Adaptation and Evolution in Dynamic Persistent Environments

Guest Editorial: Situational awareness of integrated energy systems

Force of Habit: Developing Situation Awareness in Critical Care Transport

Workplace violence remains a persistent threat in emergency departments (EDs), yet violence risk is often assessed only at triage as a single-time-point activity. In practice, risk frequently evolves during care, shaped by situational stressors and interactional dynamics. How frontline situational awareness can be translated into structured, actionable risk management within ED workflows remains insufficiently explored. To develop and pilot a nurse-led, triage-embedded, trigger-based pathway for dynamic violence risk management in the ED. A mixed-methods study was conducted. Quantitative data (September-October 2025) captured trigger-initiated reassessments using the Dynamic Appraisal of Situational Aggression (DASA) during routine care. Qualitative focus groups explored trigger recognition, reassessment decision-making, and feasible responses. Findings were integrated to inform pathway development and refinement, followed by a one-month pilot. Among 8443 adult ED encounters, 172 (2.0%) involved at least one trigger-initiated reassessment. Reassessments most commonly occurred during waiting and transitional phases of care and were frequently associated with increases in DASA scores. Nearly all aggressive incidents occurred in encounters that underwent trigger-initiated reassessment rather than baseline triage assessment alone. Qualitative findings indicated that reassessment was prompted by cumulative behavioural, interactional, and contextual changes, reflecting nurses' situational awareness. Reassessment was valued for formalizing intuitive concern, supporting communication, and legitimizing proportionate early action, while feasible responses were concentrated at lower to moderate perceived risk levels. Violence risk in the ED is dynamic and closely linked to workflow and situational context. A trigger-based pathway that operationalizes nurses' situational awareness into structured reassessment and proportionate response may support earlier, more feasible violence risk management within routine emergency care. This study was reported in accordance with relevant reporting guidelines for mixed-methods research, including the STROBE checklist for the quantitative component and the COREQ checklist for the qualitative component.

To report on registered nurses' situation awareness as a precursor to decision-making when recording changes in patients' conditions. Progress notes are important to communicate patients' progress and detail changes in patients' conditions. However, documentation is often poorly completed. There is little work that examines nurses' decision-making during documentation. This study focused on describing situation awareness as a precursor to decision-making during documentation. This study used Endsley's (Situation Awareness Analysis and Measurement, 2000, Lawrence Erlbaum Associates, NJ) work on situation awareness to guide and conceptualise information. The study was situated in a naturalistic paradigm to provide an interpretation of nurses' decision-making. Think-aloud research methods and semi-structured interviews were employed to illuminate decision-making processes. Audio recordings and interview texts were individually examined for evidence of cues, informed by Endsley's (Situation Awareness Analysis and Measurement, 2000, Lawrence Erlbaum Associates, NJ) descriptions of situation awareness. As patients' conditions changed, nurses used complex mental models and pattern-matching of information, drawing on all 3 levels of situation awareness during documentation. Level 1 situation awareness provided context, level 2 situation awareness signified a change in condition and its significance for the patient, and level 3 situation awareness was evident when nurses thought aloud about what this information indicated. Three themes associated with changes in patients' conditions emerged: deterioration in condition, not responding to prescribed treatments as expected and issues related to professional practice that impacted on patients' conditions. Nurses used a complex mental model for decision-making, drawing on 3 levels of situation awareness. Hamm's cognitive continuum theory, when related to situation awareness, is a useful decision-making theory to provide a platform on which to draw together components of situation awareness and provide a framework on which to base decision-making regarding documentation. Understanding how RNs employ situation awareness and providing a framework for decision-making during documentation may assist effective documentation about changes in patients' conditions.

Several agent frameworks have been proposed for developing intelligent software agents and multi-agent systems that are able to perform in dynamic environments. These frameworks and architectures exploit specific reasoning tasks (such as option selection, desire filtering, plan elaboration and means-end reasoning) that support agents to react, deliberate and/or interact/cooperate with other agents. Such reasoning tasks are realized by means of specific modules that agents may trigger according to circumstances, switching their behaviour between predefined discrete behavioural modes. This paper presents the facilities provided by the non-layered BDI-architecture of ICagent for supporting performance in dynamic and unpredictable multi-agent environments through efficient balancing between behavioural modes in a continuous space. This space is circumscribed by the purely (individual) reactive, the purely (individual) deliberative and the social deliberative behavioural modes. In a greater extend than existing frameworks; ICagent relates agent’s flexible behaviour to cognition and sociability, supporting the management of plans constructed by the agent’s mental and domain actions in a coordinated manner.