Facilitating Communication for First Responders Using Dynamic Distributed Constraint Optimization

Coordinated multi-agent reinforcement learning (MARL) provides a promising approach to scaling learning in large cooperative multi-agent systems. Distributed constraint optimization (DCOP) techniques have been used to coordinate action selection among agents during both the learning phase and the policy execution phase (if learning is off-line) to ensure good overall system performance. However, running DCOP algorithms for each action selection through the whole system results in significant communication among agents, which is not practical for most applications with limited communication bandwidth. In this paper, we develop a learning approach that generalizes previous coordinated MARL approaches that use DCOP algorithms and enables MARL to be conducted over a spectrum from independent learning (without communication) to fully coordinated learning depending on agents' communication bandwidth. Our approach defines an interaction measure that allows agents to dynamically identify their beneficial coordination set (i.e., whom to coordinate with) in different situations and to trade off its performance and communication cost. By limiting their coordination set, agents dynamically decompose the coordination network in a distributed way, resulting in dramatically reduced communication for DCOP algorithms without significantly affecting overall learning performance. Essentially, our learning approach conducts co-adaptation of agents' policy learning and coordination set identification, which outperforms approaches that sequence them.

Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [3, 4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar shortcoming? This paper answers that question by investigating the most efficient DCOP algorithms, including both DPOP and ADOPT.

Complete algorithms have been proposed to solve problems modelled as distributed constraint optimization (DCOP). However, there are only few attempts to address real world scenarios using this formalism, mainly because of the complexity associated with those algorithms. In the present work we compare three complete algorithms for DCOP, aiming at studying how they perform in complex and dynamic scenarios of increasing sizes. In order to assess their performance we measure not only standard quantities such as number of cycles to arrive to a solution, size and quantity of exchanged messages, but also computing time and quality of the solution which is related to the particular domain we use. This study can shed light in the issues of how the algorithms perform when applied to problems other than those reported in the literature (graph coloring, meeting scheduling, and distributed sensor network).

Distributed Constraint Optimization Problems (DCOP) has emerged as one of the most important multiagent coordination techniques, due to its ability to optimize the global function modeled as a set of constraints. This paper presents a study that proposes to use of distributed constraint optimization techniques to achieve energetic efficiency on railroad driving. The objectives are to present a real world problem with dynamic behavior as a DCOP, compare the main algorithms for this formalism and evaluate the quality of the solutions obtained.

This paper introduces a new agent-based algorithm for scheduling in CSCWD. Distributed Artificial Intelligence provides a lot of research areas, including CSCWD, with efficient decentralized optimization and problem solving techniques. In this paper we focus on how DCOP (Distributed Constraint Optimization Problem) can be used for scheduling in CSCWD, discussing a new algorithm. Our algorithm is almost-complete, providing the best solution most the times. However, it saves a lot of computational resources, being useful in situations where other state of the art algorithms are not feasible. The results are quite encouraging, showing that our algorithm outperforms easily two well-known DCOP algorithms in terms of runtime, number of messages, size of messages and throughput.

Background. Shared Situation Awareness (SSA) has been applied in many fields such as sport, the military and aviation with promising outcomes on team performance. The application of SSA within the hospital emergency healthcare context has not been explored yet. The aim of this scoping review is to explore and map literature related to shared situational awareness within the hospital emergency healthcare context. Methods. The Arksey and O’Malley (2005) framework was used in which three electronic databases were searched for evidence investigating SSA within a hospital emergency healthcare context. Results. A review of the literature showed a clear lack of evidence that directly investigates SSA within the context of hospital emergency care. In the emergency medical field, the term SSA is seldom used and ‘team situation awareness’ is the most frequently used term. The most common framework was the three-level framework. Two techniques were reported in the selected studies to investigate SSA (1) freeze probe technique and (2) observer-based rating technique. The freeze probe technique mandates a simulation or artificial environment, while the observer-based rating technique could be applied in an ecological as well as an artificial environment. There is no standardized technique to calculate the score of the SSA. Finally, there was a significant impact of SSA on clinical team performance as well as some related skills such as leadership, task management, mindfulness and task prioritization. Conclusions. Reviewing the literature revealed a lack of studies investigating the use of SSA within the context of hospital emergency care. There is also a lack of agreement on how a SSA score should be calculated. Further studies are required to overcome these issues.

A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses DCOPolis---a framework for comparing and deploying DCOP software in heterogeneous environments---to contribute an analysis of two state-of-the-art DCOP algorithms solving a number of different problem types. Then, we use this empirical validation to evaluate the use of both cycle-based runtime and concurrent constraint checks.

Situational awareness (SA) is vital for coordination and safety in complex domains. However, the lack of focus on shared SA (SSA) between teammates undermines effective teamwork. Gaze sharing allows partners to view each other’s gaze points on their respective displays while completing their tasks. Understanding how performance and SSA are influenced by gaze sharing is critical but understudied in dynamic contexts. In this study, 35 teams completed unmanned aerial vehicle (UAV) command-and-control (C2) tasks using different visualization techniques. SSA scores were the highest with the fixation trail, correlating positively with target detection accuracy. SSA scores were lower in the no gaze sharing condition and the lowest while using the fixation dot. These findings underscore the importance of visualization methods in shaping SSA and task performance. If gaze sharing is used, the results here show that there is a need to select appropriate visualization techniques to support team SSA and performance.

This paper proposes a system that supports effective evacuation from danger using a Distributed Constraint Optimization Algorithm. The use of the algorithm facilitates the assisted optimization of people's evacuation timing, by estimating the location of evacuees. This system enables assistance in terms of evacuation guidance to be given to relieve congestion, by calculating evacuation routes via an ad-hoc network of evacuees' mobile devices (phones, PCs, etc.), intercommunication function, and location information.

BACKGROUND Situational awareness is the accurate and timely perception of factors in the environment, comprehension of their meanings, and projection of their future states. OBJECTIVE We aimed to develop a cloud-based Situational Awareness and Learning Tool (SALT) that generates near real-time analytic content from multi-modal data, enabling public health and hospital professionals to make informed decisions during complex population health challenges. METHODS Several focus groups were conducted with representatives from local health departments, hospitals, and emergency agencies. A first round identified data needs and requirements to inform SALT’s design. SALT was developed as a secure, cloud-based platform featuring automated deployment, role-based access, and version-controlled content publishing. A second round of focus groups evaluated the SALT prototype’s utility and gathered feedback for improvements. RESULTS Participants highlighted the need for integrated data from multiple sources, tailored dashboards for specific audiences, and legal frameworks to guide timely data sharing. SALT met these requirements by providing interactive visuals, secure access levels, and a collaborative content management system. The second focus group affirmed SALT’s effectiveness in enhancing decision-making and strategic planning, suggesting enhancements like clearer data labeling, expanded data coverage, and forecasting capabilities. CONCLUSIONS SALT addresses limitations exposed by the COVID-19 pandemic in public health data systems by offering a scalable platform for data sharing, rapid analysis, and situational awareness. It fulfills user needs for integrated, timely data and customized analytic products. SALT represents a viable solution for enhancing public health data systems in preparation for future pandemics and other complex, multisector population health challenges.

Understanding team cognition requires a multifaceted research approach, aimed at understanding individual and team cognition within and across humans and agents. Measurement and prediction of mental models and situation awareness (SA) of humans, robotic agents, and teams are needed to understand the knowledge required for advanced human-robot team performance. In this paper, we present a research approach to understanding shared mental models (SMMs) and SA with respect to human-robot teams. Having shared and accurate mental models will allow for human and robotic teammates to understand each other’s roles and tasks and also provide a level of predictive behavior that allows teammates to anticipate each other’s needs. Incorporating robotic agents’ input into team SA will improve the adaptive capability of the team. Combined with SA, SMMs can provide a deeper understanding of the happenings in the mission environment.

This research aims to investigate how air traffic controllers (ATCOs) contribute to landing instability from the perspective of situation awareness and shared situation awareness. Seven (7) air traffic controllers were involved in semi-structured interviews to study how their decisions were made during the landing phase. Based on the results obtained, the themes identifed were articulated respectively under both proactive and reactive scenarios. In proactive scenarios, ATCOs will make a plan to expedite the air traffic flow to manage the workload in the future, which may decrease the time and the distance for pilots to operate. Thus, the task-related Shared-Mental Model (SMM) enables ATCOs to expedite the landing process within acceptable margins. In addition, the team-related SMM allows ATCOs to intervene in the unstabilized landing state in advance. On the other hand, as for reactive scenarios, unexpected weather and pilots' improper performance due to fuel conservation are the most prominent scenarios for ATCOs to be reactive. To tackle unexpected changes, the task-related SMM facilitates ATCOs in providing extra time and distance for pilots to operate the aircraft. To conclude, for a team in a complex system to achieve the required performance, proper SMM is compulsory to mitigate requirements conflicts between different operators. In this case, it enables ATCOs to achieve a proactive mangement in ways which can be accepted by pilots. Furthermore, proper SMM also helps ATCOs to actively provide extra instructions for pilots under contingencies.

Situation Awareness (SA) problems all require an understanding of current activities, an ability to anticipate what may happen next, and techniques to analyze the threat or impact of current activities and predictions. These processes of SA are common regardless of the domain and can be applied to the detection of cyber attacks. This paper will describe the application of a SA framework to implementing Cyber SA, describe some metrics for measuring and evaluating systems implementing Cyber SA, and discuss ongoing work in this area. We conclude with some ideas for future activities.

This workshop will focus on the problem of occupant and vehicle situational awareness with respect to automated vehicles when the driver must take over control. It will explore the future of fully automated and mixed traffic situations where vehicles are assumed to be operating at level 3 or above. In this case, all critical driving functions will be handled by the vehicle with the possibility of transitions between manual and automated driving modes at any time. This creates a driver environment where, unlike manual driving, there is no direct intrinsic motivation for the driver to be aware of the traffic situation at all times. Therefore, it is highly likely that when such a transition occurs, the driver will not be able to transition either safely or within an appropriate period of time. This workshop will address this challenge by inviting experts and practitioners from the automotive and related domains to explore concepts and solutions to increase, maintain and transfer situational awareness in semi-automated vehicles.

The risk of major occupational accidents involving tripping hazards is commonly underestimated with a large number of studies having been conducted to better understand variables that affect situation awareness: the ability to detect, perceive, and comprehend constantly evolving surroundings. An important property that affects situation awareness is the limited capacity of the attentional system. To maintain situation awareness while exposed to tripping hazards, a worker needs to obtain feedforward information about hazards, detect immediate tripping hazards, and visually scan surroundings for any potential environmental hazards. Despite the importance of situation awareness, its relationship with attention remains unknown in the construction industry. To fill this theoretical knowledge gap, this study examines differences in attentional allocation between workers with low and high situation awareness levels while exposed to tripping hazards in a real construction site. Participants were exposed to tripping hazards on a real jobsite while walking along a path in the presence of other workers. Situation awareness was measured using the situation awareness rating technique, and subjects’ eye movements were tracked as direct measures of attention via a wearable mobile eye tracker. Investigating the attentional distribution of subjects by examining fixation-count heat maps and scan paths revealed that as workers with higher situation awareness walked, they periodically looked down and scanned ahead to remain fully aware of the environment and its associated hazards. Furthermore, this study quantitatively compared the differences between the eye-tracking metrics of worker with different situation awareness levels (low versus high) using permutation simulation. The results of the statistical analysis indicate that subjects did not allocate their attention equally to all hazardous areas of interest, and these differences in attentional distribution were modulated by the workers’ level of situation awareness. This study advances theory by presenting one of the first attempts to use mobile eye-tracking technology to examine the role of cognitive processes (i.e., attention) in human error (i.e., failure to identify a hazard) and occupational accidents.