Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models

Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.Comment: 26 pages, 6 figure

A Sensor Ontology For The Domain Of Firefighting Robots

Fires create thousands of dollars in damage and thousands of deaths each year. Firefighters risk their lives everyday and are often killed in action. Firefighting robots may be able to reduce the loss of lives and damage due to fires. Robots are often used for redundant tasks that require the consistency and efficiency of a machine. They are especially optimal for tasks that require strength that exceeds that of a typical human being or for environments that are hazardous to people. Robots\u27 metallic exteriors are far more durable and easier to replace than flesh and blood, thus they are ideal for fighting fire that may be unreachable or too dangerous for humaning beings. Firefighting robots are most often shaped like tanks and are equipped with fire extinguishers, sensors, and cameras. The robots are typically operated via remote control and lack autonomy. Because of the volatile nature of fires, it is difficult for software engineers to create algorithms to make firefighting robots more autonomous. Ontologies are commonly used for sharing domain information and structuring and analyzing data. This study proposes using an ontology that is designed specifically for a firefighting robot programmed to rescue a human in danger in order to make a decision making algorithm. The methodology uses ontological tools to build the ontology. A decision-making algorithm is created using the information that is stored in the ontology. The study is evaluated on the accuracy rate of making the correct decision. It is also evaluated on if the decision-making algorithm performs significantly better than decisions chosen at random

Risk assessment of subway station fire by using a Bayesian network-based scenario evolution model

Subway station fires frequently result in massive casualties, economic losses and even social panic due to the massive passenger flow, semiconfined space and limited conditions for escape and smoke emissions. The combination of different states of fire hazard factors increases the uncertainty and complexity of the evolution path of subway station fires and causes difficulty in assessing fire risk. Traditional methods cannot describe the development process of subway station fires, and thus, cannot assess fire risk under different fire scenarios. To realise scenario-based fire risk assessment, the elements that correspond to each scenario state during fire development in subway stations are identified in this study to explore the intrinsic driving force of fire evolution. Accordingly, a fire scenario evolution model of subway stations is constructed. Then, a Bayesian network is adopted to construct a scenario evolution probability calculation model for calculating the occurrence probability of each scenario state during subway station fire development and identifying critical scenario elements that promote fire evolution. Xi’an subway station system is used as a case to illustrate the application of Bayesian network-based scenario evolution model, providing a practical management tool for fire safety managers. The method adopted in this study enables managers to predict fire risk in each scenario and understand the evolution path of subway station fire, supporting the establishment of fire response strategies based on “scenario–response” planning.</p

Multi-robot team formation control in the GUARDIANS project

Purpose The GUARDIANS multi-robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots guide and accompany the firefighters on site whilst indicating possible obstacles and the locations of danger and maintaining communications links. Design/methodology/approach In order to fulfill the aforementioned tasks the robots need to exhibit certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots. Findings The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus the application has forced us to redefine the concept of a formation. Using the graph-theoretic terminology, we can say that a formation may be stretched out as a path or be compact as a star or wheel. We have implemented the developed behaviours in simulation environments as well as on real ERA-MOBI robots commonly referred to as Erratics. We discuss advantages and shortcomings of our model, based on the simulations as well as on the implementation with a team of Erratics.</p

Mutual shaping in swarm robotics: User studies in fire and rescue, storage organization, and bridge inspection

Many real-world applications have been suggested in the swarm robotics literature. However, there is a general lack of understanding of what needs to be done for robot swarms to be useful and trusted by users in reality. This paper aims to investigate user perception of robot swarms in the workplace, and inform design principles for the deployment of future swarms in real-world applications. Three qualitative studies with a total of 37 participants were done across three sectors: fire and rescue, storage organization, and bridge inspection. Each study examined the users’ perceptions using focus groups and interviews. In this paper, we describe our findings regarding: the current processes and tools used in these professions and their main challenges; attitudes toward robot swarms assisting them; and the requirements that would encourage them to use robot swarms. We found that there was a generally positive reaction to robot swarms for information gathering and automation of simple processes. Furthermore, a human in the loop is preferred when it comes to decision making. Recommendations to increase trust and acceptance are related to transparency, accountability, safety, reliability, ease of maintenance, and ease of use. Finally, we found that mutual shaping, a methodology to create a bidirectional relationship between users and technology developers to incorporate societal choices in all stages of research and development, is a valid approach to increase knowledge and acceptance of swarm robotics. This paper contributes to the creation of such a culture of mutual shaping between researchers and users, toward increasing the chances of a successful deployment of robot swarms in the physical realm

Psychological, physical, and heat stress indicators prior to and after a 15-minute structural firefighting task

SIMPLE SUMMARY: Firefighters must endure extreme environments. Such exposure increases their body temperature, which can induce fatigue, reduce motivation, and impair their decision-making. This study set out to investigate the relationship between these factors. Nine firefighters were required to complete simulated firefighting tasks in a controlled structural fire for 15 min. Logical reasoning, speed and accuracy, memory recall, general motivation and fatigue, and physical and mental effort were recorded prior to, immediately after, and 20 min after the simulation. Results of this study identified that alongside a significant increase in firefighter tympanic membrane temperature post-task; (1) body weight loss was poorly correlated with post-task motivation and fatigue scores; (2) pre-task logical reasoning scores were predictive of change in tympanic membrane temperature. ABSTRACT: Firefighters work in strenuous conditions for prolonged periods wearing up to 20 kg of personal protective equipment. This often contributes to significant heat and cardiovascular strain. This study examined the relationships between psychological and physical measures taken prior to undertaking a 15 min firefighting task, and the occurrence of heat stress and high levels of fatigue following the task. Nine qualified firefighters completed a 15 min “live burn” scenario designed to mimic a fire started by a two-seater couch in a lounge room and completed simulated tasks throughout the duration. Logical reasoning, speed and accuracy, general motivation and fatigue, and physical and mental effort were recorded pre-scenario, and at 0- and 20-min post-scenario. General motivation and fatigue scores at 0- and 20-min post-scenario were highly correlated with each other (r(s) = 0.90; p = 0.001). The general motivation and fatigue scores, at 0- and 20-min post-scenario, were also strongly related to pre-task logic/reasoning test scores (Post 0 r(s) = −0.77, p = 0.016; Post 20 r(s) = −0.87, p = 0.002). Firefighters with lower logical reasoning and speed and accuracy scores were more susceptible to fatigue and impaired cognition when exposed to rises in core temperature and heat stress

The role of expertise in dynamic risk assessment: A reflection of the problem-solving strategies used by experienced fireground commanders

Although the concept of dynamic risk assessment has in recent times become more topical in the training manuals of most high risk domains, only a few empirical studies have reported how experts actually carry out this crucial task. The knowledge gap between research and practice in this area therefore calls for more empirical investigation within the naturalistic environment. In this paper, we present and discuss the problem solving strategies employed by sixteen experienced operational firefighters using a qualitative knowledge elicitation tool — the critical decision method. Findings revealed that dynamic risk assessment is not merely a process of weighing the risks of a proposed course of action against its benefits, but rather an experiential and pattern recognition process. The paper concludes by discussing the implications of designing training curriculum for the less experienced officers using the elicited expert knowledge

The role of expertise in dynamic risk assessment: a reflection of the problem-solving strategies used by experienced fireground commanders

Although the concept of dynamic risk assessment has in recent times become more topical in the training manuals of most high risk domains, only a few empirical studies have reported how experts actually carry out this crucial task. The knowledge gap between research and practice in this area therefore calls for more empirical investigation within the naturalistic environment. In this paper, we present and discuss the problem solving strategies employed by sixteen experienced operational firefighters using a qualitative knowledge elicitation tool — the critical decision method. Findings revealed that dynamic risk assessment is not merely a process of weighing the risks of a proposed course of action against its benefits, but rather an experiential and pattern recognition process. The paper concludes by discussing the implications of designing training curriculum for the less experienced officers using the elicited expert knowledge

Decision making study: methods and applications of evidential reasoning and judgment analysis

Decision making study has been the multi-disciplinary research involving operations researchers, management scientists, statisticians, mathematical psychologists and economists as well as others. This study aims to investigate the theory and methodology of decision making research and apply them to different contexts in real cases. The study has reviewed the literature of Multiple Criteria Decision Making (MCDM), Evidential Reasoning (ER) approach, Naturalistic Decision Making (NDM) movement, Social Judgment Theory (SJT), and Adaptive Toolbox (AT) program. On the basis of these literatures, two methods, Evidence-based Trade-Off (EBTO) and Judgment Analysis with Heuristic Modelling (JA-HM), have been proposed and developed to accomplish decision making problems under different conditions. In the EBTO method, we propose a novel framework to aid people s decision making under uncertainty and imprecise goal. Under the framework, the imprecise goal is objectively modelled through an analytical structure, and is independent of the task requirement; the task requirement is specified by the trade-off strategy among criteria of the analytical structure through an importance weighting process, and is subject to the requirement change of a particular decision making task; the evidence available, that could contribute to the evaluation of general performance of the decision alternatives, are formulated with belief structures which are capable of capturing various format of uncertainties that arise from the absence of data, incomplete information and subjective judgments. The EBTO method was further applied in a case study of Soldier system decision making. The application has demonstrated that EBTO, as a tool, is able to provide a holistic analysis regarding the requirements of Soldier missions, the physical conditions of Soldiers, and the capability of their equipment and weapon systems, which is critical in domain. By drawing the cross-disciplinary literature from NDM and AT, the JA-HM extended the traditional Judgment Analysis (JA) method, through a number of novel methodological procedures, to account for the unique features of decision making tasks under extreme time pressure and dynamic shifting situations. These novel methodological procedures include, the notion of decision point to deconstruct the dynamic shifting situations in a way that decision problem could be identified and formulated; the classification of routine and non-routine problems, and associated data alignment process to enable meaningful decision data analysis across different decision makers (DMs); the notion of composite cue to account for the DMs iterative process of information perception and comprehension in dynamic task environment; the application of computational models of heuristics to account for the time constraints and process dynamics of DMs decision making process; and the application of cross-validation process to enable the methodological principle of competitive testing of decision models. The JA-HM was further applied in a case study of fire emergency decision making. The application has been the first behavioural test of the validity of the computational models of heuristics, in predicting the DMs decision making during fire emergency response. It has also been the first behavioural test of the validity of the non-compensatory heuristics in predicting the DMs decisions on ranking task. The findings identified extend the literature of AT and NDM, and have implications for the fire emergency decision making