Information Systems for Supporting Fire Emergency Response

Despite recent work on information systems, many first responders in emergency situations are unable to develop sufficient understanding of the situation to enable them to make good decisions. The record of the UK Fire and Rescue Service (FRS) has been particularly poor in terms of providing the information systems support to the fire fighters decision-making during their work. There is very little work on identifying the specific information needs of different types of fire fighters. Consequently, this study has two main aims. The first is to identify the information requirements of several specific members of the FRS hierarchy that lead to better Situation Awareness. The second is to identify how such information should be presented. This study was based on extensive data collected in the FRS brigades of three counties and focused on large buildings having a high-risk of fire and four key fire fighter job roles: Incident Commander, Sector Commander, Breathing Apparatus Entry Control Officer and Breathing Apparatus Wearers. The requirements elicitation process was guided by a Cognitive Task Analysis (CTA) tool: Goal Directed Information Analysis (GDIA), which was developed specifically for this study. Initially appropriate scenarios were developed. Based on the scenarios, 44 semi-structured interviews were carried out in three different elicitation phases with both novice and experienced fire fighters. Together with field observations of fire simulation and training exercises, fire and rescue related documentation; a comprehensive set of information needs of fire fighters was identified. These were validated through two different stages via 34 brainstorming sessions with the participation of a number of subject-matter experts. To explore appropriate presentation methods of information, software mock-up was developed. This mock-up is made up of several human computer interfaces, which were evaluated via 19 walkthrough and workshop sessions, involving 22 potential end-users and 14 other related experts. As a result, many of the methods used in the mock-up were confirmed as useful and appropriate and several refinements proposed. The outcomes of this study include: 1) A set of GDI Diagrams showing goal related information needs for each of the job roles with the link to their decision-making needs, 2) A series of practical recommendations suitable for designing of human computer interfaces of fire emergency response information system, 3) Human computer interface mock-ups for an information system to enhance Situation Awareness of fire fighters and 4) A conceptual architecture for the underlying information system. In addition, this study also developed an enhanced cognitive task analysis tool capable of exploring the needs of emergency first responders. This thesis contributes to our understanding of how information systems could be designed to enhance the Situation Awareness of first responders in a fire emergency. These results will be of particular interest to practicing information systems designers and developers in the FRS in the UK and to the wider academic community

Serious games for the human behaviour analysis in emergency evacuation scenarios

This paper describes an experiment designed to elicit human behaviour when facing the urgent need of exiting an unknown building. This work is part of a larger effort to devise the methodological approach underlying the implementation of simulation of pedestrians and elicitation of their emergent dynamics, an experimental framework coined SPEED. To validate our experimental setup, a group of 16 experts on fire safety, emergency planning and building evacuation were consulted. The experts were solicited to answer a questionnaire, rating their gaming experiences and validating the questions in the form to be presented to subjects. Their comments were valuable inputs used in the development of the experiment described in this paper. A sample of 62 subjects was then used to test our approach, which consists in having the subjects answering a questionnaire and later on playing a Serious Game resorting to the Unity3D game engine. Some specific scenarios were carefully designed and presented to subjects, both in the questionnaire and in the game environment to maintain consistency of answers. Preliminary results are promising, showing that the challenge made players think about the various situations that might happen when facing an emergency. They are also implied to reason on their stream of decisions, such as which direction to take considering the environment and some adverse situations, such as smoke, fire and people running on the opposite direction of the emergency signage.info:eu-repo/semantics/publishedVersio

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

A situation risk awareness approach for process systems safety

Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In today's process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects - addressing hazards that arise from hardware failure and reducing human error through decision-making - have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from US Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach. © 2013 Elsevier Ltd

Eliciting Model Structures for Multivariate Probabilistic Risk Analysis

Notionally objective probabilistic risk models, built around ideas of cause and effect, are used to predict impacts and evaluate trade-offs. In this paper, we focus on the use of expert judgement to fill gaps left by insufficient data and understanding. Psychological and contextual phenomena such as anchoring, availability bias, confirmation bias and overconfidence are pervasive and have powerful effects on individual judgements. Research across a range of fields has found that groups have access to more diverse information and ways of thinking about problems, and routinely outperform credentialled individuals on judgement and prediction tasks. In structured group elicitation, individuals make initial independent judgements, opinions are respected, participants consider the judgements made by others, and they may have the opportunity to reconsider and revise their initial estimates. Estimates may be aggregated using behavioural, mathematical or combined approaches. In contrast, mathematical modelers have been slower to accept that the host of psychological frailties and contextual biases that afflict judgements about parameters and events may also influence model assumptions and structures. Few, if any, quantitative risk analyses embrace sources of uncertainty comprehensively. However, several recent innovations aim to anticipate behavioural and social biases in model construction and to mitigate their effects. In this paper, we outline approaches to eliciting and combining alternative ideas of cause and effect. We discuss the translation of ideas into equations and assumptions, assessing the potential for psychological and social factors to affect the construction of models. We outline the strengths and weaknesses of recent advances in structured, group-based model construction that may accommodate a variety of understandings about cause and effect

Modeling a User-Oriented Ontology on Accessible Homes for Supporting Activities of Daily Living (ADL) in Healthy Aging

Inaccessibility of the buildings is the most common obstacle which presents barriers for older adults with different motor abilities. An inclusive design process, where elderly and designers work together, is required to overcome this obstacle. To do so, this study proposes a user-oriented model (i) to define a knowledge presentation for designers; (ii) to assist them during the development of accessible homes and (iii) to accommodate exemplary home attributes for activities of daily living (ADL). The ontology for this model was first constructed by collecting user information through LEGO® Serious Play® on the four subdomains of motor abilities: (1) strength; (2) balance; (3) locomotion; and (4) endurance. The findings of this study are significant for future aging studies and mobile computing researches in terms of indicating that diverse motor ability difficulties are associated with different requirements of accessibility attributes, and structured knowledge is required to diagrammatize their association with ADL