A Disaster Response System based on Human-Agent Collectives

Abstract

Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system

Crowdsourcing Approach for Disaster Response Assessment

“…Presenting several example, a study conducted by Aramaki et al in which they detect influenza epidemic by using Support Vector Machines (SVM) classifier on the collected tweets throughout a certain period of time (Ramchurn et al, 2015). Then another study by Sakaki et al in which they predict earthquake in Japan by performing semantic analysis on real-time tweets (Sakaki et al, 2010).…”

“…It is one of the key challenges that they may face, to collect relevant information for analysis. In emergencies it is vital to gain situational awareness what is happening in the ground in order to determine appropriately what is to be delivered (Ramchurn, Fischer & Simpson, 2015). …”

Provenance Network Analytics

“…Provenance has been recorded in an increasing number of applications, from legal notices, 1 climate science (Ma et al 2014), medical applications, 2 scientific workflows (Alper et al 2013;Silva et al 2011;Davidson et al 2007; Altintas et al 2006), computational reproducibility (Chirigati et al 2013), emergency response (Ramchurn et al 2016), and in the geospatial domain. 3 As a provenance description 'links' artefacts with their influences, it can be represented in a graph, called a provenance graph, whose nodes represent the artefacts/influences and whose edges their relations with one another.…”

A Templating System to Generate Provenance

“…In an increasing number of applications, provenance has become crucial in making systems accountable, by exposing how information flows in systems, and in helping users decide whether information is to be trusted. Provenance is not restricted to computer systems, it can also be used to describe how objects are transformed and people are involved in a physical system [5].Applications and use cases for provenance are well documented in the literature [7], [8], [9], [10]. They include making systems more auditable and accountable [11], reproducing results [12], deriving trust and classification [13], asserting attribution and generating acknowledgments [14], supporting predictive analytics [13], and facilitating traceability [15].…”

“…In an increasing number of applications, provenance has become crucial in making systems accountable, by exposing how information flows in systems, and in helping users decide whether information is to be trusted. Provenance is not restricted to computer systems, it can also be used to describe how objects are transformed and people are involved in a physical system [5].…”