HAC-ER: a disaster response system based on human-agent collectives

This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emergency responders by enabling humans and agents, using state-of-the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC-ER utilises crowdsourcing combined with machine learning to extract situational awareness information 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 a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations

Human-agent collectives

We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented

Obstacles and benefits in implementation of gold, silver, and bronze (GSB) model in emergency response in the UAE

The United Arab Emirates (UAE) is vulnerable to natural disasters such as earthquakes, floods, and tsunamis. Emergency response and incident command model have been implemented to help mitigate against these hazards in various part of the world. More recently, the Gold, Silver, and Bronze (GSB) model of incident command has been adopted in the UAE to integrate joint efforts, to control over emergency response and incident management at the local, regional and the national levels. The GSB model was originally established in the UK to organize efforts for quick control on incidents and has since been adopted by the UAE. In the UAE context, the GSB model provides commanders with clear responsibilities during emergencies and facilitates coordination between the commanders and partners towards achieving its desired benefits. The study deploys a case study research strategy, qualitative exploratory research design as a methodological choice to understand the current GSB obstacles and benefits in the context of the UAE’s Civil Defense General Command (CDGC). Thematic and content analysis is used to analyse the semi-structured interviews with senior commanders. Despite having applied the GSB model successfully, the qualitative findings demonstrate the CDGC has faced many obstacles related to it is efficiency in responding incidents. In contrast, the GSB model has defined the roles and responsibilities of commanders during incidents and thus organized the incident response procedures in a way that each commander achieves

Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems

Crowdsourcing systems commonly face the problem of aggregating multiple judgments provided by potentially unreliable workers. In addition, several aspects of the design of efficient crowdsourcing processes, such as defining worker's bonuses, fair prices and time limits of the tasks, involve knowledge of the likely duration of the task at hand. Bringing this together, in this work we introduce a new time--sensitive Bayesian aggregation method that simultaneously estimates a task's duration and obtains reliable aggregations of crowdsourced judgments. Our method, called BCCTime, builds on the key insight that the time taken by a worker to perform a task is an important indicator of the likely quality of the produced judgment. To capture this, BCCTime uses latent variables to represent the uncertainty about the workers' completion time, the tasks' duration and the workers' accuracy. To relate the quality of a judgment to the time a worker spends on a task, our model assumes that each task is completed within a latent time window within which all workers with a propensity to genuinely attempt the labelling task (i.e., no spammers) are expected to submit their judgments. In contrast, workers with a lower propensity to valid labeling, such as spammers, bots or lazy labelers, are assumed to perform tasks considerably faster or slower than the time required by normal workers. Specifically, we use efficient message-passing Bayesian inference to learn approximate posterior probabilities of (i) the confusion matrix of each worker, (ii) the propensity to valid labeling of each worker, (iii) the unbiased duration of each task and (iv) the true label of each task. Using two real-world public datasets for entity linking tasks, we show that BCCTime produces up to 11% more accurate classifications and up to 100% more informative estimates of a task's duration compared to state-of-the-art methods

Managing energy tariffs with agents: a field study of a future smart energy system at home

© 2015 ACM.Interactive autonomous systems are likely to be more involved in future energy systems to assist human users. Given this, we prototyped a future scenario in which householders are assisted in switching electricity tariffs by an agent-based interactive system. The system uses real-time electricity monitoring to instantiate a scenario where participants may have to make, or delegate to their agent (in a variety ways), tariff switching decisions given uncertainty about their own consumption. We carried out a field trial with 12 households for 6 weeks in order to study the notion of autonomy. The results show nuanced ways in which monitoring system performance and taking control is balanced in everyday practice. Our field study provides promising directions for future use of smart systems that help householders manage their energy

A Canonical Form for PROV Documents and its Application to Equality, Signature, and Validation

We present a canonical form for prov that is a normalized way of representing prov documents as mathematical expressions. As opposed to the normal form specified by the prov-constraints recommendation, the canonical form we present is defined for all prov documents, irrespective of their validity, and it can be serialized in a unique way. The article makes the case for a canonical form for prov and its potential uses, namely comparison of prov documents in different formats, validation, and signature of prov documents. A signature of a prov document allows the integrity and the author of provenance to be ascertained; since the signature is based on the canonical form, these checks are not tied to a particular encoding, but can be performed on any representation of prov . </jats:p