Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation

State-of-the-art emergency navigation approaches are designed to evacuate civilians during a disaster based on real-time decisions using a pre-defined algorithm and live sensory data. Hence, casualties caused by the poor decisions and guidance are only apparent at the end of the evacuation process and cannot then be remedied. Previous research shows that the performance of routing algorithms for evacuation purposes are sensitive to the initial distribution of evacuees, the occupancy levels, the type of disaster and its as well its locations. Thus an algorithm that performs well in one scenario may achieve bad results in another scenario. This problem is especially serious in heuristic-based routing algorithms for evacuees where results are affected by the choice of certain parameters. Therefore, this paper proposes a simulation-based evacuee routing algorithm that optimises evacuation by making use of the high computational power of cloud servers. Rather than guiding evacuees with a predetermined routing algorithm, a robust Cognitive Packet Network based algorithm is first evaluated via a cloud-based simulator in a faster-than-real-time manner, and any "simulated casualties" are then re-routed using a variant of Dijkstra's algorithm to obtain new safe paths for them to exits. This approach can be iterated as long as corrective action is still possible.Comment: Submitted to PerNEM'15 for revie

Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making

ACoPla: a Multiagent Simulator to Study Individual Strategies in Dynamic Situations

One important issue in multi-agent systems is how to define agents’ interaction strategies in dynamic open environments. Generally, agents’ behaviors, such as being cooperative/altruistic or competitive/adversarial, are defined a priori by their creators. However, this is a weak premise when considering interaction among anonymous self-interested agents. Whenever agents meet, there is always a decision to be made: what is the best group interaction strategy? We argue that the answer depends on the amount of information required to make a decision and on the deadline proximity for accomplishing the task in hand. In certain situations, it is to the agents’ advantage to exchange information with others, while in other situations there are no incentives for them to spend time doing so. Understanding effective behaviors according to the decision- making scenario is still an open issue in multi-agent systems. In this paper, we present a multi-agent simulator (ACoPla) to understand the correlations between agents’ interaction strategy, decision-making context and successful task accomplishment rate. Additionally, we develop a case study in the domain of site evacuation to exemplify our findings. Through this study, we detect the types of conditions under which cooperation becomes the preferred strategy, as the environment changes

Decentralised Coordination in RoboCup Rescue

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads, and extinguish the ?res which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximise the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralised fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long range communication devices. Against this background, we provide a novel decentralised solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a Coalition Formation with Spatial and Temporal constraints (CFST) problem where agents form coalitions in order to complete tasks, each with different demands. In order to design a decentralised algorithm for CFST we formulate it as a Distributed Constraint Optimisation problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralised message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralised algorithms used for this problem

A cognitive architecture for emergency response

Plan recognition, cognitive workload estimation and human assistance have been extensively studied in the AI and human factors communities, resulting in many techniques being applied to domains of various levels of realism. These techniques have seldom been integrated and evaluated as complete systems. In this paper, we report on the development of an assistant agent architecture that integrates plan recognition, current and future user information needs, workload estimation and adaptive information presentation to aid an emergency response manager in making high quality decisions under time stress, while avoiding cognitive overload. We describe the main components of a full implementation of this architecture as well as a simulation developed to evaluate the system. Our evaluation consists of simulating various possible executions of the emergency response plans used in the real world and measuring the expected time taken by an unaided human user, as well as one that receives information assistance from our system. In the experimental condition of agent assistance, we also examine the effects of different error rates in the agent's estimation of user's stat or information needs

Holistic Resilience Quantification Framework of Rural Communities

Communities need to prepare for anticipated hazards, adapt to varying conditions, and resist and recover rapidly from disturbances. Protecting the built environment from natural and man-made hazards and understanding the impact of these hazards helps allocate resources efficiently. Recently, an indicator-based and time-dependent approach was developed for defining and measuring the functionality and disaster resilience continuously at the community level. This computational method uses seven dimensions that find qualitative characteristics and transforms them into quantitative measures. The proposed framework is used to study the resilience of rural communities’ subject to severe flooding events. Harlan County in the Appalachian region is chosen as a case study to evaluate the proposed resilience quantification framework subject to severe flooding. The results show the validity of the proposed approach as a decision-support mechanism to assess and enhance the resilience of rural communities