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 fires 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 maximize 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 decentralized 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 decentralized 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 to complete tasks, each with different demands. To design a decentralized algorithm for CFST, we formulate it as a distributed constraint optimization problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralized 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 decentralized algorithms used for this problem.
File in questo prodotto:
Non ci sono file associati a questo prodotto.