Engineering Smart Behavior in Evacuation Planning using Local Cooperative Path Finding Algorithms and Agent-based Simulations

摘要

This paper addresses evacuation problems from the perspective of cooperative path finding (CPF). The evacuation problem we call multi-agent evacuation (MAE) consists of an undirected graph and a set of agents. The task is to move agents from the endangered part of the graph into the safe part as quickly as possible. Although there exist centralized evacuation algorithms based on network flows that are optimal with respect to various objectives, such algorithms would hardly be applicable in practice since real agents will not be able to follow the centrally created plan. Therefore we designed a local evacuation planning algorithm called LC-MAE based on local CPF techniques. Agent-based simulations in multiple real-life scenarios show that LC-MAE produces solutions that are only worse than the optimum by a small factor. Moreover our approach led to important findings about how many agents need to behave rationally to increase the speed of evacuation.