BioPlan: Classical Planning with Crowd simulation

Abstract

Crowd simulation for evacuation situations often assumes that all agents are trying to reach a single point within an environment. Although such an assumption is not entirely wrong, human agents often exhibit more complex behaviors, even if deviations from the standard behavior are not particularly frequent. Classical planning is far from the best way to achieve the minimal path or correct behavior for agents, but adds a deeper level of reasoning about complex goal-achievement and about actions that are more complex than simply moving about. In this paper, we describe a crowd simulation experiment that uses classical AI planning to enrich the behavior of the agents in the scenario. Using this approach, we can express not only the target destination of the agents, but also (sub)goals and path preferences.