We present an approach for generating contextualized behavior recommendations from a large, data-driven, complex agent-based simulation. We extend a previous method for generating a summary description by decomposing the output of a simulation into a tree of causally-relevant states, and show how behavior recommendations can be generated by ranking these causally relevant states in terms of their impact on an outcome of interest. An end-user can provide a query specifying a partial state description, which is used to retrieve the appropriate set of states from the summary description. The structure of the tree is used to generate the contexts that differentiate the behavior recommendations. We apply our method to a very complex simulation of a disaster in a major urban area and present results for multiple queries.

我们提出了一种从大型、数据驱动、复杂的基于代理的模拟中生成情境化行为建议的方法。我们通过将模拟的输出分解为因果相关状态树来扩展先前用于生成摘要描述的方法，并展示如何通过对这些因果相关状态根据其对感兴趣结果的影响进行排名来生成行为建议. 最终用户可以提供指定部分状态描述的查询，用于从摘要描述中检索适当的状态集。树的结构用于生成区分行为推荐的上下文。我们将我们的方法应用于一个主要城市地区的非常复杂的灾难模拟，并呈现多个查询的结果。