With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.

随着大量数据的可用性，数据驱动的决策成为解决复杂的多智能体决策问题的替代方法。数据驱动的方法不是使用领域知识来明确构建决策模型，而是从可用数据中学习决策（可能是最佳决策）。这消除了传统知识驱动决策中的知识瓶颈，需要领域专家的大力支持。在本文中，我们在交互式动态影响图 (I-DID) 的背景下研究数据驱动的决策，这是一种不确定性下多智能体顺序决策的通用框架。我们提出了一个数据驱动的框架来解决 I-DID 模型，并专注于学习问题域中其他代理的行为。由于数据有限，挑战在于学习将嵌入 I-DID 模型的完整策略树。我们提出了两种新方法来为 I-DID 中的其他代理开发完整的策略树。第一种方法使用简单的聚类过程，而第二种方法使用复杂的统计检查。我们以理论方式分析所提出的算法，并在两个问题域上对其进行实验。