Daily activity data recording individuals’ various activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high collection costs and potential privacy issues. Therefore, simulating human activities to produce massive high-quality data is of great importance. However, existing solutions, including rule-based methods with simplified behavior assumptions and data-driven methods directly fitting real-world data, both cannot fully qualify for matching reality. In this article, motivated by the classic psychological theory, Maslow’s need theory describing human motivation, we propose a knowledge-driven simulation framework based on generative adversarial imitation learning. Our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model. Specifically, a hierarchical model structure that disentangles different need levels and the use of neural stochastic differential equations successfully capture the piecewise-continuous characteristics of need dynamics. Extensive experiments demonstrate that our framework outperforms the state-of-the-art baselines regarding data fidelity and utility. We also present the insightful interpretability of the need modeling. Moreover, privacy preservation evaluations validate that the generated data does not leak individual privacy. The code is available at https://github.com/tsinghua-fib-lab/Activity-Simulation-SAND.

记录个人日常生活中各种活动的日常活动数据广泛应用于活动调度、活动推荐和政策制定等许多应用中。尽管其价值很高，但由于收集成本高和潜在的隐私问题，其可访问性受到限制。因此，模拟人类活动产生海量高质量数据具有重要意义。然而，现有的解决方案，包括具有简化行为假设的基于规则的方法和直接拟合现实世界数据的数据驱动方法，都无法完全符合匹配现实的条件。在本文中，受经典心理学理论——描述人类动机的马斯洛需求理论的启发，我们提出了一种基于生成对抗性模仿学习的知识驱动模拟框架。我们的核心思想是将人类需求的演变建模为驱动模拟模型中活动生成的基本机制。具体来说，分解不同需求级别的分层模型结构和神经随机微分方程的使用成功地捕获了需求动态的分段连续特征。大量的实验表明，我们的框架在数据保真度和实用性方面优于最先进的基线。我们还提出了需求建模的富有洞察力的可解释性。此外，隐私保护评估验证生成的数据不会泄露个人隐私。代码可在 https://github.com/tsinghua-fib-lab/Activity-Simulation-SAND 获取。