目的 人类行为理解是机器智能研究中最富有挑战性的领域。其根本问题是语义获取，即从动作推理得到人的行为，需要跨越两者之间的语义鸿沟，为此提出一种人关于日常行为知识与人体动作行为、环境信息之间的建模方法，以及可扩展的开放式结构环境—行为关系模型，基于该模型提出一种新的行为理解的渐进式认知推理方法。方法 首先根据知识，建立多种特征、复合特征和行为之间的关系模型。系统根据当前的输入流，处理得到当前的特征与复合特征集，推理得到当前的可能行为集。该行为集指导处理模块，更新特征集，得到新的行为集。结果 应用本文渐进式连续推理方法，系统可以把人关于日常行为的知识与人体运动、环境变化等传感器数据处理获取到的信息动态绑定，实现知识辅助的行为理解。结论 提出的推理方法能连续处理长时间、同时发生的行为。

Objective Human behavior understanding is a challenging area in computer vision and machine intelligence. A basic problem in this area is the semantic gap between observable actions and human behavior, which should be bridged via context based reasoning. In this paper, we proposed a method to model the daily knowledge about action behavior, environment and their relationship. A novel progressive reasoning method is further built for striding over the semantic gap based on an extendable environment and action model. Method At first, models about the relation between features, complex features and behavior are built. Feature extraction modules process the continuously sensor data and forward the results to the reasoning module, in which a set of possible behaviors is selected via the feature-behavior models. The set of behaviors is further used as a condition in feature extraction for finding more features to support or to discriminate the behaviors in the set. Result A system is developed to continuously progressively reason human behavior by the proposed models. Conclusion Primary experiment shown the system can continuously understand concurrent human behaviors.