This paper compares two distinct approaches to modeling robotic behavior: imperative Behavior Trees (BTs) and declarative Executable Ontologies (EO), implemented through the boldsea framework. BTs structure behavior hierarchically using control-flow, whereas EO represents the domain as a temporal, event-based semantic graph driven by dataflow rules. We demonstrate that EO achieves comparable reactivity and modularity to BTs through a fundamentally different architecture: replacing polling-based tick execution with event-driven state propagation. We propose that EO offers an alternative framework, moving from procedural programming to semantic domain modeling, to address the semantic-process gap in traditional robotic control. EO supports runtime model modification, full temporal traceability, and a unified representation of data, logic, and interface - features that are difficult or sometimes impossible to achieve with BTs, although BTs excel in established, predictable scenarios. The comparison is grounded in a practical mobile manipulation task. This comparison highlights the respective operational strengths of each approach in dynamic, evolving robotic systems.

翻译：本文比较了两种不同的机器人行为建模方法：基于命令式编程的行为树（BTs）和通过boldsea框架实现的声明式可执行本体（EO）。行为树采用控制流层次化组织行为，而可执行本体则将领域表示为基于数据流规则驱动的、时序化的事件语义图。我们证明，可执行本体通过一种根本不同的架构——用事件驱动的状态传播取代基于轮询的tick执行——实现了与行为树相当的反应性和模块化。我们提出，可执行本体提供了一种替代框架，从过程式编程转向语义领域建模，以解决传统机器人控制中的语义-过程鸿沟。可执行本体支持运行时模型修改、完整的时序可追溯性以及数据、逻辑和接口的统一表示——这些特性在行为树中难以或有时无法实现，尽管行为树在既定、可预测的场景中表现出色。该比较基于一个实际的移动操作任务进行。此对比凸显了两种方法在动态、演进的机器人系统中各自的操作优势。