Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms

TL;DR

This paper compares Behavior Trees and Executable Ontologies for robot control, demonstrating EO's advantages in semantic modeling and flexibility while acknowledging BTs' predictability in static scenarios.

Contribution

It introduces EO as a viable alternative to BTs, highlighting its semantic, modular, and adaptable features in robotic control systems.

Findings

EO achieves comparable reactivity to BTs

EO allows runtime model modification and traceability

BTs excel in predictable, static scenarios

Abstract

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.