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

arXiv: https://arxiv.org/abs/2511.15274 · HTML: https://mlsanity.com/paper/2511.15274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Comparison between Behavior Trees and Finite State Machines](https://mlsanity.com/paper/2405.16137/) (2024)
- [Control Flow Versus Data Flow in Distributed Systems Integration: Revival of Flow-Based Programming for the Industrial Internet of Things](https://mlsanity.com/paper/2108.08081/) (2021)
- [Subject-Event Ontology Without Global Time: Foundations and Execution Semantics](https://mlsanity.com/paper/2510.18040/) (2025)

## Built on by (most cited first, 1 of 1)

- [Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling](https://mlsanity.com/paper/2601.07964/) (2026)

## Abstract (arXiv)

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.